why data analysis is important essay

Importance of Data Analysis Essay

The data analysis process will take place after all the necessary information is obtained and structured appropriately. This will be a basis for the initial stage of the mentioned process – primary data processing. It is important to analyze the results of each study as soon as possible after its completion. So far, the researcher’s memory can suggest those details that, for some reason, are not fixed but are of interest for understanding the essence of the matter. When processing the collected data, it may turn out that they are either insufficient or contradictory and therefore do not give grounds for final conclusions.

In this case, the study must be continued with the required additions. After collecting information from various sources, it is necessary to understand what exactly is needed for the initial analysis of needs in accordance with the task at hand. In most cases, it is advisable to start processing with the compilation of tables (pivot tables) of the data obtained (Simplilearn, 2021). For both manual and computer processing, the initial data is most often entered into the original pivot table. Recently, computer processing has become the predominant form of mathematical and statistical processing.

The second stage is mathematical data processing, which implies a complex preparation. In order to determine the methods of mathematical and statistical processing, first of all, it is important to assess the nature of the distribution for all the parameters used. For parameters that are normally distributed or close to normal, parametric statistics methods can be used, which in many cases are more powerful than nonparametric statistical methods (Ali & Bhaskar, 2016). The advantage of the latter is that they allow testing statistical hypotheses regardless of the shape of the distribution.

One of the most common tasks in data processing is assessing the reliability of differences between two or more series of values. There are a number of ways in mathematical statistics to solve it. The computer version of data processing has become the most widespread today. Many statistical applications have procedures for evaluating the differences between the parameters of the same sample or different samples (Tyagi, 2020). With fully computerized processing of the material, it is not difficult to use the appropriate procedure at the right time and assess the differences of interest.

The following stage may be called the formulation of conclusions. The latter are statements expressing in a concise form the meaningful results of the study. They, in a thesis form, reflect the new findings that were obtained by the author. A common mistake is that the author includes in the conclusions generally accepted in science provisions – no longer needing proof. The responses to each of the objectives listed in the introduction should be reflected in the conclusions in a certain way.

The format for presenting the results after completing the task of analyzing information is of no small importance (Tyagi, 2020). The main content needs to be translated into an easy-to-read format based on their requirements. At the same time, you should provide easy access to additional background data for those who are interested and want to understand the topic more thoroughly. These basic rules apply regardless of the format of the presentation of the information.

In order to successfully solve this problem, special methods of analysis and information processing are required. Classical information technologies make it possible to efficiently store, structure and quickly retrieve information in a user-friendly form. The main strength of SPSS Statistics is the provision of a vast range of instruments that can be utilized in the framework of statistics (Allen et al., 2014). For all the complexity of modern methods of statistical analysis, which use the latest achievements of mathematical science, the SPSS program allows one to focus on the peculiarities of their application in each specific case. This program has capabilities that significantly exceed the scope of functions provided by standard business programs such as Excel.

The SPSS program provides the user with ample opportunities for statistical processing of experimental data, for the formation of databases (SPSS data files), for their modification. SPSS may be considered a complex and flexible statistical analysis tool (Allen et al., 2014). SPSS can take data from virtually any file type and use it to create tabular reports, graphs and distribution maps, descriptive statistics, and sophisticated statistical analysis.

At this point, it seems reasonable to define the sequence of the analysis using the SPSS tools. First, it is essential to draw up a questionnaire with the questions necessary for the researcher. Next, a survey is carried out. To process the received data, you need to draw up a coding table. The coding table establishes the correspondence between individual questions of the questionnaire and the variables used in the computer data processing (Allen et al., 2014). This solves the following tasks; first, a correspondence is established between the individual questions of the questionnaire and the variables. Second, a correspondence is established between the possible values of variables and code numbers.

Next, one needs to enter the data into the data editor according to the defined variables. After that, depending on the task, it is necessary to select the desired function and schedule. Then, you should analyze the subsequent tabular output of the result. All the necessary statistical functions that will be directly used in exploring and analyzing data are located in the Analysis menu. A very important analysis can be done with multiple responses; it is called the dichotomous method. This approach is used in cases when in the questionnaire for answering a question, it is proposed to mark several answer options (Allen et al., 2014).

Comparison of the means of different samples is one of the most commonly used methods of statistical analysis. In this case, the question must always be clarified whether the existing difference in mean values can be explained by statistical fluctuations or not. This method seems appropriate as the study will involve participants from all over the state, and their responses will need to be compared.

It should be stressed that SPSS is the most widely used statistical software. The main advantage of the SPSS software package, as one of the most advanced attainments in the area of automatized data analysis, is the broad coverage of modern statistical approaches. It is successfully combined with a large number of convenient visualization tools for processing results (Allen et al., 2014). The latest version gives notable possibilities not only within the scope of psychology, sociology, and biology but also in the field of medicine, which is crucial for the aims of future research. This greatly expands the applicability of the complex, which will serve as a significant basis for ensuring the validity of the study.

Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anesthesia, 60 (9), 662–669.

Allen, P., Bennet, K., & Heritage, B. (2014). SPSS Statistics version 22: A practical guide . Cengage.

Simplilearn. (2021). What is data analysis: Methods, process and types explained . Web.

Tyagi, N. (2020). Introduction to statistical data analysis . Analytic Steps. Web.

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IvyPanda. (2022, November 30). Importance of Data Analysis. https://ivypanda.com/essays/importance-of-data-analysis/

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The Importance of Data Analysis: An Overview of Data Analytics

Organizations today need to navigate vast oceans of data to get the information they need in order to grow their business. Data analysis serves as the compass to help them reach destinations that lead to success. In an environment where businesses are in a constant race for competitive advantage, effective data analysis helps uncover critical information, drive strategic decisions, and foster innovation. Data analysis illuminates the path to make operations more efficient, expand to other markets, and innovate new services and features for customers. By transforming raw data into actionable insights, data analysis steers organizations through the uncertainties of the business world, ensuring they stay on course toward their objectives.

By uncovering patterns, trends, and anomalies within extensive datasets, businesses gain the foresight to anticipate market shifts, tailor customer experiences, and streamline operations with precision. This enables organizations to swiftly adapt to changes in the market and make timely, informed decisions to move their business forward.

Translating data into action requires an understanding of what the data is saying. Data literacy – knowing the ‘language’ of data – is critical in today’s data-centric world. It’s the very skill that empowers professionals across all sectors to apply data analytics in a way that promotes and supports effective business decisions. There’s nothing secretive or exclusive about this language; everyone, from C-suite and management to individual contributors, should learn it.

In this blog post, we’ll describe what data analysis is and its importance in the data-heavy world we live in. We’ll also get into some details about how data analysis works, the different types, and some tools and techniques you can use to help you move forward.

What is data analysis?

Data analysis is the practice of working with data to glean informed, actionable insights from the information generated across your business. This distilled definition belies the technical processes that turn raw data into something that can be useful, however. There’s a lot that happens in those processes, but that’s not the focus of this post. If you’d like more information on those processes, check out this blog post .

Analyzing data is a universal skill. We actually do it every day: at work, at home—really anywhere we make decisions based on information. For example, if you’re shopping for groceries, chances are that you evaluate the prices of the items you want to buy. You know the usual price for a favorite brand of bread. That’s data. You notice that the price has gone up, and you make a decision whether to buy it or not. That’s data analysis.

For businesses, it’s on a much bigger scale. It’s much more complex and requires additional, more comprehensive skills and tools to analyze the data that comes in.

Why is data analysis important?

The ability to sift through, process, and interpret vast amounts of data is a core function of business operations today. Accurate, well-considered, and efficiently implemented data analysis can lead to significant benefits throughout the entire organizational structure, including:

• Reducing inefficiencies and streamlining operations: Data analysis identifies inefficiencies and bottlenecks in business processes, providing opportunities to mitigate them. By analyzing resource and process data, organizations can find ways to reduce costs, boost productivity, and save time.
• Driving revenue growth: Data analysis promotes revenue growth by optimizing marketing efforts, product development, and customer retention strategies. It enables a focused approach to maximizing returns on investment (ROI).
• Mitigating risk: Forecasting potential issues and identifying risk factors before they become problematic is invaluable for all kinds of organizations. Risk analysis provides the foresight that enables businesses to implement preventative measures and avoid potential pitfalls.
• Enhancing decision-making: Insights from analyzing data empower informed, evidence-based choices. This shifts decision-making from a reliance on intuition to a strategic, data-informed approach.
• Lowering operational expenses: Data analysis helps identify unnecessary spending and underperforming assets, facilitating more efficient resource allocation. Organizations can reduce costs and reallocate budgets to improve productivity and efficiency.
• Identifying and capitalizing on new opportunities: By revealing trends and patterns, data analysis uncovers new market opportunities and avenues for expansion. This insight allows businesses to innovate and enter new markets with a solid foundation of data.
• Improving customer experience: Analyzing customer data helps organizations identify where to tailor their products, services, and interactions to meet customer needs, enhance satisfaction, and foster loyalty.

Data analysis is the foundation of strategic planning and operational efficiency, enabling organizations to navigate and swiftly adapt to market changes and evolving customer demands. It’s a critical element for gaining a competitive advantage and fostering long-lasting success in today's data-centric business environment.

4 types of data analysis

Analyzing data isn’t a single approach; it encompasses multiple approaches, each tailored to achieve specific insights. Understanding the differences can help identify the distinct elements of the type (or types) of data analysis an organization employs. While they have different names and are approached in different ways, the core objective is the same: Extract actionable insights from data. We can also identify the different types as a way of answering a question, as you’ll see below. Here are the four most common types of data analysis, each serving a special purpose:

• Descriptive analysis Descriptive analysis focuses on summarizing and understanding historical data. Descriptive analysis answers the question, "What happened?". It’s aimed at providing a clear overview of past behaviors and outcomes. Common tools for descriptive analysis include data aggregation and data mining techniques, which help identify patterns and trends.
• Diagnostic analysis Diagnostic analysis determines the cause behind a particular data point. Beyond identifying what happened, it provides the answer to “Why did it happen?”, and digs deeper into the data to understand the reasons behind past performance. Diagnostic analysis uses techniques like drill-down, data discovery, and correlations to get to the answer.
• Predictive analysis Predictive analysis answers the question, “What is likely to happen or not happen?”. This employs statistical models and other techniques to provide a forecast of likely future outcomes based on historical data. It’s invaluable for planning and risk management helping to prepare for potential future scenarios.
• Prescriptive analysis This advanced form of data analysis answers the question, “What should we do?”. It predicts future trends and makes suggestions on how to act on them by using optimization and simulation algorithms to recommend specific courses of action.

Together, these four types of data analysis play a critical role in organizational strategy, from understanding the past to evaluating the present and informing future decisions. The skillful execution of these methods helps organizations craft a holistic data strategy that anticipates, adapts to, and shapes the future with the vital information they need to navigate the complexities of today's digital-centric world with greater insight and agility.

Data analysis process: How does it work?

The journey from collecting raw data to deriving actionable insights encompasses a structured process, ensuring accuracy, relevance, and value in the findings.

Here are the six essential steps of the data analysis process:

• Identify requirements This first step is identifying the specific data required to address the business need. This phase sets the direction for the entire data analysis process, focusing efforts on gathering relevant and actionable data. CData offers connectivity solutions for hundreds of data sources, SaaS applications, and databases, simplifying the process of identifying and integrating the necessary data for analysis. 
• Collect data Once we know what data we need, the next step is to start collecting it. CData makes it easy to pull together data from all kinds of sources, whether they're structured databases or unstructured data streams. This ensures you get a complete dataset quickly and without hassle, ready for the next stages of analysis. 
• Clean the data This important step involves removing inaccuracies, duplicates, or irrelevant data to ensure the analysis is based on clean, high-quality data. CData can automate many data-cleaning tasks, reducing the time and effort required while increasing data accuracy.
• Analyze the data With clean data in hand, the actual analysis can begin. This step might involve statistical analysis, machine learning, or other data analysis methods. CData enhances this process by offering easy integration with popular analytics platforms and tools, allowing businesses to apply the most suitable analysis techniques effectively.
• Interpret the data Interpreting the results correctly is key to making informed decisions. CData's tools enhance this critical step by facilitating the integration of data with analytical models, helping teams draw precise conclusions and make informed decisions.
• Create reporting dashboards to visualize the data This last step is about turning data into a clear format that stakeholders can understand. CData connectivity solutions let you use the visualization tools you already know, making it easier to create compelling reports and dashboards that clearly communicate the findings.

Data analysis techniques

Data analysis encompasses various techniques that allow organizations to extract valuable insights from their data, enabling informed decision-making. Each technique offers unique capabilities for exploring, clustering, predicting, analyzing time-based data, and understanding sentiment.

Here are the five essential data analysis techniques that enable organizations to turn data into actions:

• Exploratory data analysis (EDA) involves analyzing datasets to summarize their main characteristics, often through visual methods like histograms, scatter plots, and box plots. It helps in understanding the structure of the data, identifying patterns, detecting outliers, and laying the groundwork for further analysis.
• Clustering and segmentation techniques group similar data points together based on certain features or attributes. This helps in identifying meaningful patterns within the data and segmenting the data into distinct groups or clusters. Businesses use clustering to understand customer segments, market segments, or product categories, aiding in targeted marketing and product customization.
• Machine learning algorithms enable computers to learn from data and make predictions or decisions without being explicitly programmed. Businesses utilize various machine learning algorithms such as linear regression, decision trees, random forests, and neural networks to analyze data, predict outcomes, classify data points, and identify trends. These algorithms are applied in various domains, including sales forecasting, customer churn prediction, sentiment analysis, and fraud detection.
• Time series analysis is analyzing data collected over time to understand patterns, trends, and seasonal variations. It is commonly used in forecasting future values based on historical data, identifying underlying patterns, and making informed decisions. Businesses employ time series analysis in financial forecasting, demand forecasting, inventory management, and trend analysis to predict future outcomes and plan accordingly.
• Sentiment analysis involves analyzing textual data, such as customer reviews, social media posts, and survey responses, to determine the sentiment or opinion expressed within the text. Businesses use sentiment analysis to gauge customer satisfaction, brand sentiment, and public opinion regarding products or services. By understanding sentiment trends, businesses can make strategic decisions, improve customer experiences, and manage their reputation effectively.

Data analysis tools

From powerful analytics platforms to robust database management systems, a diverse array of tools exists to meet the needs of organizations across various industries.

Here is a list of some of the most popular data analysis tools available:

• Alteryx (requirements, cleaning, analysis)
• Apache Kafka (collection, requirements)
• Google Analytics (collection, analysis)
• Google Looker (interpretation, visualization)
• Informatica (requirements, cleaning)
• Microsoft Power BI (analysis, interpretation, visualization)
• PostgreSQL (analysis)
• QlikView (analysis)
• Tableau (analysis, interpretation, visualization)
• Talend (collection, requirements)

For modern organizations, the right tools are critical to streamline processes, uncover insights, and drive strategic decisions. From data collection to visualization, these tools empower businesses to stay agile and competitive in an ever-evolving digital world.

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Navigating the waters of data analysis requires clear direction and reliable tools. CData's comprehensive connectivity solutions act as a compass through each stage of the data analysis process. From collecting and cleaning data to interpreting and visualizing insights, CData empowers businesses to confidently chart their course, make informed decisions, and stay competitive in today's modern business climate.

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Why is Data Analytics Important?

• August 17, 2021

Recently, we explained what data analytics is , and here we’ll review why it’s so important to modern business practices.

As part of this discussion, we’ll cover what data analytics entails, what data analysts actually do , and why you should consider getting into the field.

Virtually everyone has heard of data analytics, business analytics, big data analytics, or the many other terms used to refer to this discipline, but few people seem to understand why it’s so important to modern organizations.

Simply put, data analytics is critical to the success of modern businesses because data analysts themselves are the individuals responsible for reviewing key performance metrics, interpreting that data, and using it to determine an effective strategy for driving organizational performance.

After you’ve learned everything you need to know about what makes data analytics such a critical discipline, fill out our information request form to receive additional details about CSU Global’s 100% online Master’s Degree Program in Data Analytics , or if you’re ready to get started, submit your application today.

What is Data Analytics?

Data analytics is the process of storing, organizing, and analyzing data for business purposes.

This process is used to inform key decision-makers and allows them to make important strategic decisions based on data, rather than hunches.

At modern organizations, data analysts are responsible for helping guide core business units and practices, including extremely important processes like:

• Information Technology
• Human Resources
• Business Development

As you may imagine, this process and the critical role played by data analytics and professional data analysts have made them incredibly important members of virtually every organization operating in any sector of the economy.

What Do Data Analysts Actually Do?

Data analysts are responsible for handling a variety of important responsibilities, including tasks like:

• Data warehousing
• Data mining and visualization
• Business analytics
• Predictive analytics
• Enterprise performance management

Accordingly, data analysts play an important role in helping senior leadership teams make difficult decisions about driving organizational effectiveness, efficiency, and profitability.

Some of the key skills data analysts need to perform these complicated tasks include:

• Analyzing large and complex sets of data.
• Applying policies and procedures to protect the privacy and security of the data that they analyze.
• Articulating analytical conclusions and strategy suggestions in writing, verbally, and visually via multimedia presentations.
• Employing data analytics solutions for business intelligence and forecasting purposes.
• Practicing ethical standards when handling data and analytics.
• Utilizing predictive analytics to address core business challenges.

Clearly then, data analyst jobs require excellent attention to detail, expert-level statistics and mathematical skills, and a deep understanding of statistics and predictive analytics.

Should I Pursue a Career in Data Analytics?

Knowing that data analytics is a difficult discipline and a challenging role, why would you want to pursue a career in the field?

First, the field is growing rapidly, and demand for skilled data analysts is projected to grow rapidly over the next decade.

In fact, the BLS predicts that employment for roles related to data analytics will rise considerably quicker than the average rate of growth for all occupations between 2021 and 2031. 

These roles include:

• Mathematicians and Statisticians - 31% projected growth.
• Computer and Information Research Scientists - 21% projected growth.
• Database Administrators - 9% projected growth.

Because the industry is growing so quickly, and demand for related roles is predicted to continue rising at such a fast pace, it’s a great time to consider entering the industry.

Next, anyone interested in playing a critical strategic role in a modern business or other organization should think about developing analytics skills.

As we mentioned earlier, data analysts are the professionals responsible for helping senior leadership make important strategic decisions about core business units, making those people trained in data analytics an essential asset for modern organizations.

Accordingly, top positions in this field command excellent salaries, with some of the top jobs for Master’s program graduates earning a considerable average annual income:

• Computer and Information Research Scientist / 2021 Median Pay: $131,490
• Top Executive / 2021 Median Pay: $98,980
• Database Administrator / 2021 Median Pay: $101,000
• Statistician / 2021 Median Pay: $96,280

Even roles for Bachelor’s program graduates pay quite well, as some of the best jobs for MIS and Business Analytics alumni include:

• Computer and Information Systems Managers / 2021 Median Pay: $159,010
• Computer Programmers / 2021 Median Pay: $93,000
• Computer Systems Analysts / 2021 Median Pay: $99,270
• Operations Research Analysts / 2021 Median Pay: $82,360

If you’re looking to play a central role as a strategic business advisor, and you want to earn an excellent income, then you’d be hard-pressed to find a better field than data analytics.

How To Launch a Career in Data Analytics

While you might be able to get an entry-level role in the industry without first completing a Bachelor’s or Master’s program, it may be far easier to break into the field if you’ve finished a degree program before you apply for related roles.

Why? Because the best way to develop the knowledge, skills, and abilities you’ll need to be successful as a professional data analyst is to study data analytics in an academic setting, like CSU Global’s online Bachelor’s Degree in MIS and Business Analytics , or our online Master’s Degree in Data Analytics . 

These programs will provide you with the experience and knowledge you need to launch a career in the analytics industry, while also giving you the academic credentials needed to catch the eye of hiring managers looking to fill related positions.

Earning a degree in analytics will increase the chances that you’re capable of bringing value to an organization from day one, and choosing to study at CSU Global will ensure that your degree is respected by the hiring managers you’ll be looking to impress when it comes time to pursue your first industry role.

Should I Get a Bachelor’s or a Master’s Degree in Analytics?

We suggest enrolling in the degree program that best meets your professional needs.

To determine your needs, review your current education credentials, your knowledge, and experience in data analytics, and weigh those against your long-term career goals.

If you’re new to analytics and just looking to get your foot in the door, or if you lack an undergraduate degree, then you may want to consider our Bachelor’s Degree in Management Information Systems and Business Analytics.

However, if you already have some experience in the industry, if you’ve already earned a bachelor’s degree, or if you want to pursue leadership or managerial-level roles, then you’ll want to consider our Master’s Degree in Data Analytics instead.

If you’re having trouble choosing between a bachelor’s or master’s degree, consider contacting an Enrollment Counselor by calling 1-800-462-7845, or by emailing enroll [at] csuglobal.edu .

The good news is that whichever program you choose, getting your degree will help develop your skills and abilities, while choosing to get your degree from CSU Global will ensure that you’ll graduate prepared to launch a lifelong career in the industry.

Can I Get My Degree in Data Analytics Online?

Yes, you can get a regionally accredited online Bachelor’s or Master’s Degree in Analytics from CSU Global. 

Our accelerated online degree programs were designed entirely for online students, and they offer far more flexibility and freedom than traditional in-person programs.

We make it easy to juggle your educational pursuits with existing work and family responsibilities, as our programs offer:

• No requirements to attend classes at set times or locations.
• Monthly class starts.
• Accelerated, eight-week courses.

