Business Analytics and Data-driven Decision Making

About This Book

The organizing claim of this textbook is that an analysis which changes nobody's action is a cost rather than an asset, and the book applies that test to itself throughout. Four features follow from that decision. The first is a single running case: Tarangi Retail Private Limited, a forty-two store omnichannel grocery and general merchandise chain, supplies the data for most chapters, joined by Vaayu Telecom for customer churn, Meghdoot Finance for credit risk, and Nandini Foods for production and supply chain. All four are fictitious, and every figure attributed to them has been constructed for teaching. Because the context accumulates, the student sees a business rather than a sequence of unrelated exercises.

The second is that every numerical result has been computed independently and shown to the step, with self-assessment problems progressing from fully worked, through partially worked, to independent practice with complete solutions. The third is the treatment of tools: exact Excel function syntax, short Python snippets a manager should be able to read, and Power BI and Tableau described by workflow and by the decisions their models embed rather than by menu paths. The fourth is that governance and ethics are treated as part of the method, because a model that cannot lawfully be deployed is not a completed analysis.

The book assumes school-level mathematics and one prior exposure to statistics for management. It does not assume programming ability. Appendix A carries a capstone case-project brief with a marking rubric, Appendix B a consolidated formula sheet, Appendix C a glossary, and Appendix D a complete model question paper.

Syllabus Coverage

Unit I — Introduction to Business Analytics (8 sessions): concept, evolution and scope; the four types of analytics; the data-driven decision-making framework; data types, sources and quality issues; an introductory overview of Excel, Power BI, Tableau, Python and R.

Unit II — Descriptive Analytics and Data Visualization (8 sessions): central tendency and dispersion; visualization principles and dashboard design; exploratory data analysis and decision-oriented business reporting.

Unit III — Predictive Analytics (10 sessions): statistical foundations for business modelling; simple and multiple regression for forecasting; time series analysis and demand forecasting; classification and machine learning for business prediction.

Unit IV — Prescriptive Analytics and Optimization (8 sessions): decision analysis under uncertainty; linear programming for business decisions; Monte Carlo simulation, inventory analytics and pricing analytics across the supply chain.

Unit V — Applications, Ethics and Emerging Trends (6 sessions): analytics in marketing, human resources and finance; big data, business intelligence and artificial intelligence trends; data privacy, ethics and governance, including the Indian data protection framework; and the capstone business analytics case project.

Keywords

How to Cite

Saluja, R., Rastogi, V., & Patadiya, V. (2026). Business Analytics and Data-driven Decision Making (1st ed.). WiseLeaf Scientific Ventures. ISBN: 978-81-689588-9-0