case study of data mart

8 Week SQL Challenge

Start your SQL learning journey today!

• Case Study #5 - Data Mart

Danny Ma · June 20, 2021

Introduction

Data Mart is Danny’s latest venture and after running international operations for his online supermarket that specialises in fresh produce - Danny is asking for your support to analyse his sales performance.

In June 2020 - large scale supply changes were made at Data Mart. All Data Mart products now use sustainable packaging methods in every single step from the farm all the way to the customer.

Danny needs your help to quantify the impact of this change on the sales performance for Data Mart and it’s separate business areas.

The key business question he wants you to help him answer are the following:

• What was the quantifiable impact of the changes introduced in June 2020?
• Which platform, region, segment and customer types were the most impacted by this change?
• What can we do about future introduction of similar sustainability updates to the business to minimise impact on sales?

Available Data

For this case study there is only a single table: data_mart.weekly_sales

The Entity Relationship Diagram is shown below with the data types made clear, please note that there is only this one table - hence why it looks a little bit lonely!

Column Dictionary

The columns are pretty self-explanatory based on the column names but here are some further details about the dataset:

• Data Mart has international operations using a multi- region strategy
• Data Mart has both, a retail and online platform in the form of a Shopify store front to serve their customers
• Customer segment and customer_type data relates to personal age and demographics information that is shared with Data Mart
• transactions is the count of unique purchases made through Data Mart and sales is the actual dollar amount of purchases

Each record in the dataset is related to a specific aggregated slice of the underlying sales data rolled up into a week_date value which represents the start of the sales week.

Example Rows

10 random rows are shown in the table output below from data_mart.weekly_sales :

Interactive SQL Instance

You can use the embedded DB Fiddle below to easily access these example datasets - this interactive session has everything you need to start solving these questions using SQL.

You can click on the Edit on DB Fiddle link on the top right hand corner of the embedded session below and it will take you to a fully functional SQL editor where you can write your own queries to analyse the data.

You can feel free to choose any SQL dialect you’d like to use, the existing Fiddle is using PostgreSQL 13 as default.

Serious SQL students will have access to the same relevant schema SQL and example solutions which they can use with their Docker setup from within the course player!

Case Study Questions

The following case study questions require some data cleaning steps before we start to unpack Danny’s key business questions in more depth.

1. Data Cleansing Steps

In a single query, perform the following operations and generate a new table in the data_mart schema named clean_weekly_sales :

Convert the week_date to a DATE format

Add a week_number as the second column for each week_date value, for example any value from the 1st of January to 7th of January will be 1, 8th to 14th will be 2 etc

Add a month_number with the calendar month for each week_date value as the 3rd column

Add a calendar_year column as the 4th column containing either 2018, 2019 or 2020 values

Add a new column called age_band after the original segment column using the following mapping on the number inside the segment value

• Add a new demographic column using the following mapping for the first letter in the segment values:

Ensure all null string values with an "unknown" string value in the original segment column as well as the new age_band and demographic columns

Generate a new avg_transaction column as the sales value divided by transactions rounded to 2 decimal places for each record

2. Data Exploration

• What day of the week is used for each week_date value?
• What range of week numbers are missing from the dataset?
• How many total transactions were there for each year in the dataset?
• What is the total sales for each region for each month?
• What is the total count of transactions for each platform
• What is the percentage of sales for Retail vs Shopify for each month?
• What is the percentage of sales by demographic for each year in the dataset?
• Which age_band and demographic values contribute the most to Retail sales?
• Can we use the avg_transaction column to find the average transaction size for each year for Retail vs Shopify? If not - how would you calculate it instead?

3. Before & After Analysis

This technique is usually used when we inspect an important event and want to inspect the impact before and after a certain point in time.

Taking the week_date value of 2020-06-15 as the baseline week where the Data Mart sustainable packaging changes came into effect.

We would include all week_date values for 2020-06-15 as the start of the period after the change and the previous week_date values would be before

Using this analysis approach - answer the following questions:

• What is the total sales for the 4 weeks before and after 2020-06-15 ? What is the growth or reduction rate in actual values and percentage of sales?
• What about the entire 12 weeks before and after?
• How do the sale metrics for these 2 periods before and after compare with the previous years in 2018 and 2019?

4. Bonus Question

Which areas of the business have the highest negative impact in sales metrics performance in 2020 for the 12 week before and after period?

• demographic
• customer_type

Do you have any further recommendations for Danny’s team at Data Mart or any interesting insights based off this analysis?

This case study actually is based off a real life change in Australia retailers where plastic bags were no longer provided for free - as you can expect, some customers would have changed their shopping behaviour because of this change!

Analysis which is related to certain key events which can have a significant impact on sales or engagement metrics is always a part of the data analytics menu. Learning how to approach these types of problems is a super valuable lesson and hopefully these ideas can help you next time you’re faced with a tough problem like this in the workplace!

Ready for the next 8 Week SQL challenge case study? Click on the banner below to get started with case study #6!

Official Solutions

If you’d like to see the official code solutions and explanations for this case study and a whole lot more, please consider joining me for the Serious SQL course - you’ll get access to all course materials and I’m on hand to answer all of your additional SQL questions directly!

Serious SQL is priced at $49USD and $29 for students and includes access to all written course content, community events as well as live and recorded SQL training videos!

Please send an email to [email protected] from your educational email or include your enrolment details or student identification for a speedy response!

Community Solutions

This section will be updated in the future with any community member solutions with a link to their respective GitHub repos!

Final Thoughts

The 8 Week SQL Challenge is proudly brought to you by me - Danny Ma and the Data With Danny virtual data apprenticeship program.

Students or anyone undertaking further studies are eligible for a $20USD student discount off the price of Serious SQL please send an email to [email protected] from your education email or include information about your enrolment for a fast response!

We have a large student community active on the official DWD Discord server with regular live events, trainings and workshops available to all Data With Danny students, plus early discounted access to all future paid courses.

There are also opportunities for 1:1 mentoring, resume reviews, interview training and more from myself or others in the DWD Mentor Team.