If you’re looking for a degree program with the flexibility to fit into your already busy life, then there may be no better option than choosing to study with us.

Why Should You Choose to Study Analytics at CSU Global?

Our Bachelor’s Degree program in Business Analytics and our Master’s Degree program in Data Analytics are both regionally accredited by the Higher Learning Commission .

These programs were designed to provide you with the foundational knowledge and skills needed to succeed as a professional analyst, featuring a curriculum designed specifically for the workplace.

Both programs are also taught exclusively by educators who have relevant and recent industry experience, so you can rest assured that you’ll be learning modern solutions to real-world business problems and that you’ll graduate prepared to provide value to any organization.

Our programs also rank exceptionally well, with each of them earning top 5 rankings in their respective fields, including:

• A #1 ranking for Best Online Bachelor’s in Management Information Systems Programs from Best Colleges for our Bachelor’s in MIS and Business Analytics.
• A #3 ranking for Best Online Master’s Degree in Data Analytics by Best Masters Programs for our Master’s in Data Analytics.

Furthermore, CSU Global itself has also recently received several distinguished rankings, including:

• A #1 ranking for Best Online Colleges & Schools in Colorado from Best Accredited Colleges .
• A #1 ranking for Best Online Colleges in Colorado from Best Colleges .
• A #10 ranking for Best Online Colleges for ROI from OnlineU .

Finally, CSU Global offers competitive tuition rates  and a Tuition Guarantee to ensure your rate won’t increase as you're getting a degree in the desired field, thus saving your money and time.

To get additional details about our fully accredited, 100% online analytics degree programs, please give us a call at 800-462-7845, or fill out our Information Request Form .

Ready to get started today? Apply now !

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Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Iqbal h. sarker.

1 Swinburne University of Technology, Melbourne, VIC 3122 Australia

2 Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chittagong, 4349 Bangladesh

The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on “Data Science” including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Introduction

We are living in the age of “data science and advanced analytics”, where almost everything in our daily lives is digitally recorded as data [ 17 ]. Thus the current electronic world is a wealth of various kinds of data, such as business data, financial data, healthcare data, multimedia data, internet of things (IoT) data, cybersecurity data, social media data, etc [ 112 ]. The data can be structured, semi-structured, or unstructured, which increases day by day [ 105 ]. Data science is typically a “concept to unify statistics, data analysis, and their related methods” to understand and analyze the actual phenomena with data. According to Cao et al. [ 17 ] “data science is the science of data” or “data science is the study of data”, where a data product is a data deliverable, or data-enabled or guided, which can be a discovery, prediction, service, suggestion, insight into decision-making, thought, model, paradigm, tool, or system. The popularity of “Data science” is increasing day-by-day, which is shown in Fig. ​ Fig.1 1 according to Google Trends data over the last 5 years [ 36 ]. In addition to data science, we have also shown the popularity trends of the relevant areas such as “Data analytics”, “Data mining”, “Big data”, “Machine learning” in the figure. According to Fig. ​ Fig.1, 1 , the popularity indication values for these data-driven domains, particularly “Data science”, and “Machine learning” are increasing day-by-day. This statistical information and the applicability of the data-driven smart decision-making in various real-world application areas, motivate us to study briefly on “Data science” and machine-learning-based “Advanced analytics” in this paper.

The worldwide popularity score of data science comparing with relevant  areas in a range of 0 (min) to 100 (max) over time where x -axis represents the timestamp information and y -axis represents the corresponding score

Usually, data science is the field of applying advanced analytics methods and scientific concepts to derive useful business information from data. The emphasis of advanced analytics is more on anticipating the use of data to detect patterns to determine what is likely to occur in the future. Basic analytics offer a description of data in general, while advanced analytics is a step forward in offering a deeper understanding of data and helping to analyze granular data, which we are interested in. In the field of data science, several types of analytics are popular, such as "Descriptive analytics" which answers the question of what happened; "Diagnostic analytics" which answers the question of why did it happen; "Predictive analytics" which predicts what will happen in the future; and "Prescriptive analytics" which prescribes what action should be taken, discussed briefly in “ Advanced analytics methods and smart computing ”. Such advanced analytics and decision-making based on machine learning techniques [ 105 ], a major part of artificial intelligence (AI) [ 102 ] can also play a significant role in the Fourth Industrial Revolution (Industry 4.0) due to its learning capability for smart computing as well as automation [ 121 ].

Although the area of “data science” is huge, we mainly focus on deriving useful insights through advanced analytics, where the results are used to make smart decisions in various real-world application areas. For this, various advanced analytics methods such as machine learning modeling, natural language processing, sentiment analysis, neural network, or deep learning analysis can provide deeper knowledge about data, and thus can be used to develop data-driven intelligent applications. More specifically, regression analysis, classification, clustering analysis, association rules, time-series analysis, sentiment analysis, behavioral patterns, anomaly detection, factor analysis, log analysis, and deep learning which is originated from the artificial neural network, are taken into account in our study. These machine learning-based advanced analytics methods are discussed briefly in “ Advanced analytics methods and smart computing ”. Thus, it’s important to understand the principles of various advanced analytics methods mentioned above and their applicability to apply in various real-world application areas. For instance, in our earlier paper Sarker et al. [ 114 ], we have discussed how data science and machine learning modeling can play a significant role in the domain of cybersecurity for making smart decisions and to provide data-driven intelligent security services. In this paper, we broadly take into account the data science application areas and real-world problems in ten potential domains including the area of business data science, health data science, IoT data science, behavioral data science, urban data science, and so on, discussed briefly in “ Real-world application domains ”.

Based on the importance of machine learning modeling to extract the useful insights from the data mentioned above and data-driven smart decision-making, in this paper, we present a comprehensive view on “Data Science” including various types of advanced analytics methods that can be applied to enhance the intelligence and the capabilities of an application. The key contribution of this study is thus understanding data science modeling, explaining different analytic methods for solution perspective and their applicability in various real-world data-driven applications areas mentioned earlier. Overall, the purpose of this paper is, therefore, to provide a basic guide or reference for those academia and industry people who want to study, research, and develop automated and intelligent applications or systems based on smart computing and decision making within the area of data science.

The main contributions of this paper are summarized as follows:

• To define the scope of our study towards data-driven smart computing and decision-making in our real-world life. We also make a brief discussion on the concept of data science modeling from business problems to data product and automation, to understand its applicability and provide intelligent services in real-world scenarios.
• To provide a comprehensive view on data science including advanced analytics methods that can be applied to enhance the intelligence and the capabilities of an application.
• To discuss the applicability and significance of machine learning-based analytics methods in various real-world application areas. We also summarize ten potential real-world application areas, from business to personalized applications in our daily life, where advanced analytics with machine learning modeling can be used to achieve the expected outcome.
• To highlight and summarize the challenges and potential research directions within the scope of our study.

The rest of the paper is organized as follows. The next section provides the background and related work and defines the scope of our study. The following section presents the concepts of data science modeling for building a data-driven application. After that, briefly discuss and explain different advanced analytics methods and smart computing. Various real-world application areas are discussed and summarized in the next section. We then highlight and summarize several research issues and potential future directions, and finally, the last section concludes this paper.

Background and Related Work

In this section, we first discuss various data terms and works related to data science and highlight the scope of our study.

Data Terms and Definitions

There is a range of key terms in the field, such as data analysis, data mining, data analytics, big data, data science, advanced analytics, machine learning, and deep learning, which are highly related and easily confusing. In the following, we define these terms and differentiate them with the term “Data Science” according to our goal.

The term “Data analysis” refers to the processing of data by conventional (e.g., classic statistical, empirical, or logical) theories, technologies, and tools for extracting useful information and for practical purposes [ 17 ]. The term “Data analytics”, on the other hand, refers to the theories, technologies, instruments, and processes that allow for an in-depth understanding and exploration of actionable data insight [ 17 ]. Statistical and mathematical analysis of the data is the major concern in this process. “Data mining” is another popular term over the last decade, which has a similar meaning with several other terms such as knowledge mining from data, knowledge extraction, knowledge discovery from data (KDD), data/pattern analysis, data archaeology, and data dredging. According to Han et al. [ 38 ], it should have been more appropriately named “knowledge mining from data”. Overall, data mining is defined as the process of discovering interesting patterns and knowledge from large amounts of data [ 38 ]. Data sources may include databases, data centers, the Internet or Web, other repositories of data, or data dynamically streamed through the system. “Big data” is another popular term nowadays, which may change the statistical and data analysis approaches as it has the unique features of “massive, high dimensional, heterogeneous, complex, unstructured, incomplete, noisy, and erroneous” [ 74 ]. Big data can be generated by mobile devices, social networks, the Internet of Things, multimedia, and many other new applications [ 129 ]. Several unique features including volume, velocity, variety, veracity, value (5Vs), and complexity are used to understand and describe big data [ 69 ].

In terms of analytics, basic analytics provides a summary of data whereas the term “Advanced Analytics” takes a step forward in offering a deeper understanding of data and helps to analyze granular data. Advanced analytics is characterized or defined as autonomous or semi-autonomous data or content analysis using advanced techniques and methods to discover deeper insights, predict or generate recommendations, typically beyond traditional business intelligence or analytics. “Machine learning”, a branch of artificial intelligence (AI), is one of the major techniques used in advanced analytics which can automate analytical model building [ 112 ]. This is focused on the premise that systems can learn from data, recognize trends, and make decisions, with minimal human involvement [ 38 , 115 ]. “Deep Learning” is a subfield of machine learning that discusses algorithms inspired by the human brain’s structure and the function called artificial neural networks [ 38 , 139 ].

Unlike the above data-related terms, “Data science” is an umbrella term that encompasses advanced data analytics, data mining, machine, and deep learning modeling, and several other related disciplines like statistics, to extract insights or useful knowledge from the datasets and transform them into actionable business strategies. In [ 17 ], Cao et al. defined data science from the disciplinary perspective as “data science is a new interdisciplinary field that synthesizes and builds on statistics, informatics, computing, communication, management, and sociology to study data and its environments (including domains and other contextual aspects, such as organizational and social aspects) to transform data to insights and decisions by following a data-to-knowledge-to-wisdom thinking and methodology”. In “ Understanding data science modeling ”, we briefly discuss the data science modeling from a practical perspective starting from business problems to data products that can assist the data scientists to think and work in a particular real-world problem domain within the area of data science and analytics.

Related Work

In the area, several papers have been reviewed by the researchers based on data science and its significance. For example, the authors in [ 19 ] identify the evolving field of data science and its importance in the broader knowledge environment and some issues that differentiate data science and informatics issues from conventional approaches in information sciences. Donoho et al. [ 27 ] present 50 years of data science including recent commentary on data science in mass media, and on how/whether data science varies from statistics. The authors formally conceptualize the theory-guided data science (TGDS) model in [ 53 ] and present a taxonomy of research themes in TGDS. Cao et al. include a detailed survey and tutorial on the fundamental aspects of data science in [ 17 ], which considers the transition from data analysis to data science, the principles of data science, as well as the discipline and competence of data education.

Besides, the authors include a data science analysis in [ 20 ], which aims to provide a realistic overview of the use of statistical features and related data science methods in bioimage informatics. The authors in [ 61 ] study the key streams of data science algorithm use at central banks and show how their popularity has risen over time. This research contributes to the creation of a research vector on the role of data science in central banking. In [ 62 ], the authors provide an overview and tutorial on the data-driven design of intelligent wireless networks. The authors in [ 87 ] provide a thorough understanding of computational optimal transport with application to data science. In [ 97 ], the authors present data science as theoretical contributions in information systems via text analytics.

Unlike the above recent studies, in this paper, we concentrate on the knowledge of data science including advanced analytics methods, machine learning modeling, real-world application domains, and potential research directions within the scope of our study. The advanced analytics methods based on machine learning techniques discussed in this paper can be applied to enhance the capabilities of an application in terms of data-driven intelligent decision making and automation in the final data product or systems.

Understanding Data Science Modeling

In this section, we briefly discuss how data science can play a significant role in the real-world business process. For this, we first categorize various types of data and then discuss the major steps of data science modeling starting from business problems to data product and automation.

Types of Real-World Data

Typically, to build a data-driven real-world system in a particular domain, the availability of data is the key [ 17 , 112 , 114 ]. The data can be in different types such as (i) Structured—that has a well-defined data structure and follows a standard order, examples are names, dates, addresses, credit card numbers, stock information, geolocation, etc.; (ii) Unstructured—has no pre-defined format or organization, examples are sensor data, emails, blog entries, wikis, and word processing documents, PDF files, audio files, videos, images, presentations, web pages, etc.; (iii) Semi-structured—has elements of both the structured and unstructured data containing certain organizational properties, examples are HTML, XML, JSON documents, NoSQL databases, etc.; and (iv) Metadata—that represents data about the data, examples are author, file type, file size, creation date and time, last modification date and time, etc. [ 38 , 105 ].

In the area of data science, researchers use various widely-used datasets for different purposes. These are, for example, cybersecurity datasets such as NSL-KDD [ 127 ], UNSW-NB15 [ 79 ], Bot-IoT [ 59 ], ISCX’12 [ 15 ], CIC-DDoS2019 [ 22 ], etc., smartphone datasets such as phone call logs [ 88 , 110 ], mobile application usages logs [ 124 , 149 ], SMS Log [ 28 ], mobile phone notification logs [ 77 ] etc., IoT data [ 56 , 11 , 64 ], health data such as heart disease [ 99 ], diabetes mellitus [ 86 , 147 ], COVID-19 [ 41 , 78 ], etc., agriculture and e-commerce data [ 128 , 150 ], and many more in various application domains. In “ Real-world application domains ”, we discuss ten potential real-world application domains of data science and analytics by taking into account data-driven smart computing and decision making, which can help the data scientists and application developers to explore more in various real-world issues.

Overall, the data used in data-driven applications can be any of the types mentioned above, and they can differ from one application to another in the real world. Data science modeling, which is briefly discussed below, can be used to analyze such data in a specific problem domain and derive insights or useful information from the data to build a data-driven model or data product.

Steps of Data Science Modeling

Data science is typically an umbrella term that encompasses advanced data analytics, data mining, machine, and deep learning modeling, and several other related disciplines like statistics, to extract insights or useful knowledge from the datasets and transform them into actionable business strategies, mentioned earlier in “ Background and related work ”. In this section, we briefly discuss how data science can play a significant role in the real-world business process. Figure ​ Figure2 2 shows an example of data science modeling starting from real-world data to data-driven product and automation. In the following, we briefly discuss each module of the data science process.

• Understanding business problems: This involves getting a clear understanding of the problem that is needed to solve, how it impacts the relevant organization or individuals, the ultimate goals for addressing it, and the relevant project plan. Thus to understand and identify the business problems, the data scientists formulate relevant questions while working with the end-users and other stakeholders. For instance, how much/many, which category/group, is the behavior unrealistic/abnormal, which option should be taken, what action, etc. could be relevant questions depending on the nature of the problems. This helps to get a better idea of what business needs and what we should be extracted from data. Such business knowledge can enable organizations to enhance their decision-making process, is known as “Business Intelligence” [ 65 ]. Identifying the relevant data sources that can help to answer the formulated questions and what kinds of actions should be taken from the trends that the data shows, is another important task associated with this stage. Once the business problem has been clearly stated, the data scientist can define the analytic approach to solve the problem.
• Understanding data: As we know that data science is largely driven by the availability of data [ 114 ]. Thus a sound understanding of the data is needed towards a data-driven model or system. The reason is that real-world data sets are often noisy, missing values, have inconsistencies, or other data issues, which are needed to handle effectively [ 101 ]. To gain actionable insights, the appropriate data or the quality of the data must be sourced and cleansed, which is fundamental to any data science engagement. For this, data assessment that evaluates what data is available and how it aligns to the business problem could be the first step in data understanding. Several aspects such as data type/format, the quantity of data whether it is sufficient or not to extract the useful knowledge, data relevance, authorized access to data, feature or attribute importance, combining multiple data sources, important metrics to report the data, etc. are needed to take into account to clearly understand the data for a particular business problem. Overall, the data understanding module involves figuring out what data would be best needed and the best ways to acquire it.
• Data pre-processing and exploration: Exploratory data analysis is defined in data science as an approach to analyzing datasets to summarize their key characteristics, often with visual methods [ 135 ]. This examines a broad data collection to discover initial trends, attributes, points of interest, etc. in an unstructured manner to construct meaningful summaries of the data. Thus data exploration is typically used to figure out the gist of data and to develop a first step assessment of its quality, quantity, and characteristics. A statistical model can be used or not, but primarily it offers tools for creating hypotheses by generally visualizing and interpreting the data through graphical representation such as a chart, plot, histogram, etc [ 72 , 91 ]. Before the data is ready for modeling, it’s necessary to use data summarization and visualization to audit the quality of the data and provide the information needed to process it. To ensure the quality of the data, the data  pre-processing technique, which is typically the process of cleaning and transforming raw data [ 107 ] before processing and analysis is important. It also involves reformatting information, making data corrections, and merging data sets to enrich data. Thus, several aspects such as expected data, data cleaning, formatting or transforming data, dealing with missing values, handling data imbalance and bias issues, data distribution, search for outliers or anomalies in data and dealing with them, ensuring data quality, etc. could be the key considerations in this step.
• Machine learning modeling and evaluation: Once the data is prepared for building the model, data scientists design a model, algorithm, or set of models, to address the business problem. Model building is dependent on what type of analytics, e.g., predictive analytics, is needed to solve the particular problem, which is discussed briefly in “ Advanced analytics methods and smart computing ”. To best fits the data according to the type of analytics, different types of data-driven or machine learning models that have been summarized in our earlier paper Sarker et al. [ 105 ], can be built to achieve the goal. Data scientists typically separate training and test subsets of the given dataset usually dividing in the ratio of 80:20 or data considering the most popular k -folds data splitting method [ 38 ]. This is to observe whether the model performs well or not on the data, to maximize the model performance. Various model validation and assessment metrics, such as error rate, accuracy, true positive, false positive, true negative, false negative, precision, recall, f-score, ROC (receiver operating characteristic curve) analysis, applicability analysis, etc. [ 38 , 115 ] are used to measure the model performance, which can guide the data scientists to choose or design the learning method or model. Besides, machine learning experts or data scientists can take into account several advanced analytics such as feature engineering, feature selection or extraction methods, algorithm tuning, ensemble methods, modifying existing algorithms, or designing new algorithms, etc. to improve the ultimate data-driven model to solve a particular business problem through smart decision making.
• Data product and automation: A data product is typically the output of any data science activity [ 17 ]. A data product, in general terms, is a data deliverable, or data-enabled or guide, which can be a discovery, prediction, service, suggestion, insight into decision-making, thought, model, paradigm, tool, application, or system that process data and generate results. Businesses can use the results of such data analysis to obtain useful information like churn (a measure of how many customers stop using a product) prediction and customer segmentation, and use these results to make smarter business decisions and automation. Thus to make better decisions in various business problems, various machine learning pipelines and data products can be developed. To highlight this, we summarize several potential real-world data science application areas in “ Real-world application domains ”, where various data products can play a significant role in relevant business problems to make them smart and automate.

Overall, we can conclude that data science modeling can be used to help drive changes and improvements in business practices. The interesting part of the data science process indicates having a deeper understanding of the business problem to solve. Without that, it would be much harder to gather the right data and extract the most useful information from the data for making decisions to solve the problem. In terms of role, “Data Scientists” typically interpret and manage data to uncover the answers to major questions that help organizations to make objective decisions and solve complex problems. In a summary, a data scientist proactively gathers and analyzes information from multiple sources to better understand how the business performs, and  designs machine learning or data-driven tools/methods, or algorithms, focused on advanced analytics, which can make today’s computing process smarter and intelligent, discussed briefly in the following section.

An example of data science modeling from real-world data to data-driven system and decision making

Advanced Analytics Methods and Smart Computing

As mentioned earlier in “ Background and related work ”, basic analytics provides a summary of data whereas advanced analytics takes a step forward in offering a deeper understanding of data and helps in granular data analysis. For instance, the predictive capabilities of advanced analytics can be used to forecast trends, events, and behaviors. Thus, “advanced analytics” can be defined as the autonomous or semi-autonomous analysis of data or content using advanced techniques and methods to discover deeper insights, make predictions, or produce recommendations, where machine learning-based analytical modeling is considered as the key technologies in the area. In the following section, we first summarize various types of analytics and outcome that are needed to solve the associated business problems, and then we briefly discuss machine learning-based analytical modeling.

Types of Analytics and Outcome

In the real-world business process, several key questions such as “What happened?”, “Why did it happen?”, “What will happen in the future?”, “What action should be taken?” are common and important. Based on these questions, in this paper, we categorize and highlight the analytics into four types such as descriptive, diagnostic, predictive, and prescriptive, which are discussed below.

