Business Analytics for Management Decision - Lecture by Rudra Pradhan

Course Plan

• 12 weeks course divided into several modules:
  • Week 1: Introduction to Business Analytics
  • Week 2: Exploring Data & Analytics on Spreadsheets
  • Weeks 3-12: Analytical modules
    • Weeks 3-5: Descriptive Analytics
    • Weeks 6-8: Predictive Analytics
    • Weeks 9-11: Prescriptive Analytics
    • Week 12: Decision Analytics

Week 1: Introduction to Business Analytics

Structure for Week 1

• Five Lectures:
  1. What is Business Analytics?
  2. Evolution of Business Analytics
  3. Classification of Business Analytics
  4. Trends in Business Analytics
  5. Framework & Scope of Business Analytics

Components Covered this Week

• Introduction to Business Analytics
• Evaluation of Business Analytics
• Classification of Business Analytics
• Trends & Framework of Business Analytics
• Scope of Business Analytics
• Data Understanding for Business Analytics
• Decision Models
• Problem-solving and Decision-making
• Management Decision using Analytics Tools

Key Concepts

Definition & Importance of Business Analytics

• Definition: Discovery and communication of meaningful patterns in data
  • Scientific process of transforming data into insights for decision making
• Importance: Helps in improving understanding, making better decisions, and enhancing business operations

Tools and Methods

• Key Attributes: Data, Information Technology, Statistical Analysis, Quantitative Methods, Mathematical and Computer-based Models
• Applications: Pricing decisions, Financial & Marketing activities, Supply Chain Management, Customer Relationship Management, HRM, ERP

Historical Trends

• Originates from time study exercises (early 1900s)
• Evolved through operations research, management science, and ICT
• Business Intelligence and Decision Support Systems
• Involvement of Personal Computers and Software

Types of Business Analytics

• Descriptive Analytics: Understanding past and present patterns
• Predictive Analytics: Analyzing past performance to predict future trends
• Prescriptive Analytics: Using optimization techniques to advise on possible outcomes and decisions

Practical Applications

• McDonald's: Uses data for customer satisfaction and profit enhancement
• Walmart: Uses data from social media and transaction history to segment and target customers
• Procter & Gamble: Optimizes product selection and pricing strategies
• Coca-Cola: Uses business analytics to predict consumer preferences
• Amazon & Financial Institutions: Security threat prediction and fraud detection

Conclusion

• Overall Goal: Use business analytics to make better decisions by connecting data, techniques, and specific business problems.
• Importance in Modern Industry: Critical for improving profitability, understanding customer needs, and staying competitive in various sectors.