From your friendly data mentor, Danny :)

All 8 Week SQL Challenge Case Studies

All of the 8 Week SQL Challenge case studies can be found below:

• Case Study #1 - Danny's Diner
• Case Study #2 - Pizza Runner
• Case Study #3 - Foodie-Fi
• Case Study #4 - Data Bank
• Case Study #6 - Clique Bait
• Case Study #7 - Balanced Tree Clothing Co.
• Case Study #8 - Fresh Segments

Share: Twitter , Facebook

• Trend & Composition Analytics
• Trading & Risk Analytics
• Portfolio Risk
• Loan and Private Credit Data
• Credit Loss Accounting
• Climate Risk
• Data Science and ML Services
• Data Pipelines / Framework Engineering
• Data Quality Assurance
• Snowflake Integration
• Model Builds
• Model Risk Management
• Portfolio Analysis
• Securitization
• Application Modernization
• Cloud Data Migration

GSE: Datamart Design and Build

The Problem

A government-sponsored enterprise needed a centralized data solution for its forecasting process, which involved cross-functional teams from different business lines.​

The firm also sought a cloud-based data warehouse to host forecasting outputs for reporting purposes with faster querying and processing speeds.​

The firm also needed assistance migrating data from legacy data sources to new datamarts. The input and output files and datasets had different sources and were often in different formats. Analysis and transformation were required prior to designing, developing and loading tables.  

The Solution

RiskSpan built and now maintains a new centralized datamart  (in both Oracle and Amazon Web Services) for the client’s  revenue and loss forecasting processes. This includes data modeling, historical data upload, and the monthly recurring data process.

The Deliverables

• Analyzed the end-to-end data flow and data elements​
• Designed data models satisfying business requirements​
• Processed and mapped forecasting input and output files​
• Migrated data from legacy databases to the new sources ​
• Built an Oracle datamart and a cloud-based data warehouse (Amazon Web Services) ​
• Led development team to develop schemas, tables and views, process scripts to maintain data updates and table partitioning logic​
• Resolved data issues with the source and assisted in reconciliation of results

More from RiskSpan

What Do 2024 Origination Trends Mean for MSRs?

RiskSpan | Apr 8,2024

While mortgage rates remain stubbornly high by recent historical standards, accurately forecasting MSR performance and valuations requires a thoughtful evaluation…

GenAI Applications for Loans and Mapping Data

RiskSpan | Mar 18,2024

RiskSpan is actively furthering the advancement of several GenAI applications aimed at transforming how mortgage loan and private credit investors…

Case Study: How a Large Financial Institution Allayed Regulator Concerns by Digitizing its Model Performance Tracking

RiskSpan | Mar 1,2024

The Situation  One of the largest financial institutions in the world, operating in a highly competitive and regulated environment, found…

Comments are closed.

Security & Compliance

D'Mart: Most Successful Indian Chain of Hypermarkets[DMart Case Study]

Varad Kitey

D'Mart is an Indian chain of hypermarkets established by DMart owner Radhakishan Damani on May 15, 2002. DMart has 214 stores in 72 cities across 11 states in India including Maharashtra, Andhra Pradesh, Telangana, Gujarat, Madhya Pradesh, Chhattisgarh, Rajasthan, National Capital Region, Tamil Nadu, Karnataka, Uttar Pradesh, Daman, and Punjab. So, let's get started with the D'mart case study .

Mumbai headquartered DMart is owned and operated by Avenue Supermarts Ltd. (ASL). After the IPO posting (as Avenue Supermarts Ltd.), it made a record opening on the National Stock Exchange(NSE). DMart’s valuation rose to ₹39,988 crore after the close of the stock on 22 March 2017. This made DMart the 65th most significant Indian firm, followed by Britannia Industries , Marico, and Bank of Baroda. As of 21 November 2019, the market capitalization of DMart was around ₹114,000 crore, taking it on 33rd position of all recorded organizations on the Bombay Stock Exchange.

This article will shed insights on the supply chain model of DMart, its business model, marketing strategies, How DMart was started, key financial highlights of DMart, growth and future of DMart in India & more.

In this Dmart Case Study, we have discussed the -

DMart - Company Highlights Foundation of DMart & Why DMart is Successful? Strategic & Organization Structure of DMart DMart - Business Model & Supply chain Model Marketing Strategy of DMart Factors Affecting the Profit of DMart DMart - Important Financial Metrics Growth of DMart in India Future of DMart

DMart - Company Highlights

Foundation of dmart & why dmart is successful.

Unlike Flipkart was established by two 25-year old youngsters toward the beginning of their professions, DMart's establishing story couldn't have been more extraordinary as DMart was established in 2002 by a then-45-year-old Radhakishan Damani at a moment that he'd effectively made his millions. When he established DMart, Damani was an incredible name in Indian securities exchanges. He had already got a few worth stocks that surpassed Gillette and HDFC Bank’s valuations.

Damani, who dropped out of a trade degree after the primary year, had first joined his dad's metal rollers business, yet had begun putting resources into stocks when he was 32. He wound up getting to be one of the greatest stock financial specialists of the 90s, and current securities exchange bull Rakesh Jhunjhunwala believes him to be a tutor. In any case, after an effective financial exchange profession putting resources into shopper confronting organizations, Damani chose to begin his own.

On May 15, 2002, Damani established grocery store chain DMart and embraced techniques that were one of a kind to Indian retail. Up to that point, most retail chains rented their stores, yet DMart picked carefully do its exploration and possessed its very own stores by and large. That technique appears to have worked as DMart has never needed to close down a store since it's opened in every one of the long periods of its activity.

While other retail players forayed into different classifications, including hardware and design, DMart stayed focussed on its center sustenance and basic food item business. What's more, when other store chains are on the whole propelling their very own private brands in an offer to improve edges, DMart still stocks just outsider items.

It's this moderate methodology that has worked for DMart. Other retail chains were picking development, yet for the initial 15 years, Dmart just worked its stores in 4 states. Indeed, even today, the company has 214 stores in 72 cities across 11 states. DMart had a benefit to-deals proportion of 3.7%.

In correlation, other significant Indian retailers don't passage very also Future Group has a benefit to deals proportion of 0.21%, Spencer's Retail had a negative benefit to deals proportion of - 8.9%, and Reliance Retail which works high-edge classifications including hardware and adornments and has more than double the incomes of DMart just dealt with a benefit to deals proportion of 1.6%.

DMart's traditionalist yet beneficial approach is by all accounts demonstrated after its author. Damani is famously media-bashful and gives no meetings. He's said to be modest, all things considered, also he doesn't appear to talk much, yet is evidently a decent audience, engrossing a lot of data rapidly, and afterward following up on it.