• Descriptive analytics: It is the interpretation of historical data to better understand the changes that have occurred in a business. Thus descriptive analytics answers the question, “what happened in the past?” by summarizing past data such as statistics on sales and operations or marketing strategies, use of social media, and engagement with Twitter, Linkedin or Facebook, etc. For instance, using descriptive analytics through analyzing trends, patterns, and anomalies, etc., customers’ historical shopping data can be used to predict the probability of a customer purchasing a product. Thus, descriptive analytics can play a significant role to provide an accurate picture of what has occurred in a business and how it relates to previous times utilizing a broad range of relevant business data. As a result, managers and decision-makers can pinpoint areas of strength and weakness in their business, and eventually can take more effective management strategies and business decisions.
• Diagnostic analytics: It is a form of advanced analytics that examines data or content to answer the question, “why did it happen?” The goal of diagnostic analytics is to help to find the root cause of the problem. For example, the human resource management department of a business organization may use these diagnostic analytics to find the best applicant for a position, select them, and compare them to other similar positions to see how well they perform. In a healthcare example, it might help to figure out whether the patients’ symptoms such as high fever, dry cough, headache, fatigue, etc. are all caused by the same infectious agent. Overall, diagnostic analytics enables one to extract value from the data by posing the right questions and conducting in-depth investigations into the answers. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations.
• Predictive analytics: Predictive analytics is an important analytical technique used by many organizations for various purposes such as to assess business risks, anticipate potential market patterns, and decide when maintenance is needed, to enhance their business. It is a form of advanced analytics that examines data or content to answer the question, “what will happen in the future?” Thus, the primary goal of predictive analytics is to identify and typically answer this question with a high degree of probability. Data scientists can use historical data as a source to extract insights for building predictive models using various regression analyses and machine learning techniques, which can be used in various application domains for a better outcome. Companies, for example, can use predictive analytics to minimize costs by better anticipating future demand and changing output and inventory, banks and other financial institutions to reduce fraud and risks by predicting suspicious activity, medical specialists to make effective decisions through predicting patients who are at risk of diseases, retailers to increase sales and customer satisfaction through understanding and predicting customer preferences, manufacturers to optimize production capacity through predicting maintenance requirements, and many more. Thus predictive analytics can be considered as the core analytical method within the area of data science.
• Prescriptive analytics: Prescriptive analytics focuses on recommending the best way forward with actionable information to maximize overall returns and profitability, which typically answer the question, “what action should be taken?” In business analytics, prescriptive analytics is considered the final step. For its models, prescriptive analytics collects data from several descriptive and predictive sources and applies it to the decision-making process. Thus, we can say that it is related to both descriptive analytics and predictive analytics, but it emphasizes actionable insights instead of data monitoring. In other words, it can be considered as the opposite of descriptive analytics, which examines decisions and outcomes after the fact. By integrating big data, machine learning, and business rules, prescriptive analytics helps organizations to make more informed decisions to produce results that drive the most successful business decisions.

In summary, to clarify what happened and why it happened, both descriptive analytics and diagnostic analytics look at the past. Historical data is used by predictive analytics and prescriptive analytics to forecast what will happen in the future and what steps should be taken to impact those effects. In Table ​ Table1, 1 , we have summarized these analytics methods with examples. Forward-thinking organizations in the real world can jointly use these analytical methods to make smart decisions that help drive changes in business processes and improvements. In the following, we discuss how machine learning techniques can play a big role in these analytical methods through their learning capabilities from the data.

Various types of analytical methods with examples

Machine Learning Based Analytical Modeling

In this section, we briefly discuss various advanced analytics methods based on machine learning modeling, which can make the computing process smart through intelligent decision-making in a business process. Figure ​ Figure3 3 shows a general structure of a machine learning-based predictive modeling considering both the training and testing phase. In the following, we discuss a wide range of methods such as regression and classification analysis, association rule analysis, time-series analysis, behavioral analysis, log analysis, and so on within the scope of our study.

A general structure of a machine learning based predictive model considering both the training and testing phase

Regression Analysis

In data science, one of the most common statistical approaches used for predictive modeling and data mining tasks is regression techniques [ 38 ]. Regression analysis is a form of supervised machine learning that examines the relationship between a dependent variable (target) and independent variables (predictor) to predict continuous-valued output [ 105 , 117 ]. The following equations Eqs. 1 , 2 , and 3 [ 85 , 105 ] represent the simple, multiple or multivariate, and polynomial regressions respectively, where x represents independent variable and y is the predicted/target output mentioned above:

Regression analysis is typically conducted for one of two purposes: to predict the value of the dependent variable in the case of individuals for whom some knowledge relating to the explanatory variables is available, or to estimate the effect of some explanatory variable on the dependent variable, i.e., finding the relationship of causal influence between the variables. Linear regression cannot be used to fit non-linear data and may cause an underfitting problem. In that case, polynomial regression performs better, however, increases the model complexity. The regularization techniques such as Ridge, Lasso, Elastic-Net, etc. [ 85 , 105 ] can be used to optimize the linear regression model. Besides, support vector regression, decision tree regression, random forest regression techniques [ 85 , 105 ] can be used for building effective regression models depending on the problem type, e.g., non-linear tasks. Financial forecasting or prediction, cost estimation, trend analysis, marketing, time-series estimation, drug response modeling, etc. are some examples where the regression models can be used to solve real-world problems in the domain of data science and analytics.

Classification Analysis

Classification is one of the most widely used and best-known data science processes. This is a form of supervised machine learning approach that also refers to a predictive modeling problem in which a class label is predicted for a given example [ 38 ]. Spam identification, such as ‘spam’ and ‘not spam’ in email service providers, can be an example of a classification problem. There are several forms of classification analysis available in the area such as binary classification—which refers to the prediction of one of two classes; multi-class classification—which involves the prediction of one of more than two classes; multi-label classification—a generalization of multiclass classification in which the problem’s classes are organized hierarchically [ 105 ].

Several popular classification techniques, such as k-nearest neighbors [ 5 ], support vector machines [ 55 ], navies Bayes [ 49 ], adaptive boosting [ 32 ], extreme gradient boosting [ 85 ], logistic regression [ 66 ], decision trees ID3 [ 92 ], C4.5 [ 93 ], and random forests [ 13 ] exist to solve classification problems. The tree-based classification technique, e.g., random forest considering multiple decision trees, performs better than others to solve real-world problems in many cases as due to its capability of producing logic rules [ 103 , 115 ]. Figure ​ Figure4 4 shows an example of a random forest structure considering multiple decision trees. In addition, BehavDT recently proposed by Sarker et al. [ 109 ], and IntrudTree [ 106 ] can be used for building effective classification or prediction models in the relevant tasks within the domain of data science and analytics.

An example of a random forest structure considering multiple decision trees

Cluster Analysis

Clustering is a form of unsupervised machine learning technique and is well-known in many data science application areas for statistical data analysis [ 38 ]. Usually, clustering techniques search for the structures inside a dataset and, if the classification is not previously identified, classify homogeneous groups of cases. This means that data points are identical to each other within a cluster, and different from data points in another cluster. Overall, the purpose of cluster analysis is to sort various data points into groups (or clusters) that are homogeneous internally and heterogeneous externally [ 105 ]. To gain insight into how data is distributed in a given dataset or as a preprocessing phase for other algorithms, clustering is often used. Data clustering, for example, assists with customer shopping behavior, sales campaigns, and retention of consumers for retail businesses, anomaly detection, etc.

Many clustering algorithms with the ability to group data have been proposed in machine learning and data science literature [ 98 , 138 , 141 ]. In our earlier paper Sarker et al. [ 105 ], we have summarized this based on several perspectives, such as partitioning methods, density-based methods, hierarchical-based methods, model-based methods, etc. In the literature, the popular K-means [ 75 ], K-Mediods [ 84 ], CLARA [ 54 ] etc. are known as partitioning methods; DBSCAN [ 30 ], OPTICS [ 8 ] etc. are known as density-based methods; single linkage [ 122 ], complete linkage [ 123 ], etc. are known as hierarchical methods. In addition, grid-based clustering methods, such as STING [ 134 ], CLIQUE [ 2 ], etc.; model-based clustering such as neural network learning [ 141 ], GMM [ 94 ], SOM [ 18 , 104 ], etc.; constrained-based methods such as COP K-means [ 131 ], CMWK-Means [ 25 ], etc. are used in the area. Recently, Sarker et al. [ 111 ] proposed a hierarchical clustering method, BOTS [ 111 ] based on bottom-up agglomerative technique for capturing user’s similar behavioral characteristics over time. The key benefit of agglomerative hierarchical clustering is that the tree-structure hierarchy created by agglomerative clustering is more informative than an unstructured set of flat clusters, which can assist in better decision-making in relevant application areas in data science.

Association Rule Analysis

Association rule learning is known as a rule-based machine learning system, an unsupervised learning method is typically used to establish a relationship among variables. This is a descriptive technique often used to analyze large datasets for discovering interesting relationships or patterns. The association learning technique’s main strength is its comprehensiveness, as it produces all associations that meet user-specified constraints including minimum support and confidence value [ 138 ].

Association rules allow a data scientist to identify trends, associations, and co-occurrences between data sets inside large data collections. In a supermarket, for example, associations infer knowledge about the buying behavior of consumers for different items, which helps to change the marketing and sales plan. In healthcare, to better diagnose patients, physicians may use association guidelines. Doctors can assess the conditional likelihood of a given illness by comparing symptom associations in the data from previous cases using association rules and machine learning-based data analysis. Similarly, association rules are useful for consumer behavior analysis and prediction, customer market analysis, bioinformatics, weblog mining, recommendation systems, etc.

Several types of association rules have been proposed in the area, such as frequent pattern based [ 4 , 47 , 73 ], logic-based [ 31 ], tree-based [ 39 ], fuzzy-rules [ 126 ], belief rule [ 148 ] etc. The rule learning techniques such as AIS [ 3 ], Apriori [ 4 ], Apriori-TID and Apriori-Hybrid [ 4 ], FP-Tree [ 39 ], Eclat [ 144 ], RARM [ 24 ] exist to solve the relevant business problems. Apriori [ 4 ] is the most commonly used algorithm for discovering association rules from a given dataset among the association rule learning techniques [ 145 ]. The recent association rule-learning technique ABC-RuleMiner proposed in our earlier paper by Sarker et al. [ 113 ] could give significant results in terms of generating non-redundant rules that can be used for smart decision making according to human preferences, within the area of data science applications.

Time-Series Analysis and Forecasting

A time series is typically a series of data points indexed in time order particularly, by date, or timestamp [ 111 ]. Depending on the frequency, the time-series can be different types such as annually, e.g., annual budget, quarterly, e.g., expenditure, monthly, e.g., air traffic, weekly, e.g., sales quantity, daily, e.g., weather, hourly, e.g., stock price, minute-wise, e.g., inbound calls in a call center, and even second-wise, e.g., web traffic, and so on in relevant domains.

A mathematical method dealing with such time-series data, or the procedure of fitting a time series to a proper model is termed time-series analysis. Many different time series forecasting algorithms and analysis methods can be applied to extract the relevant information. For instance, to do time-series forecasting for future patterns, the autoregressive (AR) model [ 130 ] learns the behavioral trends or patterns of past data. Moving average (MA) [ 40 ] is another simple and common form of smoothing used in time series analysis and forecasting that uses past forecasted errors in a regression-like model to elaborate an averaged trend across the data. The autoregressive moving average (ARMA) [ 12 , 120 ] combines these two approaches, where autoregressive extracts the momentum and pattern of the trend and moving average capture the noise effects. The most popular and frequently used time-series model is the autoregressive integrated moving average (ARIMA) model [ 12 , 120 ]. ARIMA model, a generalization of an ARMA model, is more flexible than other statistical models such as exponential smoothing or simple linear regression. In terms of data, the ARMA model can only be used for stationary time-series data, while the ARIMA model includes the case of non-stationarity as well. Similarly, seasonal autoregressive integrated moving average (SARIMA), autoregressive fractionally integrated moving average (ARFIMA), autoregressive moving average model with exogenous inputs model (ARMAX model) are also used in time-series models [ 120 ].

In addition to the stochastic methods for time-series modeling and forecasting, machine and deep learning-based approach can be used for effective time-series analysis and forecasting. For instance, in our earlier paper, Sarker et al. [ 111 ] present a bottom-up clustering-based time-series analysis to capture the mobile usage behavioral patterns of the users. Figure ​ Figure5 5 shows an example of producing aggregate time segments Seg_i from initial time slices TS_i based on similar behavioral characteristics that are used in our bottom-up clustering approach, where D represents the dominant behavior BH_i of the users, mentioned above [ 111 ]. The authors in [ 118 ], used a long short-term memory (LSTM) model, a kind of recurrent neural network (RNN) deep learning model, in forecasting time-series that outperform traditional approaches such as the ARIMA model. Time-series analysis is commonly used these days in various fields such as financial, manufacturing, business, social media, event data (e.g., clickstreams and system events), IoT and smartphone data, and generally in any applied science and engineering temporal measurement domain. Thus, it covers a wide range of application areas in data science.

An example of producing aggregate time segments from initial time slices based on similar behavioral characteristics

Opinion Mining and Sentiment Analysis

Sentiment analysis or opinion mining is the computational study of the opinions, thoughts, emotions, assessments, and attitudes of people towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes [ 71 ]. There are three kinds of sentiments: positive, negative, and neutral, along with more extreme feelings such as angry, happy and sad, or interested or not interested, etc. More refined sentiments to evaluate the feelings of individuals in various situations can also be found according to the problem domain.

Although the task of opinion mining and sentiment analysis is very challenging from a technical point of view, it’s very useful in real-world practice. For instance, a business always aims to obtain an opinion from the public or customers about its products and services to refine the business policy as well as a better business decision. It can thus benefit a business to understand the social opinion of their brand, product, or service. Besides, potential customers want to know what consumers believe they have when they use a service or purchase a product. Document-level, sentence level, aspect level, and concept level, are the possible levels of opinion mining in the area [ 45 ].

Several popular techniques such as lexicon-based including dictionary-based and corpus-based methods, machine learning including supervised and unsupervised learning, deep learning, and hybrid methods are used in sentiment analysis-related tasks [ 70 ]. To systematically define, extract, measure, and analyze affective states and subjective knowledge, it incorporates the use of statistics, natural language processing (NLP), machine learning as well as deep learning methods. Sentiment analysis is widely used in many applications, such as reviews and survey data, web and social media, and healthcare content, ranging from marketing and customer support to clinical practice. Thus sentiment analysis has a big influence in many data science applications, where public sentiment is involved in various real-world issues.

Behavioral Data and Cohort Analysis

Behavioral analytics is a recent trend that typically reveals new insights into e-commerce sites, online gaming, mobile and smartphone applications, IoT user behavior, and many more [ 112 ]. The behavioral analysis aims to understand how and why the consumers or users behave, allowing accurate predictions of how they are likely to behave in the future. For instance, it allows advertisers to make the best offers with the right client segments at the right time. Behavioral analytics, including traffic data such as navigation paths, clicks, social media interactions, purchase decisions, and marketing responsiveness, use the large quantities of raw user event information gathered during sessions in which people use apps, games, or websites. In our earlier papers Sarker et al. [ 101 , 111 , 113 ] we have discussed how to extract users phone usage behavioral patterns utilizing real-life phone log data for various purposes.

In the real-world scenario, behavioral analytics is often used in e-commerce, social media, call centers, billing systems, IoT systems, political campaigns, and other applications, to find opportunities for optimization to achieve particular outcomes. Cohort analysis is a branch of behavioral analytics that involves studying groups of people over time to see how their behavior changes. For instance, it takes data from a given data set (e.g., an e-commerce website, web application, or online game) and separates it into related groups for analysis. Various machine learning techniques such as behavioral data clustering [ 111 ], behavioral decision tree classification [ 109 ], behavioral association rules [ 113 ], etc. can be used in the area according to the goal. Besides, the concept of RecencyMiner, proposed in our earlier paper Sarker et al. [ 108 ] that takes into account recent behavioral patterns could be effective while analyzing behavioral data as it may not be static in the real-world changes over time.

Anomaly Detection or Outlier Analysis

Anomaly detection, also known as Outlier analysis is a data mining step that detects data points, events, and/or findings that deviate from the regularities or normal behavior of a dataset. Anomalies are usually referred to as outliers, abnormalities, novelties, noise, inconsistency, irregularities, and exceptions [ 63 , 114 ]. Techniques of anomaly detection may discover new situations or cases as deviant based on historical data through analyzing the data patterns. For instance, identifying fraud or irregular transactions in finance is an example of anomaly detection.

It is often used in preprocessing tasks for the deletion of anomalous or inconsistency in the real-world data collected from various data sources including user logs, devices, networks, and servers. For anomaly detection, several machine learning techniques can be used, such as k-nearest neighbors, isolation forests, cluster analysis, etc [ 105 ]. The exclusion of anomalous data from the dataset also results in a statistically significant improvement in accuracy during supervised learning [ 101 ]. However, extracting appropriate features, identifying normal behaviors, managing imbalanced data distribution, addressing variations in abnormal behavior or irregularities, the sparse occurrence of abnormal events, environmental variations, etc. could be challenging in the process of anomaly detection. Detection of anomalies can be applicable in a variety of domains such as cybersecurity analytics, intrusion detections, fraud detection, fault detection, health analytics, identifying irregularities, detecting ecosystem disturbances, and many more. This anomaly detection can be considered a significant task for building effective systems with higher accuracy within the area of data science.

Factor Analysis

Factor analysis is a collection of techniques for describing the relationships or correlations between variables in terms of more fundamental entities known as factors [ 23 ]. It’s usually used to organize variables into a small number of clusters based on their common variance, where mathematical or statistical procedures are used. The goals of factor analysis are to determine the number of fundamental influences underlying a set of variables, calculate the degree to which each variable is associated with the factors, and learn more about the existence of the factors by examining which factors contribute to output on which variables. The broad purpose of factor analysis is to summarize data so that relationships and patterns can be easily interpreted and understood [ 143 ].

Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are the two most popular factor analysis techniques. EFA seeks to discover complex trends by analyzing the dataset and testing predictions, while CFA tries to validate hypotheses and uses path analysis diagrams to represent variables and factors [ 143 ]. Factor analysis is one of the algorithms for unsupervised machine learning that is used for minimizing dimensionality. The most common methods for factor analytics are principal components analysis (PCA), principal axis factoring (PAF), and maximum likelihood (ML) [ 48 ]. Methods of correlation analysis such as Pearson correlation, canonical correlation, etc. may also be useful in the field as they can quantify the statistical relationship between two continuous variables, or association. Factor analysis is commonly used in finance, marketing, advertising, product management, psychology, and operations research, and thus can be considered as another significant analytical method within the area of data science.

Log Analysis

Logs are commonly used in system management as logs are often the only data available that record detailed system runtime activities or behaviors in production [ 44 ]. Log analysis is thus can be considered as the method of analyzing, interpreting, and capable of understanding computer-generated records or messages, also known as logs. This can be device log, server log, system log, network log, event log, audit trail, audit record, etc. The process of creating such records is called data logging.

Logs are generated by a wide variety of programmable technologies, including networking devices, operating systems, software, and more. Phone call logs [ 88 , 110 ], SMS Logs [ 28 ], mobile apps usages logs [ 124 , 149 ], notification logs [ 77 ], game Logs [ 82 ], context logs [ 16 , 149 ], web logs [ 37 ], smartphone life logs [ 95 ], etc. are some examples of log data for smartphone devices. The main characteristics of these log data is that it contains users’ actual behavioral activities with their devices. Similar other log data can be search logs [ 50 , 133 ], application logs [ 26 ], server logs [ 33 ], network logs [ 57 ], event logs [ 83 ], network and security logs [ 142 ] etc.

Several techniques such as classification and tagging, correlation analysis, pattern recognition methods, anomaly detection methods, machine learning modeling, etc. [ 105 ] can be used for effective log analysis. Log analysis can assist in compliance with security policies and industry regulations, as well as provide a better user experience by encouraging the troubleshooting of technical problems and identifying areas where efficiency can be improved. For instance, web servers use log files to record data about website visitors. Windows event log analysis can help an investigator draw a timeline based on the logging information and the discovered artifacts. Overall, advanced analytics methods by taking into account machine learning modeling can play a significant role to extract insightful patterns from these log data, which can be used for building automated and smart applications, and thus can be considered as a key working area in data science.

Neural Networks and Deep Learning Analysis

Deep learning is a form of machine learning that uses artificial neural networks to create a computational architecture that learns from data by combining multiple processing layers, such as the input, hidden, and output layers [ 38 ]. The key benefit of deep learning over conventional machine learning methods is that it performs better in a variety of situations, particularly when learning from large datasets [ 114 , 140 ].

The most common deep learning algorithms are: multi-layer perceptron (MLP) [ 85 ], convolutional neural network (CNN or ConvNet) [ 67 ], long short term memory recurrent neural network (LSTM-RNN) [ 34 ]. Figure ​ Figure6 6 shows a structure of an artificial neural network modeling with multiple processing layers. The Backpropagation technique [ 38 ] is used to adjust the weight values internally while building the model. Convolutional neural networks (CNNs) [ 67 ] improve on the design of traditional artificial neural networks (ANNs), which include convolutional layers, pooling layers, and fully connected layers. It is commonly used in a variety of fields, including natural language processing, speech recognition, image processing, and other autocorrelated data since it takes advantage of the two-dimensional (2D) structure of the input data. AlexNet [ 60 ], Xception [ 21 ], Inception [ 125 ], Visual Geometry Group (VGG) [ 42 ], ResNet [ 43 ], etc., and other advanced deep learning models based on CNN are also used in the field.

A structure of an artificial neural network modeling with multiple processing layers

In addition to CNN, recurrent neural network (RNN) architecture is another popular method used in deep learning. Long short-term memory (LSTM) is a popular type of recurrent neural network architecture used broadly in the area of deep learning. Unlike traditional feed-forward neural networks, LSTM has feedback connections. Thus, LSTM networks are well-suited for analyzing and learning sequential data, such as classifying, sorting, and predicting data based on time-series data. Therefore, when the data is in a sequential format, such as time, sentence, etc., LSTM can be used, and it is widely used in the areas of time-series analysis, natural language processing, speech recognition, and so on.

