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MBA in Business Analytics & Real life Case study Ft. Dr. Jayatu Sen, Prof. Great Lakes
June 10 2024

An MBA in Business Analytics is a postgraduate program that combines traditional business fundamentals with a strong focus on data analysis, statistics, and other analytical techniques. This equips graduates with the skills to translate data into actionable insights that can be used to drive better business decisions.

Here's what an MBA in Business Analytics typically covers:

  • Business Core: Courses in finance, accounting, marketing, operations management, and organizational behavior provide a solid understanding of how businesses function.
  • Data Analytics: You'll learn data mining, data visualization, statistical modeling, machine learning, and other tools to extract meaning from data.
  • Business Applications: These courses bridge the gap between data and business, teaching you how to use analytics to solve real-world problems in areas like marketing, finance, and supply chain management.

Skills Developed

  • Analytical Thinking: Ability to analyze complex data sets and derive actionable insights.
  • Decision Making: Using data to inform and support business decisions.
  • Problem Solving: Addressing business challenges through data-driven solutions.
  • Technical Proficiency: Mastery of analytical tools and technologies.
  • Communication: Presenting data insights to non-technical stakeholders in a clear and concise manner.

Benefits of an MBA in Business Analytics:

  • High Demand: With the rise of big data, there is a growing demand for professionals who can analyze and interpret data.
  • Interdisciplinary Skills: Combines business acumen with technical expertise, making graduates versatile in the job market.
  • Strategic Advantage: Equips professionals to make data-driven decisions, providing a competitive edge in the business world.

Career Opportunities

Graduates with an MBA in Business Analytics can pursue a wide range of careers, including:

  • Business Analyst: Analyzing business processes and recommending data-driven improvements.
  • Data Scientist: Developing algorithms and models to interpret large datasets.
  • Data Analyst: Interpreting data to support business decisions.
  • Consultant: Advising companies on data strategies and implementations.
  • Marketing Analyst: Analyzing market data to guide marketing strategies.
  • Operations Analyst: Using data to optimize business operations and supply chains.

Real-Life Case Study: Management with Business Analytics

Business analytics is a critical component for modern organizations seeking to leverage data for strategic decision-making. This case study focuses on how an MBA in Business Analytics was applied in a real-world scenario to transform a company’s operations, enhance decision-making processes, and drive business growth.

MBA Graduate's Role

An MBA graduate specialized in Business Analytics, Sarah Johnson, was hired to lead the transformation project. Her role involved:

  • Identifying key business problems through data analysis.
  • Implementing advanced analytical techniques to derive actionable insights.
  • Recommending data-driven strategies to improve business operations.


Step 1: Data Collection and Integration

Sarah started by consolidating data from various sources including sales, inventory, customer feedback, and operational reports. She used ETL (Extract, Transform, Load) tools to integrate data into a centralized data warehouse.

Step 2: Descriptive Analytics

She performed descriptive analytics to understand the current state of the business. This included:

  • Sales trend analysis.
  • Inventory turnover ratios.
  • Customer segmentation based on purchase history.

Step 3: Predictive Analytics

Using predictive analytics, Sarah developed models to forecast future sales, predict stockouts, and identify potential high-value customers. Techniques used included:

  • Regression analysis for sales forecasting.
  • Classification models to predict stockouts.
  • Clustering algorithms for customer segmentation.

Step 4: Prescriptive Analytics

Prescriptive analytics provided actionable recommendations to optimize business processes:

  • Inventory Management: Implementing an automated inventory replenishment system based on predictive demand forecasting.
  • Customer Retention: Developing personalized marketing campaigns targeting high-value customers and those at risk of churning.
  • Operational Efficiency: Streamlining supply chain operations to reduce lead times and operational costs.


Within a year of implementing the analytics-driven strategies, RetailMart Inc. experienced significant improvements:

  1. Inventory Management: Stockouts were reduced by 30%, and excess inventory was cut by 25%, resulting in a 15% reduction in holding costs.
  2. Customer Retention: Customer loyalty programs and personalized marketing increased repeat purchase rates by 20%.
  3. Operational Efficiency: Enhanced supply chain operations led to a 10% reduction in operational costs.


The application of business analytics through the expertise of an MBA graduate in Business Analytics transformed RetailMart Inc. by providing data-driven insights that improved inventory management, customer retention, and operational efficiency. This case study highlights the value of business analytics in making informed decisions and driving business growth.

Rahul Singh

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