And keeping in mind that Damani's success has made him hugely rich because of the flood in DMart's stock value, he's currently worth $15.5 Billion (over Rs 116,200 Crores) regardless he wears a white shirt and white jeans to work, the dress he's been wearing since the 80s. Despite everything, he goes for night strolls on Girgaum Chowpatty in Mumbai and unconditionally converses with the outsiders who approach him after his Dmart's open achievement.

Strategic & Organization Structure of DMart

The ultimate start with DMart needs to make a picture among the majority of a rebate store that offers the vast majority of the items from over every single real brand. Fundamentally, a store that offers an incentive for cash! Presently, since individuals for the most part come to DMart on the grounds that they all what they need under one rooftop; consequently, DMart stores are operational in high rush hour gridlock territories and crosswise over three organizations including Hypermarkets that are spread crosswise over 30,000-35,000 sqft, Express group, that is spread more than 7,000-10,000 sqft and in conclusion, the SuperCenters, that are set up at more than 1 lakh sqft.

What's more, Dmart's intended interest group being the center pay gathering, it uses Discount offers as a special instrument for baiting the clients and expanding deals too. Generally speaking – Dmart's prosperity is centered around three things: Customers, Vendors, and Employees ! Take Customers. Since Dmart is focusing on center salary family units, every one of their stores is in, or near, neighborhoods and not in shopping centers.

Their thought isn't to meet each customer's need like different contenders, yet rather, Dmart tries to meet most normal shopper needs, while offering some benefit for their cash. Furthermore, since, 90% of these stores are possessed legitimately by Dmart, they don't need to stress over month-to-month rentals and their ascent, or migration chance. Moreover, this is helping them manufacture resources on their books.

This likewise keeps Dmart all around promoted and obligation light, while its tasks produce extra money. All the cash that is spared utilizing this procedure is at the end offered back to the clients as limits! Sellers! Seller connections are the second mainstay of their model. Since he originates from a dealer foundation, his seller connections have been his greatest quality.

The FMCG business has an installment standard of 12-21 days, however, Dmart pays its sellers on the eleventh day itself. This causes him to remain in the great books of the merchants and dodges stockouts. Furthermore, since Dmart purchases in mass and pays its sellers well in time, they additionally get the chance to win higher edges. Essentially, their procedure is to "Get it low, Stack it high and sell it shabby"!Workers! This is the third mainstay of their model. DMart offers great cash, adaptability, strengthening, and loose and effective work culture.

They even proceed to employ tenth standard dropouts with the correct frame of mind and duty. They incline toward procuring crude ability and afterward put intensely in preparing, to shape them according to their prerequisite. Representatives are simply educated once concerning the worth framework and arrangements at D-Mart and after that are enabled by giving them the opportunity to work without someone continually investigating their shoulders. There is outright lucidity on what should be accomplished, yet you don't have to dread targets.

DMart - Business Model & Supply chain Model

The business model lies at the core of a successful company. A good, foolproof business model not only acts as a pillar for a business to grow but also helps it prosper in a comparatively less amount of time.

DMart, often termed as the Walmart of India, has been quite successful in its business so far, and a major credit goes to the robust business model it has developed over the years.

The chain of DMart operates on a B2C (Business to Consumer) model in which the company sells its goods from the manufacturer’s house to that of the end-user. DMart sells a wide range of products ranging from home care and personal care to grocery and staples, daily essentials, home appliances, footwear, luggage, fruits and vegetables, men’s and women’s apparel, and more. These goods, as we all know, fulfill our everyday needs, and hence, have a significant demand throughout the year. Therefore, they wipe out the possibilities of fluctuations due to high demand and helps the brand get the stability that many others dream about.

DMart is recognized for its thrifty cost structure that has made the company keep its losses under control. Here are some prominent characteristics of DMart’s business model:

Low operational costs and fewer expenses

DMart believes in the effective utilization of the spaces instead of adorning its interiors and shelves fancifully. The company works in launching more and more products in fewer spaces for the customers to choose from, which can also be summed up as a low-interior-cost concept to reduce the operational costs. Besides, when you walk into a DMart store you would also find lesser billing counters, which further works in reducing employee costs.

Ownership model

Damani, the company’s founder, had decided quite early in the game to adopt a store-ownership model. This played a major part in making DMart a low or no debt company, thereby strengthening it financially. Furthermore, the company doesn’t accrue any rental costs, which helps DMart open more stores and gain high positive cash flows. The company owns around 80% of all the stores that it is credited for.

Affordable rates of products

It is usually observed that in the FMCG sector, the retailers pay off the credit to their vendors within a period of 3 weeks whereas DMart pays off their credit within a week. This helps the company benefit in many ways including the huge discounts that they get from the vendors , which in turn is entirely rewarding for the end-users too.

Affordable rate of products with tons of discounts on various products leads to increasing the overall footfall and spike up the sales volume. This increasing sale also helps the manufacturers to rely on the brand and bring in more stocks for the rising demand, which extends another volume discount from the manufacturers' end.

Also Read: The Complete Psychology behind Free Samples & How it Works

Slotting fee

DMart levies a ‘Slotting Fee’. As the term might indicate, it is a fee that DMart charges from the manufacturers to store their products on the shelves of DMart stores , which is also sometimes referred to as an entry fee. DMart, on the other hand, with its appealing marketing strategies and attractive discounts ensures that the products are sold out as quickly as possible.

Sales channel

As discussed earlier, DMart opts for a B2C (Business to Consumer) business model, where the company sells the products directly from manufacturers to the end-consumer. The company purchases its goods in bulk and this eliminates the middleman (distributors and wholesalers) from the chain, which helps in passing their commissions as discounts to the consumers.

Target customers

DMart’s target customers are the middle-class groups and lower-middle-class groups, those who often want to buy low-cost goods that come with hefty discounts but are of good quality. This makes DMart attract an extensive customer base than many other retailers.

Regional Goods

A land of diversity, India nurtures an array of region-specific goods. This gave DMart an amazing opportunity to capture the niche markets with products specific to different regions. DMart researches the popular local brands of a particular region and makes them available, thereby avoiding people’s need to go to the local Kirana stores. This has helped DMart to gain more market share.