In addition to the most popular deep learning methods mentioned above, several other deep learning approaches [ 104 ] exist in the field for various purposes. The self-organizing map (SOM) [ 58 ], for example, uses unsupervised learning to represent high-dimensional data as a 2D grid map, reducing dimensionality. Another learning technique that is commonly used for dimensionality reduction and feature extraction in unsupervised learning tasks is the autoencoder (AE) [ 10 ]. Restricted Boltzmann machines (RBM) can be used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling, according to [ 46 ]. A deep belief network (DBN) is usually made up of a backpropagation neural network and unsupervised networks like restricted Boltzmann machines (RBMs) or autoencoders (BPNN) [ 136 ]. A generative adversarial network (GAN) [ 35 ] is a deep learning network that can produce data with characteristics that are similar to the input data. Transfer learning is common worldwide presently because it can train deep neural networks with a small amount of data, which is usually the re-use of a pre-trained model on a new problem [ 137 ]. These deep learning methods can perform  well, particularly, when learning from large-scale datasets [ 105 , 140 ]. In our previous article Sarker et al. [ 104 ], we have summarized a brief discussion of various artificial neural networks (ANN) and deep learning (DL) models mentioned above, which can be used in a variety of data science and analytics tasks.

Real-World Application Domains

Almost every industry or organization is impacted by data, and thus “Data Science” including advanced analytics with machine learning modeling can be used in business, marketing, finance, IoT systems, cybersecurity, urban management, health care, government policies, and every possible industries, where data gets generated. In the following, we discuss ten most popular application areas based on data science and analytics.

• Business or financial data science: In general, business data science can be considered as the study of business or e-commerce data to obtain insights about a business that can typically lead to smart decision-making as well as taking high-quality actions [ 90 ]. Data scientists can develop algorithms or data-driven models predicting customer behavior, identifying patterns and trends based on historical business data, which can help companies to reduce costs, improve service delivery, and generate recommendations for better decision-making. Eventually, business automation, intelligence, and efficiency can be achieved through the data science process discussed earlier, where various advanced analytics methods and machine learning modeling based on the collected data are the keys. Many online retailers, such as Amazon [ 76 ], can improve inventory management, avoid out-of-stock situations, and optimize logistics and warehousing using predictive modeling based on machine learning techniques [ 105 ]. In terms of finance, the historical data is related to financial institutions to make high-stakes business decisions, which is mostly used for risk management, fraud prevention, credit allocation, customer analytics, personalized services, algorithmic trading, etc. Overall, data science methodologies can play a key role in the future generation business or finance industry, particularly in terms of business automation, intelligence, and smart decision-making and systems.
• Manufacturing or industrial data science: To compete in global production capability, quality, and cost, manufacturing industries have gone through many industrial revolutions [ 14 ]. The latest fourth industrial revolution, also known as Industry 4.0, is the emerging trend of automation and data exchange in manufacturing technology. Thus industrial data science, which is the study of industrial data to obtain insights that can typically lead to optimizing industrial applications, can play a vital role in such revolution. Manufacturing industries generate a large amount of data from various sources such as sensors, devices, networks, systems, and applications [ 6 , 68 ]. The main categories of industrial data include large-scale data devices, life-cycle production data, enterprise operation data, manufacturing value chain sources, and collaboration data from external sources [ 132 ]. The data needs to be processed, analyzed, and secured to help improve the system’s efficiency, safety, and scalability. Data science modeling thus can be used to maximize production, reduce costs and raise profits in manufacturing industries.
• Medical or health data science: Healthcare is one of the most notable fields where data science is making major improvements. Health data science involves the extrapolation of actionable insights from sets of patient data, typically collected from electronic health records. To help organizations, improve the quality of treatment, lower the cost of care, and improve the patient experience, data can be obtained from several sources, e.g., the electronic health record, billing claims, cost estimates, and patient satisfaction surveys, etc., to analyze. In reality, healthcare analytics using machine learning modeling can minimize medical costs, predict infectious outbreaks, prevent preventable diseases, and generally improve the quality of life [ 81 , 119 ]. Across the global population, the average human lifespan is growing, presenting new challenges to today’s methods of delivery of care. Thus health data science modeling can play a role in analyzing current and historical data to predict trends, improve services, and even better monitor the spread of diseases. Eventually, it may lead to new approaches to improve patient care, clinical expertise, diagnosis, and management.
• IoT data science: Internet of things (IoT) [ 9 ] is a revolutionary technical field that turns every electronic system into a smarter one and is therefore considered to be the big frontier that can enhance almost all activities in our lives. Machine learning has become a key technology for IoT applications because it uses expertise to identify patterns and generate models that help predict future behavior and events [ 112 ]. One of the IoT’s main fields of application is a smart city, which uses technology to improve city services and citizens’ living experiences. For example, using the relevant data, data science methods can be used for traffic prediction in smart cities, to estimate the total usage of energy of the citizens for a particular period. Deep learning-based models in data science can be built based on a large scale of IoT datasets [ 7 , 104 ]. Overall, data science and analytics approaches can aid modeling in a variety of IoT and smart city services, including smart governance, smart homes, education, connectivity, transportation, business, agriculture, health care, and industry, and many others.
• Cybersecurity data science: Cybersecurity, or the practice of defending networks, systems, hardware, and data from digital attacks, is one of the most important fields of Industry 4.0 [ 114 , 121 ]. Data science techniques, particularly machine learning, have become a crucial cybersecurity technology that continually learns to identify trends by analyzing data, better detecting malware in encrypted traffic, finding insider threats, predicting where bad neighborhoods are online, keeping people safe while surfing, or protecting information in the cloud by uncovering suspicious user activity [ 114 ]. For instance, machine learning and deep learning-based security modeling can be used to effectively detect various types of cyberattacks or anomalies [ 103 , 106 ]. To generate security policy rules, association rule learning can play a significant role to build rule-based systems [ 102 ]. Deep learning-based security models can perform better when utilizing the large scale of security datasets [ 140 ]. Thus data science modeling can enable professionals in cybersecurity to be more proactive in preventing threats and reacting in real-time to active attacks, through extracting actionable insights from the security datasets.
• Behavioral data science: Behavioral data is information produced as a result of activities, most commonly commercial behavior, performed on a variety of Internet-connected devices, such as a PC, tablet, or smartphones [ 112 ]. Websites, mobile applications, marketing automation systems, call centers, help desks, and billing systems, etc. are all common sources of behavioral data. Behavioral data is much more than just data, which is not static data [ 108 ]. Advanced analytics of these data including machine learning modeling can facilitate in several areas such as predicting future sales trends and product recommendations in e-commerce and retail; predicting usage trends, load, and user preferences in future releases in online gaming; determining how users use an application to predict future usage and preferences in application development; breaking users down into similar groups to gain a more focused understanding of their behavior in cohort analysis; detecting compromised credentials and insider threats by locating anomalous behavior, or making suggestions, etc. Overall, behavioral data science modeling typically enables to make the right offers to the right consumers at the right time on various common platforms such as e-commerce platforms, online games, web and mobile applications, and IoT. In social context, analyzing the behavioral data of human being using advanced analytics methods and the extracted insights from social data can be used for data-driven intelligent social services, which can be considered as social data science.
• Mobile data science: Today’s smart mobile phones are considered as “next-generation, multi-functional cell phones that facilitate data processing, as well as enhanced wireless connectivity” [ 146 ]. In our earlier paper [ 112 ], we have shown that users’ interest in “Mobile Phones” is more and more than other platforms like “Desktop Computer”, “Laptop Computer” or “Tablet Computer” in recent years. People use smartphones for a variety of activities, including e-mailing, instant messaging, online shopping, Internet surfing, entertainment, social media such as Facebook, Linkedin, and Twitter, and various IoT services such as smart cities, health, and transportation services, and many others. Intelligent apps are based on the extracted insight from the relevant datasets depending on apps characteristics, such as action-oriented, adaptive in nature, suggestive and decision-oriented, data-driven, context-awareness, and cross-platform operation [ 112 ]. As a result, mobile data science, which involves gathering a large amount of mobile data from various sources and analyzing it using machine learning techniques to discover useful insights or data-driven trends, can play an important role in the development of intelligent smartphone applications.
• Multimedia data science: Over the last few years, a big data revolution in multimedia management systems has resulted from the rapid and widespread use of multimedia data, such as image, audio, video, and text, as well as the ease of access and availability of multimedia sources. Currently, multimedia sharing websites, such as Yahoo Flickr, iCloud, and YouTube, and social networks such as Facebook, Instagram, and Twitter, are considered as valuable sources of multimedia big data [ 89 ]. People, particularly younger generations, spend a lot of time on the Internet and social networks to connect with others, exchange information, and create multimedia data, thanks to the advent of new technology and the advanced capabilities of smartphones and tablets. Multimedia analytics deals with the problem of effectively and efficiently manipulating, handling, mining, interpreting, and visualizing various forms of data to solve real-world problems. Text analysis, image or video processing, computer vision, audio or speech processing, and database management are among the solutions available for a range of applications including healthcare, education, entertainment, and mobile devices.
• Smart cities or urban data science: Today, more than half of the world’s population live in urban areas or cities [ 80 ] and considered as drivers or hubs of economic growth, wealth creation, well-being, and social activity [ 96 , 116 ]. In addition to cities, “Urban area” can refer to the surrounding areas such as towns, conurbations, or suburbs. Thus, a large amount of data documenting daily events, perceptions, thoughts, and emotions of citizens or people are recorded, that are loosely categorized into personal data, e.g., household, education, employment, health, immigration, crime, etc., proprietary data, e.g., banking, retail, online platforms data, etc., government data, e.g., citywide crime statistics, or government institutions, etc., Open and public data, e.g., data.gov, ordnance survey, and organic and crowdsourced data, e.g., user-generated web data, social media, Wikipedia, etc. [ 29 ]. The field of urban data science typically focuses on providing more effective solutions from a data-driven perspective, through extracting knowledge and actionable insights from such urban data. Advanced analytics of these data using machine learning techniques [ 105 ] can facilitate the efficient management of urban areas including real-time management, e.g., traffic flow management, evidence-based planning decisions which pertain to the longer-term strategic role of forecasting for urban planning, e.g., crime prevention, public safety, and security, or framing the future, e.g., political decision-making [ 29 ]. Overall, it can contribute to government and public planning, as well as relevant sectors including retail, financial services, mobility, health, policing, and utilities within a data-rich urban environment through data-driven smart decision-making and policies, which lead to smart cities and improve the quality of human life.
• Smart villages or rural data science: Rural areas or countryside are the opposite of urban areas, that include villages, hamlets, or agricultural areas. The field of rural data science typically focuses on making better decisions and providing more effective solutions that include protecting public safety, providing critical health services, agriculture, and fostering economic development from a data-driven perspective, through extracting knowledge and actionable insights from the collected rural data. Advanced analytics of rural data including machine learning [ 105 ] modeling can facilitate providing new opportunities for them to build insights and capacity to meet current needs and prepare for their futures. For instance, machine learning modeling [ 105 ] can help farmers to enhance their decisions to adopt sustainable agriculture utilizing the increasing amount of data captured by emerging technologies, e.g., the internet of things (IoT), mobile technologies and devices, etc. [ 1 , 51 , 52 ]. Thus, rural data science can play a very important role in the economic and social development of rural areas, through agriculture, business, self-employment, construction, banking, healthcare, governance, or other services, etc. that lead to smarter villages.

Overall, we can conclude that data science modeling can be used to help drive changes and improvements in almost every sector in our real-world life, where the relevant data is available to analyze. To gather the right data and extract useful knowledge or actionable insights from the data for making smart decisions is the key to data science modeling in any application domain. Based on our discussion on the above ten potential real-world application domains by taking into account data-driven smart computing and decision making, we can say that the prospects of data science and the role of data scientists are huge for the future world. The “Data Scientists” typically analyze information from multiple sources to better understand the data and business problems, and develop machine learning-based analytical modeling or algorithms, or data-driven tools, or solutions, focused on advanced analytics, which can make today’s computing process smarter, automated, and intelligent.

Challenges and Research Directions

Our study on data science and analytics, particularly data science modeling in “ Understanding data science modeling ”, advanced analytics methods and smart computing in “ Advanced analytics methods and smart computing ”, and real-world application areas in “ Real-world application domains ” open several research issues in the area of data-driven business solutions and eventual data products. Thus, in this section, we summarize and discuss the challenges faced and the potential research opportunities and future directions to build data-driven products.

• Understanding the real-world business problems and associated data including nature, e.g., what forms, type, size, labels, etc., is the first challenge in the data science modeling, discussed briefly in “ Understanding data science modeling ”. This is actually to identify, specify, represent and quantify the domain-specific business problems and data according to the requirements. For a data-driven effective business solution, there must be a well-defined workflow before beginning the actual data analysis work. Furthermore, gathering business data is difficult because data sources can be numerous and dynamic. As a result, gathering different forms of real-world data, such as structured, or unstructured, related to a specific business issue with legal access, which varies from application to application, is challenging. Moreover, data annotation, which is typically the process of categorization, tagging, or labeling of raw data, for the purpose of building data-driven models, is another challenging issue. Thus, the primary task is to conduct a more in-depth analysis of data collection and dynamic annotation methods. Therefore, understanding the business problem, as well as integrating and managing the raw data gathered for efficient data analysis, may be one of the most challenging aspects of working in the field of data science and analytics.
• The next challenge is the extraction of the relevant and accurate information from the collected data mentioned above. The main focus of data scientists is typically to disclose, describe, represent, and capture data-driven intelligence for actionable insights from data. However, the real-world data may contain many ambiguous values, missing values, outliers, and meaningless data [ 101 ]. The advanced analytics methods including machine and deep learning modeling, discussed in “ Advanced analytics methods and smart computing ”, highly impact the quality, and availability of the data. Thus understanding real-world business scenario and associated data, to whether, how, and why they are insufficient, missing, or problematic, then extend or redevelop the existing methods, such as large-scale hypothesis testing, learning inconsistency, and uncertainty, etc. to address the complexities in data and business problems is important. Therefore, developing new techniques to effectively pre-process the diverse data collected from multiple sources, according to their nature and characteristics could be another challenging task.
• Understanding and selecting the appropriate analytical methods to extract the useful insights for smart decision-making for a particular business problem is the main issue in the area of data science. The emphasis of advanced analytics is more on anticipating the use of data to detect patterns to determine what is likely to occur in the future. Basic analytics offer a description of data in general, while advanced analytics is a step forward in offering a deeper understanding of data and helping to granular data analysis. Thus, understanding the advanced analytics methods, especially machine and deep learning-based modeling is the key. The traditional learning techniques mentioned in “ Advanced analytics methods and smart computing ” may not be directly applicable for the expected outcome in many cases. For instance, in a rule-based system, the traditional association rule learning technique [ 4 ] may  produce redundant rules from the data that makes the decision-making process complex and ineffective [ 113 ]. Thus, a scientific understanding of the learning algorithms, mathematical properties, how the techniques are robust or fragile to input data, is needed to understand. Therefore, a deeper understanding of the strengths and drawbacks of the existing machine and deep learning methods [ 38 , 105 ] to solve a particular business problem is needed, consequently to improve or optimize the learning algorithms according to the data characteristics, or to propose the new algorithm/techniques with higher accuracy becomes a significant challenging issue for the future generation data scientists.
• The traditional data-driven models or systems typically use a large amount of business data to generate data-driven decisions. In several application fields, however, the new trends are more likely to be interesting and useful for modeling and predicting the future than older ones. For example, smartphone user behavior modeling, IoT services, stock market forecasting, health or transport service, job market analysis, and other related areas where time-series and actual human interests or preferences are involved over time. Thus, rather than considering the traditional data analysis, the concept of RecencyMiner, i.e., recent pattern-based extracted insight or knowledge proposed in our earlier paper Sarker et al. [ 108 ] might be effective. Therefore, to propose the new techniques by taking into account the recent data patterns, and consequently to build a recency-based data-driven model for solving real-world problems, is another significant challenging issue in the area.
• The most crucial task for a data-driven smart system is to create a framework that supports data science modeling discussed in “ Understanding data science modeling ”. As a result, advanced analytical methods based on machine learning or deep learning techniques can be considered in such a system to make the framework capable of resolving the issues. Besides, incorporating contextual information such as temporal context, spatial context, social context, environmental context, etc. [ 100 ] can be used for building an adaptive, context-aware, and dynamic model or framework, depending on the problem domain. As a result, a well-designed data-driven framework, as well as experimental evaluation, is a very important direction to effectively solve a business problem in a particular domain, as well as a big challenge for the data scientists.
• In several important application areas such as autonomous cars, criminal justice, health care, recruitment, housing, management of the human resource, public safety, where decisions made by models, or AI agents, have a direct effect on human lives. As a result, there is growing concerned about whether these decisions can be trusted, to be right, reasonable, ethical, personalized, accurate, robust, and secure, particularly in the context of adversarial attacks [ 104 ]. If we can explain the result in a meaningful way, then the model can be better trusted by the end-user. For machine-learned models, new trust properties yield new trade-offs, such as privacy versus accuracy; robustness versus efficiency; fairness versus robustness. Therefore, incorporating trustworthy AI particularly, data-driven or machine learning modeling could be another challenging issue in the area.

In the above, we have summarized and discussed several challenges and the potential research opportunities and directions, within the scope of our study in the area of data science and advanced analytics. The data scientists in academia/industry and the researchers in the relevant area have the opportunity to contribute to each issue identified above and build effective data-driven models or systems, to make smart decisions in the corresponding business domains.

In this paper, we have presented a comprehensive view on data science including various types of advanced analytical methods that can be applied to enhance the intelligence and the capabilities of an application. We have also visualized the current popularity of data science and machine learning-based advanced analytical modeling and also differentiate these from the relevant terms used in the area, to make the position of this paper. A thorough study on the data science modeling with its various processing modules that are needed to extract the actionable insights from the data for a particular business problem and the eventual data product. Thus, according to our goal, we have briefly discussed how different data modules can play a significant role in a data-driven business solution through the data science process. For this, we have also summarized various types of advanced analytical methods and outcomes as well as machine learning modeling that are needed to solve the associated business problems. Thus, this study’s key contribution has been identified as the explanation of different advanced analytical methods and their applicability in various real-world data-driven applications areas including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making.

Finally, within the scope of our study, we have outlined and discussed the challenges we faced, as well as possible research opportunities and future directions. As a result, the challenges identified provide promising research opportunities in the field that can be explored with effective solutions to improve the data-driven model and systems. Overall, we conclude that our study of advanced analytical solutions based on data science and machine learning methods, leads in a positive direction and can be used as a reference guide for future research and applications in the field of data science and its real-world applications by both academia and industry professionals.

Declarations

The author declares no conflict of interest.

This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

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Your Modern Business Guide To Data Analysis Methods And Techniques

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

• Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
• Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
• Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
• Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

• Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
• Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
• Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

• Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
• Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
• Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
• Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
• Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

• Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
• Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
• Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

• Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
• Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
• SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
• Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

• Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
• External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
• Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
• Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

• Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
• Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
• Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
• Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
• Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
• Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
• Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
• Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

• Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
• Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
• Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
• SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
• Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

• By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
• 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
• Companies that exploit the full potential of their data can increase their operating margins by 60% .
• We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

• Cluster analysis
• Cohort analysis
• Regression analysis
• Factor analysis
• Neural Networks
• Data Mining
• Text analysis
• Time series analysis
• Decision trees
• Conjoint analysis 
• Correspondence Analysis
• Multidimensional Scaling 
• Content analysis 
• Thematic analysis
• Narrative analysis 
• Grounded theory analysis
• Discourse analysis 

Top 17 Data Analysis Techniques:

• Collaborate your needs
• Establish your questions
• Data democratization
• Think of data governance 
• Clean your data
• Set your KPIs
• Omit useless data
• Build a data management roadmap
• Integrate technology
• Answer your questions
• Visualize your data
• Interpretation of data
• Consider autonomous technology
• Build a narrative
• Share the load
• Data Analysis tools
• Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

What Is Data Analysis and Why Is It Important?

What is data analysis? We explain data mining, analytics, and data visualization in simple to understand terms.

The world is becoming more and more data-driven, with endless amounts of data available to work with. Big companies like Google and Microsoft use data to make decisions, but they're not the only ones.

Is it important? Absolutely!

Data analysis is used by small businesses, retail companies, in medicine, and even in the world of sports. It's a universal language and more important than ever before. It seems like an advanced concept but data analysis is really just a few ideas put into practice.

What Is Data Analysis?

Data analysis is the process of evaluating data using analytical or statistical tools to discover useful information. Some of these tools are programming languages like R or Python. Microsoft Excel is also popular in the world of data analytics .

Once data is collected and sorted using these tools, the results are interpreted to make decisions. The end results can be delivered as a summary, or as a visual like a chart or graph.

The process of presenting data in visual form is known as data visualization . Data visualization tools make the job easier. Programs like Tableau or Microsoft Power BI give you many visuals that can bring data to life.

There are several data analysis methods including data mining, text analytics, and business intelligence.

How Is Data Analysis Performed?

Data analysis is a big subject and can include some of these steps:

• Defining Objectives: Start by outlining some clearly defined objectives. To get the best results out of the data, the objectives should be crystal clear.
• Posing Questions: Figure out the questions you would like answered by the data. For example, do red sports cars get into accidents more often than others? Figure out which data analysis tools will get the best result for your question.
• Data Collection: Collect data that is useful to answer the questions. In this example, data might be collected from a variety of sources like DMV or police accident reports, insurance claims and hospitalization details.
• Data Scrubbing: Raw data may be collected in several different formats, with lots of junk values and clutter. The data is cleaned and converted so that data analysis tools can import it. It's not a glamorous step but it's very important.
• Data Analysis: Import this new clean data into the data analysis tools. These tools allow you to explore the data, find patterns, and answer what-if questions. This is the payoff, this is where you find results!
• Drawing Conclusions and Making Predictions: Draw conclusions from your data. These conclusions may be summarized in a report, visual, or both to get the right results.

