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Business Intelligence

Syllabus

Lectures

  • Introduction to Business Intelligence
  • Organization of Business Intelligence
    • Knowledge of the enterprise
    • Limits of siloed approaches
    • BI competency centers and BI jobs
    • Meetings with BI practitioners
  • Methodology in Business Intelligence
    • Standard project methodology
    • Standard project toolbox
  • Limits of project management in BI
    • Lean principles
    • Agile methods
    • Hands-on sessions
  • Fundamentals of Business Intelligence
    • Knowledge pyramid
    • Data integration
      • Master Data Management
      • Enterprise Application Integration
      • Extract, Transform, Load
      • Data Hubs and Data Mesh
    • Data Modeling
      • Entity-Relation vs. On-Line Analytical Processing (OLAP)
      • Normalization of the Data Warehouse (third normal form)
      • Denormalization of the Data Marts (stars and snowflakes)
      • Top down approach (Bill Inmon)
      • Bottom up approach (Ralph Kimball)
    • Data presentation
      • Relational OLAP
      • MultidimensionalHybrid OLAP
      • Desktop OLAP
      • Case study
  • Segmentation of Business Intelligence
    • Level 0: pull mode
      • Self-service BI and agile BI
      • Data visualization and data discovery
      • Data blending on the fly vs. in-memory
      • BI mockups and BI prototyping
      • Case study and market vendors
    • Level 1: push mode
      • Reporting (operational, corporate, pixel perfect)
      • Dashboards, scorecards, indicators
      • Alerting and mobile BI (responsive design)
      • Operational BI (ERP-embedded)
      • Case study and market vendors
    • Level 2: analysis mode
      • Multidimensional cube
      • Slice, dice, and point of view
      • Drill anywhere (up, down, to and through)
      • Analytical BI (EPM-oriented)
      • Case study and market vendors
    • Enterprise Performance Management (EPM)
      • Planning
      • Budgeting
      • Gap analysis
      • Simulation and reforecast
      • Case study and market vendors
    • Extension to Big Data
      • 3V of Big Data (volume, variety and velocity)
      • Data Lake
      • Introduction to Data Science and Advanced Analytics
      • Initiation to R
      • Case study and market vendors
    • Applications and use cases
      • Research, Development and Production
      • Supply Chain, Logistics and Procurement
      • Marketing, Sales and Commerce
      • Finance, Control and Audit
      • Human Resources

Projects

  • BI Methodology Applied
  • BI Segmentation Applied

Exam

Mode

25% final test:

  • on 4 core modules described previously
  • 3-hour written examination
  • individual assessment of theoretical knowledge
  • questions and exercises (no quiz or multiple choice questions)
  • 2 lines should be enough for each question

25% oral presentations:

  • on applied BI segmentation
  • creation of an e-learning video module in groups (5 to 6 students)
  • in 4 different presentations (Business Intelligence and Business Analytics)
  • on 1 given functional domain per group (industry, commerce, finance, etc)

50% group project:

  • on BI applied methodology
  • creation of a BI application in groups (5 to 6 students) in agile methodology
  • with a market-leading BI software (MicroStrategy Desktop)
  • on open data or enterprise data
Authors: Giacomo