Operating strategy

Contrary to their peers and rivals, DMart has always stuck to their own stores and deliberately avoided the malls, which might have otherwise risked the overall sales of the company and increased the expenditure.

Besides, the company is also not very comfortable expanding geographically. The company had its stores only in 4 Indian states until 2014, which only expanded in recent years to 11 states. One another thing is that DMart attracts low marketing costs because the main marketing strategy of DMart is that the company is recognized among its end-users via “ word of mouth ”.

Marketing Strategy of DMart

DMart is a company that doesn’t believe in marketing aggressively unlike many of its competitors. The company maintains a marketing mix where its Unique Selling Position (USP) lies in offering the products at less than Maximum Retail Price (MRP). This is the most important factor that contributes to keeping the company ahead of its peers.

What DMart indulges in is aggressive CSR activities and other low-cost promotional activities . One of the most promising campaigns is:

Better School, Brighter Futures!

DMart is a company that takes pride in the laudable CSR initiatives that it takes. Over the years, the company has grown to be a huge support for its employees and other communities alike with the help of its socially responsible business practices. This undoubtedly spreads positive vibes all around.

In its “Better School, Bright Futures!” campaign, DMart has launched an amazing program in various schools that are there in and around Mumbai. The sole aim of which helps students understand things better and create an ecosystem that allows them to benefit from better education, mentoring research facilities, and new networking opportunities.

Embracing Low-Cost Advertising Mediums for Promotion

DMart looks up to visual and print mediums to promote its brand name and products. The print medium of advertising revolves around newspaper ads with information about their products, discounts, sales, and coupons.

On the other hand, the visual component of advertisement comprises the banners, flexes, and hoardings that are put to display in locations near the stores to mention the product-specific offers, seasonal discounts, and other freebies that the company offers from time to time.

Digital Presence of DMart

DMart was founded back in 2002 and boasts of an enviable offline presence but when it comes to digital presence it bothered little about it to be true. However, the company has taken a few steps to place it ahead on the digital front. These steps include the installation of a chatbot on Facebook Messenger and the launch DMart Ready.

As of now, DMart uses Facebook as a medium for information, which the brand uses to inform and clear customers’ doubts. The company is yet to explore Instagram and Twitter fully, the proper utilization in the upcoming times will surely help the company set itself more stable in the future.

Factors Affecting the Profit of DMart

Damani is a calm man who stays under the radar, yet his triumphant characteristics are too obvious to possibly be missed. The following are his ways to deal with a business that drove him to thundering achievement:

Design of Logo

Like Warren Buffett, Damani too has been a worth speculator who might take a shrewd perspective on the long haul. When he turned into a business person, he held a similar methodology and manufactured DMart without depending on any speedy alternate ways. For example, he never rents the property for his stores however gets it. In the long haul, it spares him from a major rental outgo. This was a key factor behind the productivity of DMart.

What Is Trifle That's Important

Damani began little and did not rush to grow. Low scale gave him superior control of the store network and enabled him to concentrate on benefits directly from the earliest starting point. In the 18 years of its reality, D Mart has turned a benefit every year.

Evaluation Of People

Damani started with purchasing an establishment of Apna Bazar. That was the point at which he started fabricating individual relations with merchants and providers. He esteems both and they never let him down. The stores never leave stock.

Selling As Cheap

Damani realized what he was doing: offering individuals buyer results of everyday use at substantial limits. That turned into his sole objective. One of his strategies was to pay his providers and sellers inside days rather than weeks which was the business standard. They gave the merchandise at a less expensive rate to him in lieu of early installment. He passed on the money-saving advantages to his clients, which guaranteed steady success.

Go Steady And Slow

In spite of the fact that D-Mart began 18 years prior, despite everything it has 119 stores in a couple of states, a modest number contrasted with those claimed by Ambani and Biyani. Rather than fast development, Damani received a moderate pace which gave him his emphasis on productivity. That is the reason D-Mart has not closed a solitary store since it began and creates higher per-store incomes than the stores of Ambani or Biyani.

Neglect the Herd

Damani had learned and drilled with the progress the craft of not following the crowd while he was a financial specialist. As a business person, he has a similar methodology. There have been such a large number of brand new thoughts in retail, for example, different online business patterns, which he didn't give any significance. Designs or patterns can't impact the man who realizes what he needs and how he can get it.

Available Locally

Despite the fact that DMart is the best basic food item retail chain in the nation, Damani has restricted it towards the western states. One reason is his dependence on neighborhood supplies rather than expand supply chains.

A Job Has Conversation

Damani stays under the radar which bears him all-out devotion to his work. His moderate and quiet ascent in a discouraged division is a sign of his resolute spotlight on work. He has once in a while given a meeting to a TV channel or a paper.

DMart - Important Financial Metrics

The below table highlights the important financial metrics of DMart as per its audited, consolidated financial statements -

(Rs. in crores, unless otherwise stated)

Standalone Results -

For the quarter ended March 31, 2021 (Q4FY21):

• Total Revenue stood at Rs. 7,303 Crore, YoY growth of 17.9%  
• EBITDA of Rs. 617 Crore; YoY growth of 47.6%
• PAT stood at Rs. 435 Crore; YoY growth of 51.6%  
• Basic EPS for Q4FY21 stood at  Rs.6.71, as compared to Rs. 4.49  for Q4FY20
• 13 stores were added in Q4FY21

For the year ended  March 31, 2021  (FY21):

• Total Revenue stood at Rs. 23,787 Crore, lower by 3.6%
• EBITDA of Rs. 1,742 Crore; YoY decline of 17.9%
• PAT stood at Rs. 1,165 Crore; YoY decline  of 13.7%
• Basic EPS for FY21 stood at Rs.17.99,  as compared to Rs. 21.49  for  FY20
• 22 stores were added in FY21 and 2 stores were converted into fulfillment centers for Avenue ECommerce Limited.

Growth of DMart in India

Avenue Supermarts running the DMart chain of stores in the nation revealed a 21.4 % year-on-year net benefit development and a 32.1 % year-on-year income development for the quarter finished March 31, 2019, (Q4) at Rs 203 crore and Rs 5,033 crore, separately.

For the three months finished December 31, 2018, DMart had announced its slowest net benefit development in eight quarters at 2.1 % as it pondered developing challenges in basic food item retail.