Let's dig a little deeper into some concepts used in data analysis.

Data Mining

Data mining is a method of data analysis for discovering patterns in large data sets using statistics, artificial intelligence, and machine learning. The goal is to turn data into business decisions.

What can you do with data mining? You can process large amounts of data to identify outliers and exclude them from decision making. Businesses can learn customer purchasing habits, or use clustering to find previously unknown groups within the data.

If you use email, you see another example of data mining to sort your mailbox. Email apps like Outlook or Gmail use this to categorize your emails as "spam" or "not spam".

Text Analytics

Data is not just limited to numbers, information can come from text information as well.

Text analytics is the process of finding useful information from text. You do this by processing raw text, making it readable by data analysis tools, and finding results and patterns. This is also known as text mining.

Excel does a great job with this. Excel has many formulas to work with text that can save you time when you go to work with the data.

Text mining can also collect information from the web, a database or a file system. What can you do with this text information? You can import email addresses and phone numbers to find patterns. You can even find frequencies of words in a document.

Business Intelligence

Business intelligence transforms data into intelligence used to make business decisions. It may be used in an organization's strategic and tactical decision making. It offers a way for companies to examine trends from collected data and get insights from it.

Business intelligence is used to do a lot of things:

• Make decisions about product placement and pricing
• Identify new markets for product
• Create budgets and forecasts that make more money
• Use visual tools such as heat maps, pivot tables, and geographical mapping to find the demand for a certain product

Data Visualization

Data visualization is the visual representation of data. Instead of presenting data in tables or databases, you present it in charts and graphs. It makes complex data more understandable, not to mention easier to look at.

Increasing amounts of data are being generated by applications you use (Also known as the "Internet of Things"). The amount of data (referred to as "big data") is pretty massive. Data visualization can turn millions of data points into simple visuals that make it easy to understand.

There are various ways to visualize data:

• Using a data visualization tool like Tableau or Microsoft Power BI
• Standard Excel graphs and charts
• Interactive Excel graphs
• For the web, a tool like D3.js built using JavaScript

The visualization of Google datasets is a great example of how big data can visually guide decision-making.

Data Analysis in Review

Data analysis is used to evaluate data with statistical tools to discover useful information. A variety of methods are used including data mining , text analytics, business intelligence, combining data sets , and data visualization.

The Power Query tool in Microsoft Excel is especially helpful for data analysis. If you want to familiarize yourself with it, read our guide to create your first Microsoft Power Query script .

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Business Analytics: What It Is & Why It's Important

• 16 Jul 2019

Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.

According to a study by MicroStrategy , companies worldwide are using data to:

• Improve efficiency and productivity (64 percent)
• Achieve more effective decision-making (56 percent)
• Drive better financial performance (51 percent)

The research also shows that 65 percent of global enterprises plan to increase analytics spending.

In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics , in a previous interview . “If you’re able to go into a meeting and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means.

Check out our video on business analytics below, and subscribe to our YouTube channel for more explainer content!

What Is Business Analytics?

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

There are four primary methods of business analysis:

• Descriptive : The interpretation of historical data to identify trends and patterns
• Diagnostic : The interpretation of historical data to determine why something has happened
• Predictive : The use of statistics to forecast future outcomes
• Prescriptive : The application of testing and other techniques to determine which outcome will yield the best result in a given scenario

These four types of business analytics methods can be used individually or in tandem to analyze past efforts and improve future business performance.

Business Analytics vs. Data Science

To understand what business analytics is, it’s also important to distinguish it from data science. While both processes analyze data to solve business problems, the difference between business analytics and data science lies in how data is used.

Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process , whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming. Despite their differences, both business analytics and data science glean insights from data to inform business decisions.

To better understand how data insights can drive organizational performance, here are some of the ways firms have benefitted from using business analytics.

The Benefits of Business Analytics

1. more informed decision-making.

Business analytics can be a valuable resource when approaching an important strategic decision.

When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve speed and accuracy when responding to support tickets—it used prescriptive analytics to examine whether the product’s new iteration would be more effective than its initial version.

Through A/B testing —a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process, but saved the company millions of dollars.

2. Greater Revenue

Companies that embrace data and analytics initiatives can experience significant financial returns.

Research by McKinsey shows organizations that invest in big data yield a six percent average increase in profits, which jumps to nine percent for investments spanning five years.

Echoing this trend, a recent study by BARC found that businesses able to quantify their gains from analyzing data report an average eight percent increase in revenues and a 10 percent reduction in costs.

These findings illustrate the clear financial payoff that can come from a robust business analysis strategy—one that many firms can stand to benefit from as the big data and analytics market grows.

Related: 5 Business Analytics Skills for Professionals

3. Improved Operational Efficiency

Beyond financial gains, analytics can be used to fine-tune business processes and operations.

In a recent KPMG report on emerging trends in infrastructure, it was found that many firms now use predictive analytics to anticipate maintenance and operational issues before they become larger problems.

A mobile network operator surveyed noted that it leverages data to foresee outages seven days before they occur. Armed with this information, the firm can prevent outages by more effectively timing maintenance, enabling it to not only save on operational costs, but ensure it keeps assets at optimal performance levels.

Why Study Business Analytics?

Taking a data-driven approach to business can come with tremendous upside, but many companies report that the number of skilled employees in analytics roles are in short supply .

LinkedIn lists business analysis as one of the skills companies need most in 2020 , and the Bureau of Labor Statistics projects operations research analyst jobs to grow by 23 percent through 2031—a rate much faster than the average for all occupations.

“A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing,” said Hammond in a previous interview .

Skills Business Analysts Need

Success as a business analyst goes beyond knowing how to crunch numbers. In addition to collecting data and using statistics to analyze it, it’s crucial to have critical thinking skills to interpret the results. Strong communication skills are also necessary for effectively relaying insights to those who aren’t familiar with advanced analytics. An effective data analyst has both the technical and soft skills to ensure an organization is making the best use of its data.

Improving Your Business Analytics Skills

If you’re interested in capitalizing on the need for data-minded professionals, taking an online business analytics course is one way to broaden your analytical skill set and take your career to the next level

Through learning how to recognize trends, test hypotheses , and draw conclusions from population samples, you can build an analytical framework that can be applied in your everyday decision-making and help your organization thrive.

“If you don’t use the data, you’re going to fall behind,” Hammond said . “People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics to learn how to use data analysis to solve business problems.

This post was updated on November 14, 2022. It was originally published on July 16, 2019.

About the Author

What Data Analysis Is and the Skills Needed to Succeed

Use the tools and techniques of data analysis to make sense of mountains of information..

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From counting steps with a smartwatch to visiting this website, nearly everything we do generates data. But just collecting statistics, measurements and other numbers and storing the information is not enough. How we harness data is the key to success in our digital world.

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What Is Data Analysis and Why Is it Necessary?

How many steps you took today doesn’t mean anything unless you know information like how many steps you took yesterday, how many steps you take on average and how many steps you should be taking.

When you gather information, organize it and draw conclusions and insights, then you can make better decisions, improve operations, fine-tune technology and so on. Data analysis includes evaluating and recapping information, and it can help us understand patterns and trends.

Types of Data Analysis

There are four main types of data analysis: descriptive, diagnostic, predictive and prescriptive. These data analysis methods build on each other like tiers of a wedding cake.

Descriptive Data Analysis

Descriptive statistics tell you what is in the data you’ve gathered. Building blocks include how many data points you have, average and median measurements, the amount of variation within your data, and the certainty those things provide about your results.

Diagnostic Data Analysis

Diagnostic data analysis – also called causal analysis – examines the relationships among data to uncover possible causes and effects. To accomplish this, you might look for known relationships to explain observations or use data to identify unknown relationships.

Predictive Data Analysis

Building on diagnostic data analysis is predictive analysis, where you use those relationships to generate predictions about future results. These “models” can range from equations in a spreadsheet to applications of artificial intelligence requiring vast computing resources.

Predictive modeling is the heart of analysis, says Nick Street, professor of business analytics and associate dean for research and Ph.D. programs at the University of Iowa’s Tippie College of Business.

“My poll needs to be correct about the people who are going to vote, and my self-driving car has to be correct about whether that’s a stop sign or not,” Street says.

Prescriptive Data Analysis

Often, the goal of data analysis is to help make sound decisions. While all types of data analysis can help you accomplish this, prescriptive data analysis provides a deeper understanding of costs, benefits and risks. Basically, prescriptive data analysis helps us answer the question, “What should I do?”

The most common kind of prescriptive analysis is optimization, or figuring out "the best results under the given circumstances," according to a post at Data Science Central. So, given a set of constraints, which inputs provide the most benefit for the lowest cost and least amount of risk. For example, a particular step in surgery might reduce the risk of infection but increase the risk of other complications.

In Street’s work, data can inform a decision by predicting how likely a patient is to get an infection without the step in surgery that is supposed to reduce infection risk. That way, a doctor could determine whether the extra step is actually beneficial, or if the step could be removed from the surgical process.

Of course, while a data analyst can provide the prescriptive analysis, a doctor would need to interpret the probability and make a decision based on the data.

“I’m not qualified to make that decision,” Street says of a data analyst’s role. “I can just tell you that for this person it’s (63%).”

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Data Analysis Tools, Techniques and Methods

Data analysis involves a spectrum of tools and methodologies with overlapping goals, strengths and capabilities. Here is how each working part contributes to effective data analysis.

The Data Analysis Phases

There are different ways of looking at the phases of data analysis. Here is a typical framework.

1. Data Requirements

You need to know the questions you want to answer and determine what data you require in order to find the answer.

2. Data Collection

This involves identifying data that might answer your questions, determining what steps are required to gather the data, and understanding what strengths and weaknesses each type of data might present. Not all data is strong or relevant for answering your question.

Charlie McHenry, a partner at consulting firm Green Econometrics, says figuring out which data matters to answer a question might seem difficult, but the information you need is often hiding in plain sight.

For example, consider the data gathered from business systems, surveys and information downloaded from social media platforms. You might also consider purchasing commercial data or using public datasets.

“Every enterprise has a fire hose of collectable data,” McHenry says.

3. Data Cleansing

This is the most delicate stage of data analysis, and it often takes the most time to accomplish. All data comes in “dirty,” containing errors, omissions and biases. While data doesn’t lie, accurate analysis requires identifying and accounting for imperfections.

For example, lists of people often contain multiple entries with different spellings. The same person might appear with the names Anne, Annie and Ann. At least one of those is misspelled, and treating her as three separate people is always incorrect.

4. Data Analysis

The meatiest phase is applying descriptive, diagnostic, predictive and prescriptive analysis to the data. At first, the results may be baffling or contradictory, but always keep digging.

Just be vigilant and look for these common errors:

• False positives that seem important but are actually coincidental.
• False negatives, which are important relationships that are hidden by dirty data or statistical noise.
• Lurking variables, where an apparent relationship is caused by something the data didn’t capture.

5. Data Interpretation

This stage is where a data analyst must practice careful judgment and has the most chance to be wrong. It’s up to an analyst to determine which models, statistics and relationships are actually important.

Then the data analyst must understand and explain what the models do and do not mean. For instance, political scientists and journalists often build models to predict a presidential election by using polls. In 2008 and 2012, those models correctly predicted the results. In 2016, those models showed lower levels of certainty, and the candidate they said was more likely to win did not. By ignoring the change in certainty, many people were shocked by the election results, falling prey to confirmation bias because they only saw data that supported their beliefs about who would win.

6 . Data Visualization

Staring at equations and columns of numbers is not appealing to many people. That’s why a data analyst has to make the numbers “friendly” by transforming data into visuals like charts and graphs. Modern data visualization takes this a step further and includes digital graphics and dashboards of interrelated charts that people can explore online.

Data Analysis Tools

While there are countless tools for each phase of data analysis, the most popular tools break down in the following way:

Data Collection

• SurveyMonkey: Do you need to collect data from your users or customers? There are many tools for online surveys, but SurveyMonkey is popular with analysts for its ease of use, features and capabilities. You can apply it to survey all users, only a random portion or a sample of the public.
• Data.world: There is a lot of data already out there, much more than any person can find just by searching the web. While data.world’s primary emphasis is allowing companies to host and analyze their own data in the cloud, its community portal has a rich set of datasets you can use. Other go-to data collections include: FRED for economic data, ESRI ArcGIS Online for geographic data and the federal government’s Data.gov .
• Google Analytics: Google produces a tool for tracking users online. If you have a website, you can use this free tool to measure virtually any aspect of user behavior. Competitors include Adobe Marketing Cloud, Open Web Analytics and Plausible Analytics.

Data Storage

• Microsoft Excel : The Swiss Army knife of data analysis, current versions of the Microsoft Excel spreadsheet can store up to 1 million rows of data. It also has basic tools for manipulating and visualizing data. Excel is available in desktop, mobile and online versions. Competitors include Google Sheets, Apple’s Numbers and Apache OpenOffice.
• PostgreSQL: One of the most popular of the traditional database systems, PostgreSQL can store and query gigabytes of information split into “tables” for each kind of data. It has the SQL language built in (see below), can be used locally or in the cloud, and can be integrated with virtually any programming language. Competitors include Microsoft SQL Server, Microsoft Access and MySQL.
• MongoDB: This is a popular “nonrelational” database. MongoDB combines data so that all the information related to a given entity, such as customers, is stored in a single collection of nested data. Competitors include Apache CouchDB, Amazon DynamoDB and Apache HBase.

Data Manipulation/Programming

Of course, gathering and storing data aren’t enough. Data analysis involves tools to clean data, then transform it, summarize it and develop models from it.

• SQL: The go-to choice when your data gets too big or complex for Excel, SQL is a system for writing “queries” of a database to extract and summarize data matching a particular set of conditions. It is built into relational database programs and requires one to work. Each database system has its own version of SQL with varying levels of capability.
• R : R is the favored programming language of statisticians. It is free and has a large ecosystem of community-developed packages for specific analytical tasks. It especially excels in data manipulation, data visualization and calculations, while being less used for advanced techniques requiring heavy computation.
• Python : Python is the second-most-popular programming language in the world. It is used for everything from building websites to operating the International Space Station. In data analysis, Python excels at advanced techniques like web scraping (automatically gathering data from online sources), machine learning and natural language processing.

Data Visualization

• Tableau : Analysts swear by this desktop program’s compatibility with nearly any data source, ability to generate complex graphics, and capability of publishing interactive dashboards that allow users to explore the data for themselves.
• Google Data Studio : Similar in some ways to Tableau, this is a web-based tool that focuses on ease of use over complex capabilities. It’s strongly integrated with other Google products, and many say it produces the best-looking results out of the box.
• Microsoft Power BI : No list of data visualization tools would be complete without Microsoft Power BI. It’s tightly linked with Microsoft’s desktop, database and cloud offerings, and focuses on allowing users to create their own dashboards and visualizations.

Data Warehousing

Left flowing, the “fire hose" of data McHenry describes quickly overwhelms most databases. Where can you store a clearinghouse of information? Here are some options:

• Oracle Database : Known as “Big Red,” Oracle is famed for its ability to scale vast quantities of data. Oracle Database allows users to store and analyze big data using familiar database formats and tools like SQL.
• Amazon Redshift : Amazon Redshift is pitched as a more affordable alternative to Oracle Database. As part of Amazon Web Services, it integrates well with their other services, but it can only be used as part of the AWS cloud offerings.
• Domo: Domo combines the capabilities of a data warehouse like Oracle or Amazon Redshift with a functionality similar to Microsoft Power BI. It is used by organizations that want to allow many employees to gain access to a data warehouse.

Example of Data Analysis at Work

Putting together all the pieces of the data analysis puzzle might seem complex, but the time and resources required are worth the gains, says Pentti Tofte, vice president and head of analytics at the property insurer FM Global.

FM’s goal is not just to set insurance rates, but also to help customers reduce them, Tofte says. His inspectors visit more than 100,000 properties every year and record more than 700 pieces of data. Combining that information with data related to risks like fires and hurricanes, FM can then provide recommendations to the companies it insures.

“We believe most loss is preventable,” Tofte says. “We use data to tell them what losses to expect where and which vulnerabilities to prioritize.”

How Does Data Analysis Relate to Other Data and Business Functions?

Data analysis exists as a continuum of techniques, three of the most common being data analytics, data science and data mining.

Data Analysis vs. Data Analytics

Some people use these terms interchangeably. Data analysis also is often considered to be a subset of data analytics. Generally, data analytics covers a forward-looking outlook, or predicting future actions or results.

Data Analysis vs. Data Science

Data science takes analysis a step further by applying techniques from computer science to generate complex models that take into account large numbers of variables with complex (and sometimes poorly understood) interrelationships.

Data Analysis vs. Data Mining

Data mining goes even deeper by automating the process of discovery. Software is developed to find relationships and build models from extremely large datasets. Data mining is extremely powerful, but the resulting models require extensive evaluation to ensure they are valid.

How to Sharpen Your Data Analysis Skills

So you want to learn more about data analysis, but where to start? There is no right answer for everyone. And with such a large topic, don’t expect shortcuts. Here are a few places to get started.

If you never took a statistics class, it’s time to read The Cartoon Guide to Statistics . While it’s no replacement for a semester-long class, it’s more than enough to get you started.

Speaking of classes, there are some very good options for free online. Coursera , Udacity and Khan Academy offer relevant classes for free, although some features may require a paid upgrade. As you get more advanced, you can access a library of great tutorials at KDNuggets .

To get started right now, check out YouTube, where you will find a nearly never-ending collection of videos on data analysis. I highly recommend tuning in to The Ohio State University professor and Nobel Fellow Bear Braumoeller’s online lectures that address data literacy and visualization.

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5 reasons why everybody should learn data analytics

Could data analytics be the new coding? We certainly think so and in this article we'll look at five reasons why data analytics is a great skill to learn.

There's no doubt about it - analytics isn't just the way of the future, it's the way of right now! Having been adopted in all sorts of different industries, you'll now find analytics being used everywhere from aviation route planning through to predictive maintenance analysis in manufacturing plants. Even industries such as retail that you might not associate with big data are getting on board, utilising analytics to improve customer loyalty and tailor unique offerings.

With such a boom in the use of analytics, having the skills required to work with data isn't just valuable - it's all but a necessity. The importance of these skills is only going to become more important in the future as more industries and businesses jump onto the bandwagon, which is why we're now seeing such a focus on data analytics during higher education . Here at SAS, we believe everybody should have the chance to learn data analytics while studying, and in this article we'll look at five reasons why.

At heart, analytics is all about solving problems.

1. Gain problem solving skills

At heart, analytics is all about solving problems. The problems just happen to be on a much larger scale than what many of us are used to - effecting entire businesses, along with the staff and customers that they serve. The ability to think analytically and approach problems in the right way is a skill that's always useful, not just in the professional world, but in everyday life as well. Venture Beat explains the value of deductive reasoning skills simply, explaining that:

"Being able to look at various pieces of data and draw a conclusion is probably the most valuable skill for any employee to have, and surprisingly it's something that's too often missing from otherwise technically advanced employees."

2. High demand

This is the obvious benefit to learning data analytics, and the one most often focused on by students in higher education . Put simply, data analysts are valuable, and with a looming skills shortage on the horizon as more and more businesses and sectors start working with big data, this value is only going to increase. In practical terms, this means graduates with analytics skills will be able to command higher salaries and enjoy their pick of the available jobs.

3. Analytics is everywhere

Aside from the financial benefits that the high demand for data analytics can provide graduates, the big data boom has also meant that there are all sorts of new opportunities cropping up for talented employees. This could be working in a variety of different industries such as aviation or government, or simply having the opportunity to travel the world. With so many organisations looking to capitalise on data to improve their processes, it's a hugely exciting time to start a career in analytics.

The opportunity to leverage insight from data has never been greater.

4. It's only becoming more important

As we've touched on, now is something of a boom time in the world of analytics. With the abundance of data available at our fingertips today, the opportunity to leverage insight from that data has never been greater. This will have a few impacts but primarily the value of data analysts will go up, creating even better job opportunities and career progression options. This makes now the perfect time to start a journey into the world of big data analytics, with many education experts pushing the topic's importance as so vital that it should be taught in secondary schools as well as higher education institutions. 

This is similar to what we've seen surrounding coding in recent years, and while it may be a few years before we see data analytics as a common school subject, there's no denying how critical the discipline is likely to become in the very near future. 

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5. A range of related skills

The great thing about being an analytics specialist is that the field encompasses so much more than simply knowing how to work with data and solve problems. Yes, those are undoubtedly crucial elements, but data analysts also need to know how to communicate complex information to those without expertise. These communications skills are a vital part of any career, and with the added benefit of being a central part of an organisation's decision-making processes, analytics experts often pick up strong leadership skills as well. 

Ultimately, there really isn't any doubt that analytics is going to be a huge element of enterprises in the future. Getting ahead of the curve by learning analytics now provides a pathway to success, as well as transferrable skills that can help in every facet of life.

Here at SAS, we have a range of services such as SAS Analytics U that are designed to make learning how to work with data easier. Get in touch with us today to find out more. 

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5 key reasons why data analytics is important to business

Data analytics is the process of storing, organizing, and analyzing raw data to answer questions or gain important insights. Data analytics is integral to business because it allows leadership to create evidence-based strategy, understand customers to better target marketing initiatives, and increase overall productivity. Companies that take advantage of data analytics reap a competitive advantage because they are able to make faster changes that increase revenue, lower costs, and spur innovation.

In today’s digital world, the ability to make data-driven decisions and create strategy informed by analysis is central to successful leadership in any industry. The Certificate in Data Analytics at Penn LPS Online was created to help you enhance your data literacy and increase your professional opportunities. This Ivy League certificate is not designed to train you to become a data scientist but rather to provide a strong foundation in data analysis techniques that may be utilized in a variety of career paths. Possibilities include business analyst, policy analyst, market researcher, digital marketer, and quality assurance professional.

Read on to explore five key benefits of making data analytics a priority in business.

1. Gain greater insight into target markets

When businesses have access to the digital footprints of their customers they can learn invaluable knowledge about their preferences, their needs, and their browsing and purchasing behavior. Analyzing data collected from targeted markets can also help companies more swiftly identify trends and patterns and then customize products or services to meet these needs. The more an organization knows about who its customers are and what they want, the better it will be able to grow the customers’ loyalty, ensure they are happy, and boost sales. If leaders don’t take notice, they run the risk of losing their consumer base to a competitor who does.

Whether you’re seeking an entry-level or leadership role, it’s increasingly apparent that to be successful in today’s job market, it is critical that you are able to analyze data and communicate the findings in a way that is easily understood. DATA 1010: Introduction to Data Analytics at Penn LPS Online introduces you to important concepts in data analytics across a wide range of applications using the programming language R. You’ll complete this course with a clear understanding of how to use quantitative data to identify problems in real-time, make decisions, and create solutions.

2. Enhance decision-making capabilities

Data analytics also gives companies the power to make faster, better-informed business decisions—and avoid spending money on ineffective strategies, inefficient operations, misguided marketing campaigns, or unproven concepts for new products and services. By using a data-driven decision-making model, leaders also set up their organizations to be more proactive in identifying opportunities because they can be guided by the validity of data rather than simple intuition or industry experience. However, it is also important that decision-makers understand that although data may show a certain pattern or suggest an outcome, a flaw in the analysis or collection process could potentially render it inaccurate or misleading.

Once you’ve completed the introductory course in data analytics, the next logical step is to enroll in DATA 2100: Intermediate Data Analytics . In this course, you will learn two fundamental skills: survey and experimental research. You’ll be trained in every step of the survey research process, including how to design good survey questionnaires, draw samples, weigh data, and evaluate the responses. By the end of this flexible online class, you’ll understand how to develop and analyze a randomized experiment and build upon your skills in R programming.

 3. Create targeted strategies and marketing campaigns

Businesses can also use data to inform their strategies and drive targeted marketing campaigns to help ensure promotions engage the right audiences. By analyzing customer trends, monitoring online shopping, and evaluating point-of-sale transactional data, marketers can create customized advertising to reach new or evolving consumer segments and increase the efficiency of overall marketing efforts. And by taking advantage of these insights on consumer behavior and desires in customer-oriented marketing, businesses can meet and exceed expectations, boost brand loyalty, and encourage growth.

If you are interested in developing targeted marketing or advertising campaigns, it’s critical you understand the process by which quantitative social science and data science research is conducted. And that’s where DATA 3100: Introduction to Statistical Methods at Penn LPS Online comes in. This course comprises three complementary tracks. In the first, you’ll learn the basic tools necessary to perform social science research including descriptive statistics, sampling, probability, and statistical theory. In the second, you’ll discover how to implement these basic tools using R. And in the third, you’ll study the fundamentals of research design, including independent and dependent variables, producing testable hypotheses, and issues in causality.

4. Improve operational inefficiencies and minimize risk

Another major benefit to data analytics is the ability to use insights to increase operational efficiencies. By collecting large amounts of customer data and feedback, businesses can deduce meaningful patterns to optimize their products and services. Data analytics can also help organizations identify opportunities to streamline operations, reduce costs, or maximize profits. Companies can use insights from data analytics to quickly determine which operations lead to the best results—and which areas are underperforming. This allows decision-makers to adjust their strategies accordingly and proactively anticipate problems, manage risks, and make improvements.

Predictive modeling of data is one of the most sought-after skills in data science because it can help companies strategize future investments, nonprofits organize fundraising drives, or political candidates decide where to focus their canvassing efforts. DATA 4010: Advanced Topics in Data Analytics at Penn LPS Online starts with a comprehensive discussion on basic regression analysis and progresses to more advanced topics in R, such as mapping, textual analysis, web scraping, and working with string variables. You will also learn about more advanced data visualization skills in the class, including how to create interactive data visualizations in an R tool called Shiny.

5. Identify new product and service opportunities

When it comes to innovation, data analytics allows businesses to understand their current target audience, anticipate and identify product or service gaps, and develop new offerings to meet these needs. Not only can companies use data to track customer feedback and product performance in real-time, they can also track what rivals are doing so they can remain more competitive. Insights from data analytics can also allow organizations to update their existing products or services to reflect changing consumer demands, tweak marketing techniques, and optimize customer services. The enhanced adaptability afforded by big data can mean the difference between thriving or failing as a business.

"The Certificate in Data Analytics taught me how to clean, organize, and analyze data in R with just a few lines of code, which is so much faster than the processes I had been using in Excel. The course content is really well done, and the instructors are excellent. The weekly synchronous sessions kept me on track and helped me master new material and reinforce concepts from previous weeks. These skills will save me a lot of time in my job, and now I feel equipped to keep learning about these topics independently." - Susan Hassett , Enrollment Systems Analyst, College of Liberal and Professional Studies, University of Pennsylvania

Ready to enhance your data literacy?

The Certificate in Data Analytics at Penn LPS Online is a 4-course program designed to provide you with a point of entry to gain expertise in the field of data analytics. With flexible scheduling options and no required commute, you can develop your data literacy skills without sacrificing time dedicated to personal and professional responsibilities. The data analytics courses are taught by experienced practitioners, including members of the faculty from the Penn Program on Opinion Research and Election Studies. And the only prerequisites to succeeding in this credential are basic math skills, familiarity with using a computer, and an eagerness to expand your knowledge.

The Certificate in Data Analytics prepares you to:

• Implement and interpret basic regression models
• Understand advanced predictive modeling and machine learning
• Activate and analyze surveys
• Create experiments and A/B tests to evaluate solutions
• Learn skills in statistical programming and data analysis in R
• Manage and analyze big data sets

Whether you’re looking to immerse yourself in a personal area of interest or upgrade your skills to advance your career, the courses , certificates and undergraduate degree at Penn LPS Online are designed to fit your intellectual and professional goals. Applications and enrollment are open year-round. If you haven’t already, complete your enrollment and take the first step toward building your competency in data analytics today.

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Perspectives

Why data analytics matters to accountants.

“What do the numbers tell us?”

“Let’s dig into the data!”

“Can we analyze this in real-time?”

It’s very likely that you’ve heard these expressions around the office. Big data. Data analytics. Data science. This is important stuff. >> Are you data savvy? It’s a key component of your Business IQ. Test your IQ here”.

But why is this important? And what does it have to do with accounting?

Accountants use data analytics to help businesses uncover valuable insights within their financials, identify process improvements that can increase efficiency, and better manage risk. “Accountants will be increasingly expected to add value to the business decision making within their organizations and for their clients,” comments Associate Professor Wendell Gilland, who teaches Data Analytics for Accountants at UNC Kenan-Flagler Business School. “A strong facility with data analytics gives them the toolset to help strengthen their partnership with business leaders.”

Here are a few examples:

Auditors, both those working internally and externally, can shift from a sample-based model to employ continuous monitoring where much larger data sets are analyzed and verified. The result: less margin of error resulting in more precise recommendations.

Tax accountants use data science to quickly analyze complex taxation questions related to investment scenarios. In turn, investment decisions can be expedited, which allows companies to respond faster to opportunities to beat their competition — and the market — to the punch.

Accountants who assist, or act as, investment advisors use big data to find behavioral patterns in consumers and the market. These patterns can help businesses build analytic models that, in turn, help them identify investment opportunities and generate higher profit margins.

Four types of data analytics

To get a better handle on big data, it’s important to understand four key types of data analytics.

1. Descriptive analytics = “What is happening?” This is used most often and includes the categorization and classification of information. Accountants report on the flow of money through their organizations: revenue and expenses, inventory counts, sales tax collected. Accurate reporting is a hallmark of solid accounting practices. Compiling and verifying large amounts of data is important to this accurate reporting.

2. Diagnostic analytics = “Why did it happen?” Diagnostics are used to monitor changes in data. Accountants regularly analyze variances and calculate historical performance. Because historical precedent is often an excellent indicator of future performance, these calculations are critical to build reasonable forecasts.

3. Predictive analytics = “What’s going to happen?” Here, data is used to assess the likelihood of future outcomes. Accountants are instrumental in building forecasts and identifying patterns that shape those forecasts. When accountants act as trusted advisors and build forecasts, business leaders grow increasingly confident in following them.

Tangible actions — and critical business decisions — arise from prescriptive analytics. Accountants use the forecasts they create to make recommendations for future growth opportunities or, in some cases, raise an alert on poor choices.

4. Prescriptive analytics = “What should happen?” Tangible actions — and critical business decisions — arise from prescriptive analytics. Accountants use the forecasts they create to make recommendations for future growth opportunities or, in some cases, raise an alert on poor choices. This insight is an example of the significant impact that accountants make in the business world.

Why accountants make excellent data scientists

Accountants have outstanding technical skills. Gilland notes, “Accountants are used to aggregating information to create a picture of an organization that summarizes the details contained in each transaction. Working with descriptive analytics, predictive analytics, and prescriptive analytics comes more easily to people who already possess excellent quantitative skills.”

Accountants are natural-born problem solvers. The jump from descriptive and diagnostic analytics to predictive and prescriptive analytics requires that one shift from an organizational mindset to an inquisitive mindset; a shift from stacking and sorting information to figuring out how to use that information to make key business decisions. Accountants are experts at making this jump.

Accountants see the larger context and business implications. The true value of data analysis comes not at the point when the data is compiled, but rather when decisions are made using insights derived from the data. To uncover these insights, a data scientist must first understand the business context. Not only do accountants understand this context, they live it.

How can you become more data savvy?

Build your skills. A Master of Accounting degree from the University of North Carolina will significantly expand your knowledge of data analytics. The topic headlines one of our key courses. And, perhaps more importantly, data analytics is infused into many classes across our curriculum so that you can acquire this critical training in context with many other key topics.

Interested in data analytics? Here are a few things to try :

>> Download “What would the accountant say?” Solve a common business problem through the lens of an accounting data scientist.

>> Take our Business IQ quiz. Use our self-evaluation tool that measures numerous aspects of your business savvy, including, of course, your penchant for data and your analytics mindset.

Take the Business IQ Test now

What’s your next career move? The #1-ranked  online Master of Accounting (MAC) degree  from UNC Kenan-Flagler Business School can give your career the boost it needs, with a flexible schedule, world-class faculty, and a career services team dedicated to the needs of working professionals.

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6 Reasons Why You Should Study Data Analytics

In this age of information, the internet has made it easy for anyone to gain information whenever and wherever they need. Here's why a career in Data Analytics is the next big thing.

If you want to succeed in this digital world that is creating a knowledge-based society, you must study trends. Right from MNC’s to start-ups, everyone depends on data to formulate improved strategies for the future of their companies. 

Now picture yourself as the person these companies depend on before they make any big business decision. That is precisely why you should study data science and big data analytics.  Reasons why you should study data analytics

1. Data analytics is significant for top organisations

The outburst of data is transforming businesses. Companies - big or small - are now expecting their business decisions to be based on data-led insight. Data specialists have a tremendous impact on business strategies and marketing tactics. 

2. Job opportunities on the rise

The Science and Technology Committee published this report and noted that 58,000 jobs could be created and £216bn contributed to our economy (2.3% of GDP) by 2021. 

The demand for data specialists is on the rise while the supply remains low, thus creating great job opportunities for individuals within this field. 

Today, it is almost impossible to find any brand that does not have social media presence; soon, every company will need data analytics professionals. This makes it a wise career move that has a future in business. 

3. Increasing salaries for data analytics professionals

According to Prospects, entry level salaries for data analytics professionals range between £24,000 and £25,000. With a few years' experience, salaries can rise to somewhere between £30,000 and £35,000, while high-level professionals and consultants may earn £60,000 or more.

4. Work opportunities in a spectrum of industries 

Many industries are reliant on data, so you could opt for a career in any number of industries, including:

• business intelligence
• data assurance
• data quality
• higher education

5. You will influence the decision-making in the company

While most company employees feel the lack of decision-making power causing job dissatisfaction, that’s not the case for data professionals. 

With a unique role within the company, you will be a vital part of business policies and future strategies thus making it a very rewarding career.

6. It presents perfect freelancing opportunities

Data analytics is also a prospect to become a well-paid consultant for some of the major firms in the world. As the job is mainly IT based, with a good internet connection, it can be done from any part of the world. This gives you the perfect opportunity to broaden your sources of income and provide yourself with a good work-life balance. 

Browse our data analytics courses to become a vital professional of one of the biggest in-demand careers. 

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Why Become a Data Analyst?

Been thinking about entering the world of data analytics? There’s no better time to get started! The Harvard Business Review named the roles of data analyst and data scientist as “the sexiest job of the 21st century.” The data analyst role was also included in Career Karma’s top tech jobs for the past three years running .

In this article, we’ll talk about why becoming a data analyst is a great career choice, outline the tasks and responsibilities a data analyst has, and give a roadmap of how to continue on this exciting career path.

If you want try it out immediately, check out CareerFoundry’s free 5-day data analytics course to start out.

We’ll cover the following topics:

• Why become a data analyst?
• What does a data analyst actually do?
• How to become a data analyst
• How long does it take to become a data analyst?

Feel free to use the clickable menu to skip ahead to any section. Now, let’s get started!

1. Why become a data analyst?

When considering becoming a data analyst, you might first think about datasets, visualizations, and the like—but there’s a lot more to being a data analyst than just that! In this section, we’ll go into some of the top reasons you may want to go into the field of data. Let’s begin with…

Data analysts love solving problems 

Data analytics is a fast-paced, challenging career centered on problem-solving and thinking outside the box.

As a data analyst, you’ll work with a number of different teams who require your skills and knowledge to provide them with insights into how they can improve their processes.

Data analysts come from many different backgrounds

While many data analysts come from a more analytical or technical background, anyone can become a data analyst if they apply themselves.CareerFoundry, for example, has many successful graduates who used to work in marketing, sales, teaching, customer service, finance, architecture, HR, and IT roles.

Data analysts are in high demand

Employers struggle to find qualified data analysts, and the demand keeps growing. The average junior data analyst salary in the United States is $59,679 per year, while senior data analysts can earn as much as $108,000, according to PayScale !

Data analysts are constantly evolving

Data analysis moves quickly, and data analysts are constantly learning and advancing in their careers. There is practically no limit to how much you can improve your skills and progress in your career as a data analyst.  

Data analysts can work in many types of companies

As a data analyst, you have the opportunity to work for startups, agencies, large corporations, or even freelance . Your skills can be utilized by all kinds of businesses who want to understand their processes, customers, and business more. 

Data analysts are shaping the future

Almost all companies are collecting data on their customers, and correctly knowing how to interpret such data is becoming of increasing importance. Data analysts define how a business is currently operating. It’s up to them to look for changes, identify patterns, and spot anomalies that give an indication of how a company or organization is performing. 

… so there you have it! 

Working with data analysis is an in-demand, multifaceted, ever-evolving field, well-suited to those who are keen to seek out and solve problems and help inform important decisions using data. 

In the next section, we’ll look at what data analysts do on a day-to-day basis.

2. What does a data analyst actually do?

In a sentence: A data analyst is responsible for turning raw data into actionable insights that help drive business decisions. 

In a paragraph: Data analysts analyze and extrapolate information from raw data to answer business-specific questions, which may include: “Why did we miss on our revenue goals last quarter?” or “What will be the growth of our smallest customer segment this year?” 

They’ll make use of the data analysis process , adapted to the type of analysis they are performing. 

Once they’ve completed their analysis, they’ll present it to the relevant stakeholders in the form of a data visualization (normally a chart or graph on a dashboard). Based on these visualizations, the analyst can then make recommendations on appropriate next steps for the business.

To learn more about what a data analyst’s tasks and responsibilities are, check out our complete guide: What does a data analyst actually do?

If that’s piqued your interest, read on to learn more about how to become a data analyst.

3. How to become a data analyst

You may think that working with data requires some pretty strict certifications. Well, think again!

While you may find that possessing a degree in computer science, economics, or mathematics will give you bricks-and-mortar qualifications, not everyone has the time, patience, or budget for a four-year degree—and they’re not totally necessary, either!

A useful (and fun) way to try and work out your own path in the industry is to take the data careers quiz we’ve created. It should give you a better idea of where to specialize.

In this section, we’ll outline the basic steps to take when looking to start out as a data analyst. For a more detailed roadmap, check out how to become a data analyst .

Step 1: Gain an understanding of data analytics fundamentals

When we talk about the fundamentals of data analysis, what we mean here is getting an understanding of the key principles and tools used within the discipline. With that in mind, you may find the following articles helpful: 

• Data analytics for beginners
• The best data analytics tools for data analysts
• What is Python? A guide to the fastest-growing programming language

You may also find it useful to take on a free data analytics course , read blogs written by industry experts , or read books on the topic. The world is your data-filled oyster!

Step 2: Obtain a data analytics certification

We’ve just touched upon data analytics degrees, and if that’s the path you choose to go down—great!

However, tech bootcamps have been rising in popularity over the years due to their accessibility, flexibility, networking opportunities, and more options generally not available from sandstone institutions. These allow you to become a fully-qualified data analyst in anywhere from three months to a year, usually while still continuing with your current day job

Of course, each bootcamp provider will offer more or fewer opportunities that will benefit you personally depending on their approach, so it’s best to do your research. Learn more about data analytics bootcamps in this comprehensive guide to the market’s most highly-sought after courses .

Step 3: Create (and update) your data analytics portfolio

A crucial step to take before getting into the job market as a data analyst is creating your data analytics portfolio, which is a repository of your best projects to show prospective employers or colleagues.

Basically, it’s your place online to show off your skills! Many data analytics courses will include capstone projects specifically designed to include as part of your portfolio, but you should also look into passion projects to use in order to show your keenness on the topic.

Be sure to check out our complete guide on how to create a data analytics portfolio , as well as some of the best data analytics portfolios from which to draw inspiration.

Step 4: Apply for jobs and get networking!

Now, this might seem as straightforward as writing your resume and cover letter and answering interview questions , but there’s definitely way more to it than that if you want a truly enriching career as a data analyst.

Networking is also very important—data analysts love to share information, whether it’s about a snippet of code or a promising upcoming project or workplace. Get to know other data analysts on LinkedIn , GitHub , or Medium , and you’ll gain priceless insights into your new world.

Similarly, if your bootcamp provider or university offers opportunities to work with a career coach , take them! While you may have applied for jobs on your own before, a career coach will have a great overview of the industry, and may even have personal insights into roles or traineeship opportunities that won’t be listed online.

4. How long does it take to become a data analyst?

So, you’re coming to the end of the article, and you’re getting excited about the prospect of becoming a data analyst, but there’s one thing on your mind: how long will it take to get there?

Unfortunately, there’s no clear-cut answer to that question. For example, it can take one person anywhere from three months to a year to complete a data analytics bootcamp (potentially longer if also working, and shorter if not).

For those studying at a university, three- and four-year degrees are the most common, but you could also take on a data analytics graduate certificate if you’ve already graduated with a bachelor’s, which would only add on an extra six months to a year. 

Once you’ve finished studying, the journey from graduation to employment is as long as a piece of string—you could get one after your first application if you’re really lucky!

The important thing to keep in mind is that you should study at your own pace and ensure you’re making the most of your time with whatever provider or institution you enroll with. When it comes to the job hunt, research organizations carefully and make sure you’re happy with what they can offer you. Data analytics is a field that can truly take you anywhere, so if it’s possible, it helps to be open to relocation, too!

Bear in mind that data analyst jobs are expected to rise in demand at least until 2031 (according to the U.S. Bureau of Labor Statistics ), so as the old saying goes: if at first you don’t succeed, try again—there are plenty more jobs out there!

5. Next steps

In this article we’ve answered the question: Why become a data analyst? If you’re looking for a career change, data analytics is a vibrant path to take with ever-increasing opportunities available.

With such demand on the market, our advice to aspiring analysts is not to rush into the field but instead to take the time to learn new skills, apply them to projects you’re passionate about, build up a portfolio, and apply for jobs with companies you care about.

If you’re keen to get started, why not try out this free data analytics short course ? It’ll teach you the absolute fundamentals so that you can see whether you enjoy data or not. Pass the short quiz at the end of the course, and you’ll be rewarded with a discount you can put towards the tuition for the full CareerFoundry Data Analytics Program .

You may also find the following articles interesting:

• What is data analytics?
• What does a data analyst do?

5 Reasons Why Data Analytics is Important in Problem Solving

Data analytics  is important in problem solving and it is a key sub-branch of data science. Even though there are endless data analytics applications in a business, one of the most crucial roles it plays is problem-solving. 

Using data analytics not only boosts your problem-solving skills, but it also makes them a whole lot faster and efficient, automating a majority of the long and repetitive processes.

Whether you’re fresh out of university graduate or a professional who works for an organization, having top-notch  problem-solving skills  is a necessity and always comes in handy. 

Everybody keeps facing new kinds of complex problems every day, and a lot of time is invested in overcoming these obstacles. Moreover, much valuable time is lost while trying to find solutions to unexpected problems, and your plans also get disrupted often.