Second from last quarter income development came in at 33 % (year-on-year), which is likewise a merry quarter, said experts, suggesting the organization had figured out how to keep up its pace of development as far as the top line in Q4 in the midst of focused power. The numbers were comprehensively in accordance with Street gauges. A survey by investigators of Bloomberg had pegged net benefit at Rs 211 crore and income at Rs 5,122 crore for the quarter under audit.

Income before intrigue, duty, deterioration, and amortization (Ebitda) for Q4 was at Rs 377 crore, up 27.9 % throughout the year-prior period and again extensively in accordance with Street assessments of Rs 395 crore. Yet, Ebitda edges contracted for the third straight quarter, however, the drop was negligible at 20 premise focuses to 7.5 % from a year sooner.

This is additionally the most reduced as far as Ebitda edges for DMart in 75%. While the organization did not indicate same-store deals development for Q4, examiners said it was somewhere in the range of 15 and 18 % for the period under audit.

Same-store deals development is the development of a similar deal of stores for one year or more. For the entire year finished March 31, 2019, (FY19), Neville Noronha, overseeing executive (MD) and (CEO), Avenue Supermarts, said same-store deals development was 17.8 % even as income grew 32 % year-on-year to Rs 19,916 crore and net benefit went up 19 % from a year sooner to Rs 936 crore.

The FY19 same-store deals development was higher than the 14.2 % revealed for FY18, division examiners stated, as the firm drove higher deals throughput at its stores. Income from deals per square feet at DMart stores remained at Rs 35,647 for FY19 against Rs 32,719 in FY18, an ascent of about 9 %. The organization additionally included 21 stores in FY19, of which 12 were included in Q4 alone, taking the aggregate to 176 for the monetary year.

Future of DMart

Avenue Supermarts runs the DMart grocery store chain of stores. If in any case, the nation experiences a crisis, financial specialists question whether the organization shows enough strength during these intense occasions. But examiners in a note from Systematix Shares and Stocks (India) Ltd. said, " The continuous crisis in utilization and higher aggressive force in staple retail should confine development in determining deals per square feet to 7% in the financial year 2020 from 13% in FY19."

While speculators will intently follow how that works out in the coming quarters, Avenue Supermarts' income development of almost 27% in the June quarter is nothing to get surprised at. Obviously, it should likewise be referenced at the same time that high development rates are a basic for the DMart share, which is one of the most costly stocks in the nation.

It currently exchanges at amazing multiple times evaluated income for FY20. FY20 has begun an idealistic note for the organization. The development in EBITDA (income before premium, assessment, deterioration, and amortization) edge in the June quarter will mitigate financial specialists' uneasiness about weights on productivity somewhat.

What is Dmart?

Founded in 2002, Dmart is an Indian retail corporation that is designed to stand as a one-stop supermarket chain that brings a wide range of products ranging from basic home products, personal products and more.

Where is the Dmart headquarters?

DMart headquarters is in Mumbai, Maharashtra.

Who founded Dmart?

Radhakishan Damani and his family founded Dmart in 2002.

Where was the first branch of D mart?

The first branch of D mart is in Powai's Hiranandani Gardens.

What is the vision and mission of Dmart?

The mission and vision of DMart is " to provide the best possible value for consumers so that every penny spends on shopping gives them more value for money than they would get anywhere else," as per the vision and mission statement of Dmart.

How many D mart stores in India are there in total?

Currently, the total number of D mart stores in India were reported to be more than 234 in number, spread across more than 11 states in India, as per February 2022's reports.

Must have tools for startups - Recommended by StartupTalky

• Convert Visitors into Leads- SeizeLead
• Payment Gateway- Razorpay
• Spy on your Competitors- Adspyder
• Manage your business smoothly- Google Workspace

How to Excel in Internships: Dos and Don'ts for Students

This article has been contributed by Aarul Malaviya, Founder, AI enabled - Zamit. Internships serve as invaluable opportunities for students to gain practical experience, explore career paths, and develop essential skills for the professional world. Excelling in internships requires a strategic approach and a commitment to learning and growth. To

The Impact of AI on Digital Marketing Campaigns and the Latest AI Technologies Shaping ROI and Campaign Optimisation in the Industry

This article has been contributed by Prateek Sethi, Founder, Trip Creative Agency. The dawn of the 21st century significantly changed people's personal and professional lives. Among other shifts, new-age technologies have gained considerable traction by altering the dynamics for the better. As a result, artificial intelligence (AI), formerly regarded as

Predictive Analytics in Healthcare: Utilizing AI to Anticipate and Minimize Adverse Health Events

This article has been contributed by Dr Narendra Vankar, Co-Founder, Quantum CorpHealth Pvt. Ltd. India’s health landscape is rapidly evolving. We are moving towards the best possible medical care with experienced, trained doctors; new advancements in medical technology; and the introduction of personalized, AI-based healthcare, perfectly tailored to fit

Inside the Profit Engine: How Online Marketplaces Monetize Beyond Sales?

Online marketplaces have revolutionized commerce, transforming the way we buy and sell goods and services. These platforms connect a diverse range of buyers and sellers, creating a dynamic virtual marketplace that fosters convenience, choice, and often, competitive pricing. But how do these online marketplaces themselves turn a profit?  The Rise

Finance Data Mart

Oliver Vander Horn

March 13, 2024

A Personal Data Mart for Financial Analysis

Who should read this.

This article is for operators who analyze confidential financial data over time (time-series analysis). This includes historical financials, forecast, headcount, quota attainment, commissions, operating plans, and more.

A data mart is a well-known term in Data Management. However, only the most sophisticated finance teams understand its capabilities.

What is a Personal Finance Data Mart?

A Finance Data Mart is a centralized hub that streamlines data management - your Google Drive on steroids. It allows you to import spreadsheets, create live connections to Google Sheets, 1-click integrate with CRMs and data sources, clean and transform data, analyze data from various sources, and provide a one-stop repository. More than just a storage system, it's a strategic asset that drives financial insights, enabling business growth. Designed for user-friendliness, it's cost-effective and efficient. With a personal finance data mart, finance teams maintain control, bypassing bottlenecks, quickly adapting to changes, and driving strategic insights, making it invaluable for CFOs and FP&A teams.