This is where data analytics comes in. It lets you find and analyze the relevant data without too much of human-support. It’s a real time-saver and has become a necessity in problem-solving nowadays. So if you don’t already use data analytics in solving these problems, you’re probably missing out on a lot!

As the saying goes from the chief analytics officer of TIBCO, 

“Think analytically, rigorously, and systematically about a  business problem  and come up with a  solution that leverages the available data .”  

– Michael O’Connell.

In this article, I will explain the importance of data analytics in problem-solving and go through the top 5 reasons why it cannot be ignored. So, let’s dive into it right away.

Highly Recommended Articles:

13 Reasons Why Data Analytics is Important in Decision Making

This is Why Business Analytics is Vital in Every Business

Is Data Analysis Qualitative or Quantitative? (We find Out!)

Will Algorithms Erode our Decision-Making Skills?

What is Data Analytics?

Data analytics is the art of automating processes using algorithms to collect raw data from multiple sources and transform it. This results in achieving the data that’s ready to be studied and used for analytical purposes, such as finding the trends, patterns, and so forth.

Why is Data Analytics Important in Problem Solving?

Problem-solving and data analytics often proceed hand in hand. When a particular problem is faced, everybody’s first instinct is to look for supporting data. Data analytics plays a pivotal role in finding this data and analyzing it to be used for tackling that specific problem.

Although the analytical part sometimes adds further complexities, since it’s a whole different process that might get  challenging  sometimes, it eventually helps you get a better hold of the situation. 

Also, you come up with a more informed solution, not leaving anything out of the equation.

Having strong analytical skills help you dig deeper into the problem and get all the insights you need. Once you have extracted enough relevant knowledge, you can proceed with solving the problem. 

However, you need to make sure you’re using the  right, and complete  data, or using data analytics may even backfire for you. Misleading data can make you believe things that don’t exist, and that’s bound to take you off the track, making the problem appear more complex or simpler than it is.

Let’s see a very straightforward daily life example to examine the importance of data analytics in problem-solving; what would you do if a question appears on your exam, but it doesn’t have enough data provided for you to solve the question? 

Obviously, you won’t be able to solve that problem. You need a certain level of facts and figures about the situation first, or you’ll be wandering in the dark.

However, once you get the information you need, you can analyze the situation and quickly develop a solution. Moreover, getting more and more knowledge of the situation will further ease your ability to solve the given problem. This is precisely how data analytics assists you. It eases the process of collecting information and processing it to solve real-life problems.

5 Reasons Why Data Analytics Is Important in Problem Solving

Now that we’ve established a general idea of how strongly connected analytical skills and problem-solving are, let’s dig deeper into the top 5 reasons  why data analytics is important in problem-solving .

1. Uncover Hidden Details

Data analytics is great at putting the minor details out in the spotlight. Sometimes, even the most qualified data scientists might not be able to spot tiny details existing in the data used to solve a certain problem. However, computers don’t miss. This enhances your ability to solve problems, and you might be able to come up with solutions a lot quicker.

Data analytics tools have a wide variety of features that let you study the given data very thoroughly and catch any hidden or recurring trends using built-in features without needing any effort. These tools are entirely automated and require very little programming support to work. They’re great at excavating the depths of data, going back way into the past.

2. Automated Models

Automation is the future. Businesses don’t have enough time nor the budget to let manual workforces go through tons of data to solve business problems. 

Instead, what they do is hire a data analyst who automates problem-solving processes, and once that’s done, problem-solving becomes completely independent of any human intervention.

The tools can collect, combine, clean, and transform the relevant data all by themselves and finally using it to predict the solutions. Pretty impressive, right? 

However, there might be some complex problems appearing now and then, which cannot be handled by algorithms since they’re completely new and nothing similar has come up before. But a lot of the work is still done using the algorithms, and it’s only once in a blue moon that they face something that rare.

However, there’s one thing to note here; the process of automation by designing complex analytical and  ML algorithms  might initially be a bit challenging. Many factors need to be kept in mind, and a lot of different scenarios may occur. But once it goes up and running, you’ll be saving a significant amount of manpower as well as resources.

3. Explore Similar Problems

If you’re using a data analytics approach for solving your problems, you will have a lot of data available at your disposal. Most of the data would indirectly help you in the form of similar problems, and you only have to figure out how these problems are related. 

Once you’re there, the process gets a lot smoother because you get references to how such problems were tackled in the past.

Such data is available all over the internet and is automatically extracted by the data analytics tools according to the current problems. People run into difficulties all over the world, and there’s no harm if you follow the guidelines of someone who has gone through a similar situation before.

Even though exploring similar problems is also possible without the help of data analytics, we’re generating a lot of data  nowadays , and searching through tons of this data isn’t as easy as you might think. So, using analytical tools is the smart choice since they’re quite fast and will save a lot of your time.

4. Predict Future Problems

While we have already gone through the fact that data analytics tools let you analyze the data available from the past and use it to predict the solutions to the problems you’re facing in the present, it also goes the other way around.

Whenever you use data analytics to solve any present problem, the tools you’re using store the data related to the problem and saves it in the form of variables forever. This way, similar problems faced in the future don’t need to be analyzed again. Instead, you can reuse the previous solutions you have, or the algorithms can predict the solutions for you even if the problems have evolved a bit.

This way, you’re not wasting any time on the problems that are recurring in nature. You jump directly onto the solution whenever you face a situation, and this makes the job quite simple.

5. Faster Data Extraction

However, with the latest tools, the  data extraction  is greatly reduced, and everything is done automatically with no human intervention whatsoever. 

Moreover, once the appropriate data is mined and cleaned, there are not many hurdles that remain, and the rest of the processes are done without a lot of delays.

When businesses come across a problem, around  70%-80%  is their time is consumed while gathering the relevant data and transforming it into usable forms. So, you can estimate how quick the process could get if the data analytics tools automate all this process.

Even though many of the tools are open-source, if you’re a bigger organization that can spend a bit on paid tools, problem-solving could get even better. The paid  tools  are literal workhorses, and in addition to generating the data, they could also develop the models to your solutions, unless it’s a very complex one, without needing any support of data analysts.

What problems can data analytics solve? 3 Real-World Examples

Employee performance problems .

Imagine a Call Center with over 100 agents

By Analyzing data sets of employee attendance, productivity, and issues that tend to delay in resolution. Through that, preparing refresher training plans, and mentorship plans according to key weak areas identified.

Sales Efficiency Problems 

Imagine a Business that is spread out across multiple cities or regions

By analyzing the number of sales per area, the size of the sales reps’ team, the overall income and disposable income of potential customers, you can come up with interesting insights as to why some areas sell more or less than the others. Through that, prepping a recruitment and training plan or area expansion in order to boost sales could be a good move.

Business Investment Decisions Problems

Imagine an Investor with a portfolio of apps/software)

By analyzing the number of subscribers, sales, the trends in usage, the demographics, you can decide which peace of software has a better Return on Investment over the long term.

Throughout the article, we’ve seen various reasons why data analytics is very important for problem-solving. 

Many different problems that may seem very complex in the start are made seamless using data analytics, and there are hundreds of analytical tools that can help us solve problems in our everyday lives.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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Developing Deeper Analysis & Insights

Analysis is a central writing skill in academic writing. Essentially, analysis is what writers do with evidence to make meaning of it. While there are specific disciplinary types of analysis (e.g., rhetorical, discourse, close reading, etc.), most analysis involves zooming into evidence to understand how the specific parts work and how their specific function might relate to a larger whole. That is, we usually need to zoom into the details and then reflect on the larger picture. In this writing guide, we cover analysis basics briefly and then offer some strategies for deepening your analysis. Deepening your analysis means pushing your thinking further, developing a more insightful and interesting answer to the “so what?” question, and elevating your writing.

Analysis Basics

Questions to Ask of the Text:

• Is the evidence fully explained and contextualized? Where in the text/story does this evidence come from (briefly)? What do you think the literal meaning of the quote/evidence is and why? Why did you select this particular evidence?
• Are you selecting a long enough quote to work with and analyze? While over-quoting can be a problem, so too can under-quoting.
• Do you connect each piece of evidence explicitly to the claim or focus of the paper?

Strategies & Explanation

• Sometimes turning the focus of the paper into a question can really help someone to figure out how to work with evidence. All evidence should answer the question--the work of analysis is explaining how it answers the question.
• The goal of evidence in analytical writing is not just to prove that X exists or is true, but rather to show something interesting about it--to push ideas forward, to offer insights about a quote. To do this, sometimes having a full sentence for a quote helps--if a writer is only using single-word quotes, for example, they may struggle to make meaning out of it.

Deepening Analysis

Not all of these strategies work every time, but usually employing one of them is enough to really help elevate the ideas and intellectual work of a paper:

• Bring the very best point in each paragraph into the topic sentence. Often these sentences are at the very end of a paragraph in a solid draft. When you bring it to the front of the paragraph, you then need to read the paragraph with the new topic sentence and reflect on: what else can we say about this evidence? What else can it show us about your claim?
• Complicate the point by adding contrasting information, a different perspective, or by naming something that doesn’t fit. Often we’re taught that evidence needs to prove our thesis. But, richer ideas emerge from conflict, from difference, from complications. In a compare and contrast essay, this point is very easy to see--we get somewhere further when we consider how two things are different. In an analysis of a single text, we might look at a single piece of evidence and consider: how could this choice the writer made here be different? What other choices could the writer have made and why didn’t they? Sometimes naming what isn’t in the text can help emphasize the importance of a particular choice.
• Shift the focus question of the essay and ask the new question of each piece of evidence. For example, a student is looking at examples of language discrimination (their evidence) in order to make an argument that answers the question: what is language discrimination? Questions that are definitional (what is X? How does Y work? What is the problem here?) can make deeper analysis challenging. It’s tempting to simply say the equivalent of “Here is another example of language discrimination.” However, a strategy to help with this is to shift the question a little bit. So perhaps the paragraphs start by naming different instances of language discrimination, but the analysis then tackles questions like: what are the effects of language discrimination? Why is language discrimination so problematic in these cases? Who perpetuates language discrimination and how? In a paper like this, it’s unlikely you can answer all of those questions--but, selecting ONE shifted version of a question that each paragraph can answer, too, helps deepen the analysis and keeps the essay focused.
• Examine perspective--both the writer’s and those of others involved with the issue. You might reflect on your own perspectives as a unique audience/reader. For example, what is illuminated when you read this essay as an engineer? As a person of color? As a first-generation student at Cornell? As an economically privileged person? As a deeply religious Christian? In order to add perspective into the analysis, the writer has to name these perspectives with phrases like: As a religious undergraduate student, I understand X to mean… And then, try to explain how the specificity of your perspective illuminates a different reading or understanding of a term, point, or evidence. You can do this same move by reflecting on who the intended audience of a text is versus who else might be reading it--how does it affect different audiences differently? Might that be relevant to the analysis?
• Qualify claims and/or acknowledge limitations. Before college level writing and often in the media, there is a belief that qualifications and/or acknowledging the limitations of a point adds weakness to an argument. However, this actually adds depth, honesty, and nuance to ideas. It allows you to develop more thoughtful and more accurate ideas. The questions to ask to help foster this include: Is this always true? When is it not true? What else might complicate what you’ve said? Can we add nuance to this idea to make it more accurate? Qualifications involve words like: sometimes, may effect, often, in some cases, etc. These terms are not weak or to be avoided, they actually add accuracy and nuance.

A Link to a  PDF Handout of this Writing Guide 

Choose Your Test

Sat / act prep online guides and tips, 5 steps to write a great analytical essay.

General Education

Do you need to write an analytical essay for school? What sets this kind of essay apart from other types, and what must you include when you write your own analytical essay? In this guide, we break down the process of writing an analytical essay by explaining the key factors your essay needs to have, providing you with an outline to help you structure your essay, and analyzing a complete analytical essay example so you can see what a finished essay looks like.

What Is an Analytical Essay?

Before you begin writing an analytical essay, you must know what this type of essay is and what it includes. Analytical essays analyze something, often (but not always) a piece of writing or a film.

An analytical essay is more than just a synopsis of the issue though; in this type of essay you need to go beyond surface-level analysis and look at what the key arguments/points of this issue are and why. If you’re writing an analytical essay about a piece of writing, you’ll look into how the text was written and why the author chose to write it that way. Instead of summarizing, an analytical essay typically takes a narrower focus and looks at areas such as major themes in the work, how the author constructed and supported their argument, how the essay used literary devices to enhance its messages, etc.

While you certainly want people to agree with what you’ve written, unlike with persuasive and argumentative essays, your main purpose when writing an analytical essay isn’t to try to convert readers to your side of the issue. Therefore, you won’t be using strong persuasive language like you would in those essay types. Rather, your goal is to have enough analysis and examples that the strength of your argument is clear to readers.

Besides typical essay components like an introduction and conclusion, a good analytical essay will include:

• A thesis that states your main argument
• Analysis that relates back to your thesis and supports it
• Examples to support your analysis and allow a more in-depth look at the issue

In the rest of this article, we’ll explain how to include each of these in your analytical essay.

How to Structure Your Analytical Essay

Analytical essays are structured similarly to many other essays you’ve written, with an introduction (including a thesis), several body paragraphs, and a conclusion. Below is an outline you can follow when structuring your essay, and in the next section we go into more detail on how to write an analytical essay.

Introduction

Your introduction will begin with some sort of attention-grabbing sentence to get your audience interested, then you’ll give a few sentences setting up the topic so that readers have some context, and you’ll end with your thesis statement. Your introduction will include:

• Brief background information explaining the issue/text
• Your thesis

Body Paragraphs

Your analytical essay will typically have three or four body paragraphs, each covering a different point of analysis. Begin each body paragraph with a sentence that sets up the main point you’ll be discussing. Then you’ll give some analysis on that point, backing it up with evidence to support your claim. Continue analyzing and giving evidence for your analysis until you’re out of strong points for the topic. At the end of each body paragraph, you may choose to have a transition sentence that sets up what the next paragraph will be about, but this isn’t required. Body paragraphs will include:

• Introductory sentence explaining what you’ll cover in the paragraph (sort of like a mini-thesis)
• Analysis point
• Evidence (either passages from the text or data/facts) that supports the analysis
• (Repeat analysis and evidence until you run out of examples)

You won’t be making any new points in your conclusion; at this point you’re just reiterating key points you’ve already made and wrapping things up. Begin by rephrasing your thesis and summarizing the main points you made in the essay. Someone who reads just your conclusion should be able to come away with a basic idea of what your essay was about and how it was structured. After this, you may choose to make some final concluding thoughts, potentially by connecting your essay topic to larger issues to show why it’s important. A conclusion will include:

• Paraphrase of thesis
• Summary of key points of analysis
• Final concluding thought(s)

5 Steps for Writing an Analytical Essay

Follow these five tips to break down writing an analytical essay into manageable steps. By the end, you’ll have a fully-crafted analytical essay with both in-depth analysis and enough evidence to support your argument. All of these steps use the completed analytical essay in the next section as an example.

#1: Pick a Topic

You may have already had a topic assigned to you, and if that’s the case, you can skip this step. However, if you haven’t, or if the topic you’ve been assigned is broad enough that you still need to narrow it down, then you’ll need to decide on a topic for yourself. Choosing the right topic can mean the difference between an analytical essay that’s easy to research (and gets you a good grade) and one that takes hours just to find a few decent points to analyze

Before you decide on an analytical essay topic, do a bit of research to make sure you have enough examples to support your analysis. If you choose a topic that’s too narrow, you’ll struggle to find enough to write about.

For example, say your teacher assigns you to write an analytical essay about the theme in John Steinbeck’s The Grapes of Wrath of exposing injustices against migrants. For it to be an analytical essay, you can’t just recount the injustices characters in the book faced; that’s only a summary and doesn’t include analysis. You need to choose a topic that allows you to analyze the theme. One of the best ways to explore a theme is to analyze how the author made his/her argument. One example here is that Steinbeck used literary devices in the intercalary chapters (short chapters that didn’t relate to the plot or contain the main characters of the book) to show what life was like for migrants as a whole during the Dust Bowl.

You could write about how Steinbeck used literary devices throughout the whole book, but, in the essay below, I chose to just focus on the intercalary chapters since they gave me enough examples. Having a narrower focus will nearly always result in a tighter and more convincing essay (and can make compiling examples less overwhelming).

#2: Write a Thesis Statement

Your thesis statement is the most important sentence of your essay; a reader should be able to read just your thesis and understand what the entire essay is about and what you’ll be analyzing. When you begin writing, remember that each sentence in your analytical essay should relate back to your thesis

In the analytical essay example below, the thesis is the final sentence of the first paragraph (the traditional spot for it). The thesis is: “In The Grapes of Wrath’s intercalary chapters, John Steinbeck employs a variety of literary devices and stylistic choices to better expose the injustices committed against migrants in the 1930s.” So what will this essay analyze? How Steinbeck used literary devices in the intercalary chapters to show how rough migrants could have it. Crystal clear.

#3: Do Research to Find Your Main Points

This is where you determine the bulk of your analysis--the information that makes your essay an analytical essay. My preferred method is to list every idea that I can think of, then research each of those and use the three or four strongest ones for your essay. Weaker points may be those that don’t relate back to the thesis, that you don’t have much analysis to discuss, or that you can’t find good examples for. A good rule of thumb is to have one body paragraph per main point

This essay has four main points, each of which analyzes a different literary device Steinbeck uses to better illustrate how difficult life was for migrants during the Dust Bowl. The four literary devices and their impact on the book are:

• Lack of individual names in intercalary chapters to illustrate the scope of the problem
• Parallels to the Bible to induce sympathy for the migrants
• Non-showy, often grammatically-incorrect language so the migrants are more realistic and relatable to readers
• Nature-related metaphors to affect the mood of the writing and reflect the plight of the migrants

#4: Find Excerpts or Evidence to Support Your Analysis

Now that you have your main points, you need to back them up. If you’re writing a paper about a text or film, use passages/clips from it as your main source of evidence. If you’re writing about something else, your evidence can come from a variety of sources, such as surveys, experiments, quotes from knowledgeable sources etc. Any evidence that would work for a regular research paper works here.

In this example, I quoted multiple passages from The Grapes of Wrath  in each paragraph to support my argument. You should be able to back up every claim you make with evidence in order to have a strong essay.

#5: Put It All Together

Now it's time to begin writing your essay, if you haven’t already. Create an introductory paragraph that ends with the thesis, make a body paragraph for each of your main points, including both analysis and evidence to back up your claims, and wrap it all up with a conclusion that recaps your thesis and main points and potentially explains the big picture importance of the topic.

Analytical Essay Example + Analysis

So that you can see for yourself what a completed analytical essay looks like, here’s an essay I wrote back in my high school days. It’s followed by analysis of how I structured my essay, what its strengths are, and how it could be improved.

One way Steinbeck illustrates the connections all migrant people possessed and the struggles they faced is by refraining from using specific titles and names in his intercalary chapters. While The Grapes of Wrath focuses on the Joad family, the intercalary chapters show that all migrants share the same struggles and triumphs as the Joads. No individual names are used in these chapters; instead the people are referred to as part of a group. Steinbeck writes, “Frantic men pounded on the doors of the doctors; and the doctors were busy.  And sad men left word at country stores for the coroner to send a car,” (555). By using generic terms, Steinbeck shows how the migrants are all linked because they have gone through the same experiences. The grievances committed against one family were committed against thousands of other families; the abuse extends far beyond what the Joads experienced. The Grapes of Wrath frequently refers to the importance of coming together; how, when people connect with others their power and influence multiplies immensely. Throughout the novel, the goal of the migrants, the key to their triumph, has been to unite. While their plans are repeatedly frustrated by the government and police, Steinbeck’s intercalary chapters provide a way for the migrants to relate to one another because they have encountered the same experiences. Hundreds of thousands of migrants fled to the promised land of California, but Steinbeck was aware that numbers alone were impersonal and lacked the passion he desired to spread. Steinbeck created the intercalary chapters to show the massive numbers of people suffering, and he created the Joad family to evoke compassion from readers.  Because readers come to sympathize with the Joads, they become more sensitive to the struggles of migrants in general. However, John Steinbeck frequently made clear that the Joads were not an isolated incident; they were not unique. Their struggles and triumphs were part of something greater. Refraining from specific names in his intercalary chapters allows Steinbeck to show the vastness of the atrocities committed against migrants.

Steinbeck also creates significant parallels to the Bible in his intercalary chapters in order to enhance his writing and characters. By using simple sentences and stylized writing, Steinbeck evokes Biblical passages. The migrants despair, “No work till spring. No work,” (556).  Short, direct sentences help to better convey the desperateness of the migrants’ situation. Throughout his novel, John Steinbeck makes connections to the Bible through his characters and storyline. Jim Casy’s allusions to Christ and the cycle of drought and flooding are clear biblical references.  By choosing to relate The Grapes of Wrath to the Bible, Steinbeck’s characters become greater than themselves. Starving migrants become more than destitute vagrants; they are now the chosen people escaping to the promised land. When a forgotten man dies alone and unnoticed, it becomes a tragedy. Steinbeck writes, “If [the migrants] were shot at, they did not run, but splashed sullenly away; and if they were hit, they sank tiredly in the mud,” (556). Injustices committed against the migrants become greater because they are seen as children of God through Steinbeck’s choice of language. Referencing the Bible strengthens Steinbeck’s novel and purpose: to create understanding for the dispossessed.  It is easy for people to feel disdain for shabby vagabonds, but connecting them to such a fundamental aspect of Christianity induces sympathy from readers who might have otherwise disregarded the migrants as so many other people did.

The simple, uneducated dialogue Steinbeck employs also helps to create a more honest and meaningful representation of the migrants, and it makes the migrants more relatable to readers. Steinbeck chooses to accurately represent the language of the migrants in order to more clearly illustrate their lives and make them seem more like real paper than just characters in a book. The migrants lament, “They ain’t gonna be no kinda work for three months,” (555). There are multiple grammatical errors in that single sentence, but it vividly conveys the despair the migrants felt better than a technically perfect sentence would. The Grapes of Wrath is intended to show the severe difficulties facing the migrants so Steinbeck employs a clear, pragmatic style of writing.  Steinbeck shows the harsh, truthful realities of the migrants’ lives and he would be hypocritical if he chose to give the migrants a more refined voice and not portray them with all their shortcomings. The depiction of the migrants as imperfect through their language also makes them easier to relate to. Steinbeck’s primary audience was the middle class, the less affluent of society. Repeatedly in The Grapes of Wrath , the wealthy make it obvious that they scorn the plight of the migrants. The wealthy, not bad luck or natural disasters, were the prominent cause of the suffering of migrant families such as the Joads. Thus, Steinbeck turns to the less prosperous for support in his novel. When referring to the superior living conditions barnyard animals have, the migrants remark, “Them’s horses-we’re men,” (556).  The perfect simplicity of this quote expresses the absurdness of the migrants’ situation better than any flowery expression could.

In The Grapes of Wrath , John Steinbeck uses metaphors, particularly about nature, in order to illustrate the mood and the overall plight of migrants. Throughout most of the book, the land is described as dusty, barren, and dead. Towards the end, however; floods come and the landscape begins to change. At the end of chapter twenty-nine, Steinbeck describes a hill after the floods saying, “Tiny points of grass came through the earth, and in a few days the hills were pale green with the beginning year,” (556). This description offers a stark contrast from the earlier passages which were filled with despair and destruction. Steinbeck’s tone from the beginning of the chapter changes drastically. Early in the chapter, Steinbeck had used heavy imagery in order to convey the destruction caused by the rain, “The streams and the little rivers edged up to the bank sides and worked at willows and tree roots, bent the willows deep in the current, cut out the roots of cottonwoods and brought down the trees,” (553). However, at the end of the chapter the rain has caused new life to grow in California. The new grass becomes a metaphor representing hope. When the migrants are at a loss over how they will survive the winter, the grass offers reassurance. The story of the migrants in the intercalary chapters parallels that of the Joads. At the end of the novel, the family is breaking apart and has been forced to flee their home. However, both the book and final intercalary chapter end on a hopeful note after so much suffering has occurred. The grass metaphor strengthens Steinbeck’s message because it offers a tangible example of hope. Through his language Steinbeck’s themes become apparent at the end of the novel. Steinbeck affirms that persistence, even when problems appear insurmountable, leads to success. These metaphors help to strengthen Steinbeck’s themes in The Grapes of Wrath because they provide a more memorable way to recall important messages.

John Steinbeck’s language choices help to intensify his writing in his intercalary chapters and allow him to more clearly show how difficult life for migrants could be. Refraining from using specific names and terms allows Steinbeck to show that many thousands of migrants suffered through the same wrongs. Imitating the style of the Bible strengthens Steinbeck’s characters and connects them to the Bible, perhaps the most famous book in history. When Steinbeck writes in the imperfect dialogue of the migrants, he creates a more accurate portrayal and makes the migrants easier to relate to for a less affluent audience. Metaphors, particularly relating to nature, strengthen the themes in The Grapes of Wrath by enhancing the mood Steinbeck wants readers to feel at different points in the book. Overall, the intercalary chapters that Steinbeck includes improve his novel by making it more memorable and reinforcing the themes Steinbeck embraces throughout the novel. Exemplary stylistic devices further persuade readers of John Steinbeck’s personal beliefs. Steinbeck wrote The Grapes of Wrath to bring to light cruelties against migrants, and by using literary devices effectively, he continuously reminds readers of his purpose. Steinbeck’s impressive language choices in his intercalary chapters advance the entire novel and help to create a classic work of literature that people still are able to relate to today. 

This essay sticks pretty closely to the standard analytical essay outline. It starts with an introduction, where I chose to use a quote to start off the essay. (This became my favorite way to start essays in high school because, if I wasn’t sure what to say, I could outsource the work and find a quote that related to what I’d be writing about.) The quote in this essay doesn’t relate to the themes I’m discussing quite as much as it could, but it’s still a slightly different way to start an essay and can intrigue readers. I then give a bit of background on The Grapes of Wrath and its themes before ending the intro paragraph with my thesis: that Steinbeck used literary devices in intercalary chapters to show how rough migrants had it.

Each of my four body paragraphs is formatted in roughly the same way: an intro sentence that explains what I’ll be discussing, analysis of that main point, and at least two quotes from the book as evidence.

My conclusion restates my thesis, summarizes each of four points I discussed in my body paragraphs, and ends the essay by briefly discussing how Steinbeck’s writing helped introduce a world of readers to the injustices migrants experienced during the dust bowl.

What does this analytical essay example do well? For starters, it contains everything that a strong analytical essay should, and it makes that easy to find. The thesis clearly lays out what the essay will be about, the first sentence of each of the body paragraph introduces the topic it’ll cover, and the conclusion neatly recaps all the main points. Within each of the body paragraphs, there’s analysis along with multiple excerpts from the book in order to add legitimacy to my points.

Additionally, the essay does a good job of taking an in-depth look at the issue introduced in the thesis. Four ways Steinbeck used literary devices are discussed, and for each of the examples are given and analysis is provided so readers can understand why Steinbeck included those devices and how they helped shaped how readers viewed migrants and their plight.

Where could this essay be improved? I believe the weakest body paragraph is the third one, the one that discusses how Steinbeck used plain, grammatically incorrect language to both accurately depict the migrants and make them more relatable to readers. The paragraph tries to touch on both of those reasons and ends up being somewhat unfocused as a result. It would have been better for it to focus on just one of those reasons (likely how it made the migrants more relatable) in order to be clearer and more effective. It’s a good example of how adding more ideas to an essay often doesn’t make it better if they don’t work with the rest of what you’re writing. This essay also could explain the excerpts that are included more and how they relate to the points being made. Sometimes they’re just dropped in the essay with the expectation that the readers will make the connection between the example and the analysis. This is perhaps especially true in the second body paragraph, the one that discusses similarities to Biblical passages. Additional analysis of the quotes would have strengthened it.

Summary: How to Write an Analytical Essay

What is an analytical essay? A critical analytical essay analyzes a topic, often a text or film. The analysis paper uses evidence to support the argument, such as excerpts from the piece of writing. All analytical papers include a thesis, analysis of the topic, and evidence to support that analysis.

When developing an analytical essay outline and writing your essay, follow these five steps:

Reading analytical essay examples can also give you a better sense of how to structure your essay and what to include in it.

What's Next?

Learning about different writing styles in school? There are four main writing styles, and it's important to understand each of them. Learn about them in our guide to writing styles , complete with examples.

Writing a research paper for school but not sure what to write about? Our guide to research paper topics has over 100 topics in ten categories so you can be sure to find the perfect topic for you.

Literary devices can both be used to enhance your writing and communication. Check out this list of 31 literary devices to learn more !

Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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12 Reasons Why Data Is Important

Learn why data is important, what you can do with it, and how it relates to the field

If you work in human services because you hate math, terms like “data,” “quantitative analysis,” or “pivot table” might sound scary. Don’t be intimidated! Data does not have to be complicated. Simply stated, data is useful information that you collect to support organizational decision-making and strategy.

The list below shares twelve reasons why data is important, what you can do with it, and how it relates to the human services field. You can also download ’12 Reasons Why Data Is Important’ to print out copies and share with your colleagues and other stakeholders.

1. Improve People’s Lives

Data will help you to improve quality of life for people you support: Improving quality is first and foremost among the reasons why organizations should be using data. By allowing you to measure and take action, an effective data system can enable your organization to improve the quality of people’s lives.

2. Make Informed Decisions

Data = Knowledge. Good data provides indisputable evidence, while anecdotal evidence, assumptions, or abstract observation might lead to wasted resources due to taking action based on an incorrect conclusion.

3. Stop Molehills From Turning Into Mountains

Data allows you to monitor the health of important systems in your organization: By utilizing data for quality monitoring , organizations are able to respond to challenges before they become full-blown crisis. Effective quality monitoring will allow your organization to be proactive rather than reactive and will support the organization to maintain best practices over time.

4. Get The Results You Want

Data allows organizations to measure the effectiveness of a given strategy: When strategies are put into place to overcome a challenge, collecting data will allow you to determine how well your solution is performing, and whether or not your approach needs to be tweaked or changed over the long-term.

5. Find Solutions To Problems

Data allows organizations to more effectively determine the cause of problems. Data allows organizations to visualize relationships between what is happening in different locations, departments, and systems. If the number of medication errors has gone up, is there an issue such as staff turnover or vacancy rates that may suggest a cause? Looking at these data points side-by-side allows us to develop more accurate theories, and put into place more effective solutions.

6. Back Up Your Arguments

Data is a key component to systems advocacy. Utilizing data will help present a strong argument for systems change. Whether you are advocating for increased funding from public or private sources, or making the case for changes in regulation, illustrating your argument through the use of data will allow you to demonstrate why changes are needed.

7. Stop The Guessing Game

Data will help you explain (both good and bad) decisions to your stakeholders. Whether or not your strategies and decisions have the outcome you anticipated, you can be confident that you developed your approach based not upon guesses, but good solid data.

8. Be Strategic In Your Approaches

Data increases efficiency. Effective data collection and analysis will allow you to direct scarce resources where they are most needed. If an increase in significant incidents is noted in a particular service area, this data can be dissected further to determine whether the increase is widespread or isolated to a particular site . If the issue is isolated, training, staffing, or other resources can be deployed precisely where they are needed, as opposed to system-wide. Data will also support organizations to determine which areas should take priority over others.

9. Know What You Are Doing Well

Data allows you to replicate areas of strength across your organization. Data analysis will support you to identify high-performing programs, service areas, and people. Once you identify your high-performers, you can study them in order to develop strategies to assist programs, service areas and people that are low-performing.

10. Keep Track Of It All

Good data allows organizations to establish baselines, benchmarks, and goals to keep moving forward. Because data allows you to measure, you will be able to establish baselines, find benchmarks and set performance goals. A baseline is what a certain area looks like before a particular solution is implemented. Benchmarks establish where others are at in a similar demographic, such as Personal Outcome Measures® national data. Collecting data will allow your organization to set goals for performance and celebrate your successes when they are achieved.

11. Make The Most Of Your Money

Funding is increasingly outcome and data-driven . With the shift from funding that is based on services provided to funding that is based on outcomes achieved, it is increasingly important for organizations to implement evidence-based practice and develop systems to collect and analyze data.

12. Access The Resources Around You

Your organization probably already has most of the data and expertise you need to begin analysis. Your HR office probably already tracks data regarding your staff. You are probably already reporting data regarding incidents to your state oversight agency. You probably have at least one person in your organization who has experience with Excel. But, if you don’t do any of these things, there is still hope! There are lots of free resources online that can get you started. Do a web search for “how to analyze data” or “how to make a chart in Excel.”

Big Data: What It Is and Why It’s Important

These huge datasets reveal patterns and trends for better decision making.

What Is Big Data?

Big data refers to large, diverse data sets made up of structured, unstructured and semi-structured data. This data is generated continuously and always growing in size, which makes it too high in volume, complexity and speed to be processed by traditional data management systems.

Big Data Definition

Big data refers to massive, complex data sets that are rapidly generated and transmitted from a wide variety of sources. Big data sets can be structured, semi-structured and unstructured, and they are frequently analyzed to discover applicable patterns and insights about user and machine activity.

Big data is used across almost every industry to draw insights, perform analytics, train  artificial intelligence  and  machine learning  models, as well as help make data-driven business decisions.

Why Is Big Data Important?

Data is generated anytime we open an app, use a search engine or simply travel place to place with our mobile devices. The result? Massive collections of valuable information that companies and organizations manage, store, visualize and analyze.

Traditional data tools aren’t equipped to handle this kind of complexity and volume, which has led to a slew of specialized  big data software platforms  designed to manage the load.

Though the large-scale nature of big data can be overwhelming, this amount of data provides a heap of information for organizations to use to their advantage. Big data sets can be mined to deduce patterns about their original sources, creating insights for improving business efficiency or predicting future business outcomes.

As a result, big data analytics is used in nearly every industry to identify patterns and trends, answer questions, gain insights into customers and tackle complex problems.  Companies and organizations use the information  for a multitude of reasons like automating processes, optimizing costs, understanding customer behavior, making forecasts and targeting key audiences for advertising.

The 3 V’s of Big Data

Big data is  commonly characterized  by three V’s:

Volume refers to the huge amount of data that’s generated and stored. While traditional data is measured in familiar sizes like megabytes, gigabytes and terabytes, big data is stored in petabytes and zettabytes.

Variety refers to the different types of data being collected from various sources, including text, video, images and audio. Most data is unstructured, meaning it’s unorganized and difficult for conventional data tools to analyze. Everything from emails and videos to scientific and meteorological data can constitute a big data stream, each with their own unique attributes.

Big data is generated, processed and analyzed at high speeds. Companies and organizations must have the capabilities to harness this data and generate insights from it in real-time, otherwise it’s not very useful. Real-time processing allows decision makers to act quickly.

How Big Data Works

Big data is produced from multiple data sources like mobile apps, social media, emails, transactions or  Internet of Things (IoT)  sensors, resulting in a continuous stream of varied digital material. The diversity and constant growth of big data makes it inherently difficult to extract tangible value from it in its raw state. This results in the need to use specialized big data tools and systems, which help collect, store and ultimately translate this data into usable information. These systems make big data work by applying three main actions — integration, management and analysis.

1. Integration 

Big data first needs to be gathered from its various sources. This can be done in the form of  web scraping  or by accessing databases, data warehouses, APIs and other data logs. Once collected, this data can be ingested into a big data pipeline architecture, where it is prepared for processing.

Big data is often raw upon collection, meaning it is in its original, unprocessed state. Processing big data involves cleaning, transforming and aggregating this raw data to prepare it for storage and analysis.

2. Management

Once processed, big data is stored and managed within  the cloud  or on-premises storage servers (or both). In general, big data typically requires  NoSQL databases  that can store the data in a scalable way, and that doesn’t require strict adherence to a particular model. This provides the flexibility needed to cohesively analyze disparate sources of data and gain a holistic view of what is happening, how to act and when to act on data.

3. Analysis 

Analysis is one the final steps of the big data lifecycle, where the data is explored and analyzed to find applicable insights, trends and patterns. This is frequently carried out using big data analytics tools and software. Once useful information is found, it can be applied to make business decisions and communicated to stakeholders in the form of data visualizations.

Uses of Big Data

Here are a few  examples of industries  where the big data revolution is already underway:

Finance and insurance industries  utilize big data  and predictive analytics for fraud detection, risk assessments, credit rankings, brokerage services and blockchain technology, among other uses.  Financial institutions  also use big data to enhance their cybersecurity efforts and personalize financial decisions for customers.

Hospitals, researchers and pharmaceutical companies  adopt big data solutions  to improve and advance healthcare. With access to vast amounts of patient and population data, healthcare is enhancing treatments, performing more effective research on diseases like cancer and Alzheimer’s, developing new drugs, and gaining critical insights on patterns within population health.

Using  big data in education  allows educational institutions and professionals to better understand student patterns and create relevant educational programs. This can help in personalizing lesson plans, predicting learning outcomes and tracking school resources to reduce operational costs.

Retail utilizes big data  by collecting large amounts of customer data through purchase and transaction histories. Information from this data is used to predict future consumer behavior and personalize the shopping experience.

Big data in government  can work to gather insights on citizens from public financial, health and demographic data and adjust government actions accordingly. Certain legislation, financial procedures or crisis response plans can be enacted based on these big data insights. 

Big data in marketing  helps provide an overview of user and consumer behavior for businesses. Data gathered from these parties can reveal insights on market trends or buyer behavior, which can be used to direct marketing campaigns and optimize marketing strategies.

If you’ve ever used Netflix, Hulu or any other streaming services that  provide recommendations , you’ve witnessed big data at work. Media companies analyze our reading, viewing and listening habits to build individualized experiences. Netflix even uses data on  graphics, titles and colors  to make decisions about customer preferences.

Big Data Challenges

1. volume and complexity of data.

Big data is massive, complicated and ever growing. This makes it difficult in nature to capture, organize and understand, especially as time goes on. In order to manage big data, new technologies have to be developed indefinitely and organizational  big data strategies  have to continually adapt. 

2. Integration and Processing Requirements

Aside from storage challenges, big data also has to be properly processed, cleaned and formatted to make it useful for analysis. This can take a considerable amount of time and effort due to big data’s size, multiple data sources and combinations of structured, unstructured and semi-structured data. Processing efforts and identifying what information is useful can also be compounded in the case of excess noisy data or data corruption.

3. Cybersecurity and Privacy Risks

Big data systems can sometimes handle sensitive or personal user information, making them vulnerable to cybersecurity attacks or privacy breaches. As more personal data resides in big data storage, and at such massive scales, this raises the difficulty and costs of safeguarding this data from criminals. Additionally, how businesses collect personal data through big data systems  may not comply  with regional data collection laws or regulations, leading to a breach of privacy for affected users.

Big Data Technologies

Big data technologies describe the tools used to handle and manage data at enormous scales. These technologies include those used for big data analytics, collection, mining, storage and visualization.

Data Analysis Tools

Data analysis tools  involve software that can be used for big data analytics, where relevant insights, correlations and patterns are identified within given data.

Big Data Tools

Big data tools refer to any  data platform ,  database ,  business intelligence tool  or application where large data sets are stored, processed or analyzed. 

Data Visualization Tools

Data visualization tools  help to display the findings extracted from big data analytics in the form of charts, graphs or dashboards.

History of Big Data

“Big data” as a term became popularized  in the mid-1990s  by computer scientist John Mashey, as Mashey used the term to refer to handling and analyzing massive data sets.

In 2001, Gartner analyst Doug Laney characterized big data as having three main traits of volume, velocity and variety, which came to be known as the three V’s of big data.

Starting in the 2000s, companies began conducting big data research and developing solutions to handle the influx of information coming from the internet and web applications. Google created the Google File System  in 2003  and MapReduce  in 2004 , both systems meant to help process large data sets. Using Google’s research on these technologies, software designer Doug Cutting and computer scientist Mike Cafarella developed Apache Hadoop in 2005, a software framework used to store and process big data sets for applications. In 2006, Amazon released Amazon Web Services (AWS), an on-demand cloud computing service that became a popular option to store data without using physical hardware.

In the 2010s, big data gained more prevalence as mobile device and tablet adoption increased. According to IBM  as of 2020 , humans produce 2.5 quintillion bytes of data on a daily basis, with the world expected to produce 175 zettabytes of data by 2025. As connected devices and internet usage continue to grow, so will big data and its possibilities for enhanced analytics and real-time insights.

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Using Evidence: Analysis

Beyond introducing and integrating your paraphrases and quotations, you also need to analyze the evidence in your paragraphs. Analysis is your opportunity to contextualize and explain the evidence for your reader. Your analysis might tell the reader why the evidence is important, what it means, or how it connects to other ideas in your writing.

Note that analysis often leads to synthesis , an extension and more complicated form of analysis. See our synthesis page for more information.

Example 1 of Analysis

Without analysis.

Embryonic stem cell research uses the stem cells from an embryo, causing much ethical debate in the scientific and political communities (Robinson, 2011). "Politicians don't know science" (James, 2010, p. 24). Academic discussion of both should continue (Robinson, 2011).

With Analysis (Added in Bold)

Embryonic stem cell research uses the stem cells from an embryo, causing much ethical debate in the scientific and political communities (Robinson, 2011). However, many politicians use the issue to stir up unnecessary emotion on both sides of the issues. James (2010) explained that "politicians don't know science," (p. 24) so scientists should not be listening to politics. Instead, Robinson (2011) suggested that academic discussion of both embryonic and adult stem cell research should continue in order for scientists to best utilize their resources while being mindful of ethical challenges.

Note that in the first example, the reader cannot know how the quotation fits into the paragraph. Also, note that the word both was unclear. In the revision, however, that the writer clearly (a) explained the quotations as well as the source material, (b) introduced the information sufficiently, and (c) integrated the ideas into the paragraph.

Example 2 of Analysis

Trow (1939) measured the effects of emotional responses on learning and found that student memorization dropped greatly with the introduction of a clock. Errors increased even more when intellectual inferiority regarding grades became a factor (Trow, 1939). The group that was allowed to learn free of restrictions from grades and time limits performed better on all tasks (Trow, 1939).

In this example, the author has successfully paraphrased the key findings from a study. However, there is no conclusion being drawn about those findings. Readers have a difficult time processing the evidence without some sort of ending explanation, an answer to the question so what? So what about this study? Why does it even matter?

Trow (1939) measured the effects of emotional responses on learning and found that student memorization dropped greatly with the introduction of a clock. Errors increased even more when intellectual inferiority regarding grades became a factor (Trow, 1939). The group that was allowed to learn free of restrictions from grades and time limits performed better on all tasks (Trow, 1939). Therefore, negative learning environments and students' emotional reactions can indeed hinder achievement.

Here the meaning becomes clear. The study’s findings support the claim the reader is making: that school environment affects achievement.

Analysis Video Playlist

Note that these videos were created while APA 6 was the style guide edition in use. There may be some examples of writing that have not been updated to APA 7 guidelines.

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