In the context of corporate finance, a data mart is a specialized version of a data warehouse. It's essentially a storage system, like a mini supermarket, where all relevant financial data is consolidated and can be easily accessed when needed. This data may include historical financials, forecasts, headcount, quota attainment, commissions, operating plans, and more. The data mart allows for automated data import, cleaning, transformation, consolidation, analysis, exporting, and connecting to other apps like spreadsheets and visualization tools. It's designed to overcome challenges related to data size, speed, and detail level, making financial data management more efficient.

Personal Data Mart KPIs

You can measure the success of a Personal Data Mart I you can outperform the following KPIs:

• Time it takes to import, integrate, consolidate, and extract or visualize the first data sets
• 10% of the cost of a bare-bones centralized IT solution
• Reduction in cost compared to a traditional centralized IT with a Finance “Center of Excellence”
• Note: a database manager will cost around $100k per year, and a data engineer will cost more, and this excludes the software that runs in the tens of thousands
• Time taken to integrate and sync various data sources
• Time taken for the team to understand and start effectively using the personal data mart
• The accuracy of the data in the data mart compared to the original data sources
• Satisfaction rate among the users of the personal data mart
• Ability to extract historical information, such as scenarios and plans
• Note: It’s standard that not all historical information is loaded, as this can be a very large amount with no practical use
• Time taken to find, access, and extract required data
• Time taken to start generating valuable insights from the data

Picture this

Imagine having a Google Drive on steroids, where all your data sources and Google Sheets are consolidated into a single database. This personal data mart can be referenced in any other sheet or system, unrestricted by data size, speed, or level of detail. It's akin to your familiar Google Drive, but turbocharged with the power of Google BigQuery, tailored to meet your unique needs. This is not just a storage unit; it's akin to a well-stocked mini supermarket, where all your data needs are met efficiently, making managing your spreadsheets a breeze, doing away with any technical challenges or resources.

How is a Personal Data Mart Different Than a Traditional Data Mart?

A traditional data Mart is a subsection of an IT owned data warehouse, typically separated for a specific department. A personal data Mart is owned and operated and managed by an individual or team, not an external team of IT resources. The major differences are:

• Centralized-IT: Owned and operated by IT department.
• Personal Data Mart: Owned and operated by the user, reducing overhead and limiting exposure to confidential information.
• Centralized-IT: Requires technical expertise.
• Personal Data Mart: Designed for a non-technical audience, similar to Google Drive.
• Centralized-IT: High cost of establishment and maintenance.
• Personal Data Mart: Can be established for a fraction of the cost without any technical resource requirements.
• Centralized-IT: Takes weeks to months to stand up (excludes hiring)
• Personal Data Mart: Can be stood up in seconds.
• Centralized-IT: Large recurring expense to maintain connections.
• Personal Data Mart: Requires as much maintenance as Google Drive.
• Centralized-IT: Limited flexibility to meet unique needs.
• Personal Data Mart: Can be tailored to meet individual or team needs.
• Centralized-IT: Limited access to data, virtually zero ability to post (write) data to production.
• Personal Data Mart: Users have direct access, facilitating faster and more efficient data analysis.
• Centralized-IT: Managed by IT departments internal resources.
• Personal Data Mart: Offloaded to the vendor, such as Google.
• Centralized-IT: Limited adaptability to accommodate new data sources or changes in data structures.
• Personal Data Mart: Nimble and flexible, designed to intake new data sources in flexible formats quickly.
• Centralized-IT: Requires significant infrastructural changes to scale.
• Personal Data Mart: Can scale up or down based on user needs.
• Centralized-IT: Highly flexible as all infrastructure is built and maintained internally.
• Personal Data Mart: Limited customization due to the pre-configured templates required for turnkey value.
• Centralized-IT: Homegrown investment in integrations and maintenance with other tools and platforms.
• Personal Data Mart: Prebuilt and simple integrations with other tools.
• Centralized-IT: Finance and IT control the budget and allocation of resources.
• Personal Data Mart: Fixed and predictable scaleable spend.
• Centralized-IT: Very difficult, requires the termination of long-term contracts and employees.
• Personal Data Mart: Can be turned on and off easily.

How a Personal Finance Data Mart Works:

Watch this <5min video that shows how to import data from multiple Google Sheets via a live connection, consolidate files into a single source, and connect the data mart back to a Google Sheet to get a specific view just for a Sales team.

Create a personal data mart from scratch, consolidate multiple files, and generate a report for the Sales team in under 5 minutes!

With a personal finance data mart, you can effortlessly connect to a data source, Google Sheet, or upload a file. The difference is, your data is now powered by a robust data warehouse, enabling you to handle large volumes of data with ease.

Here's a clear and simple step-by-step guide on how a personal finance data mart works:

• Time to Live: <5 minutes
• Time to Live: <1 minute

This is the power of our personal finance data mart. It's designed to make your data management tasks as hassle-free as possible, while also providing you with the robust capabilities of Google BigQuery.

Capabilities and Feature List:

• Import Data: Just upload a CSV, and voila! Your data set is instantly available and ready for action. For instance, import sales data to track performance over time.
• Connect Sheets: Link your Google Sheets effortlessly. Pull in data, or extract it to another sheet. You can easily keep track of headcount, models, and figures across multiple sheets.
• Integrate: Seamlessly connect with other tools and systems. Your data mart stays in sync, ensuring you always have the latest data from all your tools. Perfect for combining CRM and sales data.
• Export: With a simple click, export any of your tables to a spreadsheet. Ideal for sharing insights with your team or for one-off analysis.
• Extract: Link up with other applications, like business intelligence tools, to push out data for insightful, actionable results.
• Refresh: 1-click refresh of data in all connected places, keeping everything up-to-date. Great for real-time financial modelling or BI dashboard updates.
• Consolidate: Merge data from various locations into a single table. Imagine having all your historical data, forecasts, and plans in one accessible place for easy dashboarding and reporting.
• Transform: Use intuitive built-in no-code tools to clean and enrich your data.
• Get Technical : Use AI or SQL to perform hyper-advanced analysis on your data automatically.
• Scale Up: With Google's BigQuery powering your data mart, scalability becomes a non-issue. Handy for handling large influxes of data during peak business periods.
• Forecast: Utilize pre-built algorithms or connect/import spreadsheets to have all scenarios in one spot. Excellent for financial forecasting or scenario planning .
• Report: Keep all reports or models fresh with the latest data directly from the data mart. Ensure everyone's working with the most current data.
• Visualize: Data marts naturally connect to business intelligence and reporting tools. Visualize your sales trends or customer behavior patterns with ease.

Why FP&A and Rev Ops Choose a Personal Data Warehouse

Finance and Rev Ops teams often opt for a personal data warehouse over IT-owned data marts. This is because they handle data that can't be shared - such as salaries, future terminations, commission plans, and various scenarios. Further, they have reporting requirements and deadlines that differ from those of IT teams.

These teams need a system that consolidates data from any source into one location to maintain accuracy and consistency. It must be capable of managing large data volumes and scale smoothly with business growth. Additionally, the ability to instantly update reports and models in any spreadsheet, using the same underlying data, promotes collaboration and alignment within the team. Easy access to previous versions and scenarios, without the need to find files or remember initial assumptions, is a time-saver and reduces frustration.

When Finance or Rev Ops teams own the connection to spreadsheets and other tools via a personal data warehouse, they gain additional control over data security. They can make certain that users can't copy, unfilter, or export underlying data that may expose sensitive information. This includes highly confidential data such as salaries. This level of data security is not only crucial for maintaining privacy and compliance but also adds an extra layer of trust within the organization.

Benefits of a Personal Finance Data Mart

Managing financial data requires speed and simplicity. A personal finance data mart, designed with these needs in mind, offers several benefits that streamline financial team operations:

• Integration with Finance Tools : A key advantage of a personal finance data mart is its seamless integration with existing finance tools. Unlike traditional data warehouses and IT departments that often avoid spreadsheets, this solution embraces them, providing a bridge between these staple tools and the power of a data warehouse.
• Finance Ownership : With a personal finance data mart, finance teams control their processes and data. This autonomy eliminates waiting for IT to prioritize requests or navigate bureaucratic hurdles, enabling timely decision-making based on the most recent data.
• Independence from IT : Less reliance on IT allows finance teams to bypass common bottlenecks and dependencies that slow down their work. This independence leads to a more agile and responsive approach to financial management, with the ability to keep confidential information private.
• Repeatability and Scale : A personal finance data mart allows teams to establish repeatable processes that can be scaled as needed, ensuring accuracy and reliability in financial reporting and analysis, which provides a solid foundation for strategic decision-making.
• Long-Term Cost Efficiency : Traditional financial data management often involves significant long-term costs, including the need for additional personnel to handle incremental improvements. A personal finance data mart streamlines workflows and reduces manual interventions, offering a more cost-effective solution. It allows businesses to grow and evolve without being constrained by escalating costs and resource requirements.

A personal finance data mart is a strategic asset that empowers finance teams to work more intelligently, swiftly, and autonomously. It's an investment in efficiency, scalability, and long-term success.

Alternatives to a Personal Finance Data Mart

Alternative #1: delegating the work to it.

• Technical Expertise : IT departments possess a large team of highly technical team members.
• Integration with Company-Wide Systems : IT can manage the alignment with other company systems.
• Customization : IT departments with an analytical center of excellence can build virtually anything the finance team requests.
• Loss of Autonomy : Finance teams may experience delays in decision-making as they wait for IT to prioritize their requests.
• Limited Understanding of Finance Needs : IT might not fully understand the specific needs and nuances of financial data, which days projects or results as there's a large learning curve.
• Inflexibility : IT-driven systems might lack the agility and adaptability that finance teams require for ad hoc analysis and quick changes, especially as it relates to working with spreadsheets.
• Resource Intensive : IT departments can be a significant resource drain, requiring substantial funds and personnel to operate effectively.

Alternative #2: Maintaining the Status Quo

• No Immediate Cost : Keeping the status quo requires no upfront investment.
• Familiarity : Teams can continue using the systems and processes they are familiar with.
• No Training Required : Since employees are already familiar with the current system, no additional training is necessary.
• No Transition Period : There's no disruption or downtime associated with transitioning to a new system.
• Inefficiency : Persisting with outdated methods can lead to time-consuming manual work and increased likelihood of errors.
• Lack of Competitive Edge : Failure to modernize financial data management can place the company at a disadvantage compared to more flexible competitors.
• Limited Growth : Without the capacity to scale and adapt, finance teams may struggle to support the company's growth and changing needs.
• Data Silos : Existing systems may not facilitate easy data sharing or collaboration, leading to data silos and inconsistencies.

How to Get Started with a Personal Finance Data Mart: A Strategic Approach

Setting up a personal finance data mart is instant, but that doesn’t mean you shouldn’t have a plan. Here's a step-by-step guide to assist you:

• Time to Complete: <1 day
• Time to Complete: 1 day
• Time to Complete: 1-7 days
• Time to Complete: < 1 day

By following these steps, you can efficiently establish your personal finance data mart, enhancing the accuracy and efficiency of your financial reporting and analysis processes.

If you aren't convinced that a personal data mart is right for you, pick a tiny use case, like this one , to get started.

{{cta-component}}

Understanding the ELT Pipeline in a Personal Finance Data Mart

Think of your financial data as a network of rivers and streams, each carrying valuable information from diverse sources. In a personal finance data mart, we use an ELT (Extract, Load, Transform) pipeline to manage this data flow, ensuring it reaches its destination accurately and efficiently.

• Connect to Data Sources : We begin by connecting to various data sources, similar to tapping into different streams. These sources could be your sales data, inventory records, or financial statements, each flowing raw and unfiltered into our system.
• Load into the Database : Next, we channel all these streams into a single reservoir, our database. At this stage, the data is still raw, like the mixed water from different streams in a reservoir.
• Perform Transformations : Once the data is in the database, we start the purification process. We filter, clean, and transform the data, much like a treatment plant purifies water. This process ensures that the data is accurate, consistent, and ready for analysis.
• Consolidate or Separate Data Sets : Depending on the needs, we can either merge these purified data streams into a single information river or maintain them as separate channels. This flexibility allows us to tailor the data flow to best suit the analysis or reporting requirements.

The ELT framework's adaptability is its strength. If a data stream changes course or dries up, we can easily connect to a new source without disrupting the entire system. It's akin to having a purification process template that can be applied to any stream, ensuring consistent data quality, regardless of its source.

For non-technical users, this process is streamlined and user-friendly. You don't need to worry about the technical details of connecting to data sources or transforming the data. Your data mart platform handles all of that, allowing you to focus on analyzing the clean, consolidated data to make informed financial decisions. It's as simple as choosing which data streams to tap into and telling the system how to handle the rest.

Case Study: Innovapptive's Finance Data Mart Journey

The challenge:.

Our customer, Innovapptive, encountered significant challenges in managing their financial data. Their process was heavily dependent on numerous spreadsheets and Google Sheets, which were updated manually and dispersed across the organization. Historical data from various locations were manually inputted and the consolidation of information and reforecasting took weeks. Non-accounting data, such as customer numbers and headcount, were manually input into the reports themselves, turning each report into a new source of financial information. With data aggregation relying on formulas like SUMIF and forecasting at the hierarchy level, there was no capability to drill down or visualize the underlying details. Scenarios were kept in separate spreadsheets, turning comparison into a tedious task of sorting through old spreadsheets and formulas.

The Solution:

• Documentation & Automation: The notebook interface allows for documentation of a process along with the automation of the actual tasks. Think of it like documenting a procedure, but the document executes itself.
• Single Source of Truth for Accounting Data : We combined all actual accounting data from different systems into one source, providing transaction-level detail. The transformations were written in plain English and fully controlled by the finance team.
• Integration of Non-Accounting Historical Data : Data such as headcount, customers, historical pipeline, bookings, upsell, downsell, churn, and ratios were maintained in a simple Google Sheet. This sheet was linked to the data warehouse, allowing any new metrics added to be automatically added to the data warehouse and merged with the financial data.
• Extraction of Foundational Data : The most important data could now be extracted from the data mart into any spreadsheet, visualization tool, or report, ensuring consistency and accuracy.
• Unified Financial Models and Reports : Financial models and reports now extracted data from the data mart using simple formulas, pivot tables, tables, or charts and graphs. This eliminated the inconsistencies in methodologies that varied by spreadsheet and report.
• Data Validations: Buiness intelligence dashboards used to check for variances and anomalies were automatically populated upon import of a file. This allowed the analyst and CFO to quickly understand any changes long before the issue made it to a report.
• Forecast Integration : The forecast was directly loaded from the financial model into the data mart. Historical spreadsheet models, including underlying details and assumptions, were also loaded into the data mart.
• Comprehensive Data Accessibility : All historical data, historical operating plans, budgets, and the current forecast were now easily extractable from the same source. This data could be pulled into spreadsheets with pivot tables for ad-hoc analysis or into business intelligence tools like Power BI. All reports now automatically pulled from the same information source at the push of a refresh button.

Through our solution, Innovapptive transformed their financial data management process, achieving efficiency, consistency, and control. This case study demonstrates the power of a personal finance data mart in streamlining financial operations and providing actionable insights.

Innovapptive's Results

‍  Innovapptive now consolidates actuals, archives scenarios, analyzes variances, reforecasts, and refreshes dashboards and reports for their investors within a single day.

What is a personal finance data mart?

‍ A personal finance data mart is a streamlined platform that simplifies the management of financial data. It's like a central hub where you can import, clean, transform, and analyze data from various sources, all in one place.

How does it differ from a traditional data warehouse?

‍ Unlike traditional data warehouses, which are often complex and managed by IT departments, a personal finance data mart is user-friendly and designed for finance teams. It offers simplicity, autonomy, and direct control over your financial data.

Do I need IT expertise to set up a personal finance data mart?

‍ No, you don't need IT expertise. A personal finance data mart is designed to be easily set up and managed by finance professionals without requiring technical assistance.

Can I integrate my existing financial tools with a personal finance data mart?

‍ Yes, you can integrate your existing financial tools, such as spreadsheets and CRMs, with a personal finance data mart. This allows for seamless data flow and consolidation.

How does a personal finance data mart benefit finance teams?

‍ It offers several benefits, including faster access to data, streamlined reporting, the ability to handle large data volumes, and enhanced collaboration. It empowers finance teams to make informed decisions quickly and efficiently.

Is my data secure in a personal finance data mart?

‍ Yes, security is a top priority. Personal finance data marts use robust security measures to protect your data, ensuring it remains confidential and secure.

At Analyst Intelligence, we are backed by Google and create a completely separate Google data warehouse for your information. You even have the credentials to log in as if a multi-million dollar IT team set it up for you.

Can I perform forecasting and scenario analysis with a personal finance data mart?

‍ Absolutely. A personal finance data mart is ideal for forecasting and scenario analysis, providing you with the flexibility to explore different financial models and outcomes.

How long does it take to implement a personal finance data mart?

‍ Implementation time is instant. Simply upload a CSV or connect a spreadsheet and the infrastructure is instantly created behind the scenes. We call this "invisible infrastructure" because the technical complexity is completely removed.

How does a personal finance data mart handle data from multiple sources?

‍ It consolidates data from various sources into a single, centralized hub. This makes it easier to manage, analyze, and gain insights from your financial data.

What assistance is available for setting up the data mart and implementing best practices?

‍ Yes, we and most other companies offer analytical support to help you set up your personal finance data mart and guide you through best practices for managing and analyzing your data.

Analyst Intelligence offers real-time phone, email, and Slack support. We also provide best practices and guidance for a few hours per month free of charge.

How much does a personal finance data mart cost?

‍ The cost depends on your specific use case, data sizes, and other factors. However, for data under 1 million rows, it's likely around $1,000 per month with advanced support included.

How is a small company like yours able to offer this kind of solution?

‍ We collaborated with the Google Cloud team to make their Google BigQuery datawarehouse accessible. This partnership benefits Google by expanding their user base to include companies that are normally unable to access such advanced capabilities. In turn, it provides smaller companies with a turnkey solution that innovates at the pace of the most innovative company in the space.

Free 30-Minute Intro Call

You’re 30 minutes away from your no-code SQL generator with automated workflows.

Why do we have this call?

Understand your objectives and chart you a path to reaching them

Understand what apps you want access to and provide you with training and documentation

Make sure you're not a bot trying to gain access to a powerful tech stack to mine bitcoin

• Even more »

Account Options

• Try the new Google Books
• Advanced Book Search
• Elsevier.com
• Barnes&Noble.com
• Books-A-Million
• Find in a library
• All sellers  »

Selected pages

Other editions - View all

Common terms and phrases, references to this book, about the author  (2001), bibliographic information.