BI and Data Warehousing in the Real World

Setting the Stage

  • Introduction to Business Intelligence (BI) and Data Warehousing (DW)
  • Importance of integrating BI with data warehousing for effective architectural design

Key Propositions

  • Architectural Integration: BI and data warehousing work best when designed together.
  • Evolution of Systems: Initial setups often require refinements over time; a strong foundation allows for easier adjustments.
  • Continuous Development: Both BI capabilities and data warehouses will evolve as business needs change.

Scenario Overview

  • Business Context: Focus on an online nutrition/packaged food company.
  • Objective: Measure and manage the "Perfect Order Index" (POI).
  • Impact of Order Accuracy:
    • Affects customer satisfaction, retention, and profitability.
    • Interlinks with call center operations and business processes.
    • Influences staffing at distribution centers and reduces product waste.

Business Rules for Orders

  • Customers can specify permissible substitutions for out-of-stock items:
    • Example: "If the healthy chocolate chip cookies are out, okay with oatmeal raisin cookies."
  • Limitation: Only substitutions chosen by the customer are allowed, disallowing unauthorized changes by staff.

Problems Impacting Order Accuracy

  • Key Issues:
    • Missing items
    • Incorrect items
  • Query: Where are these packing errors detected in real operations?

BI and Data Warehousing Planning

  • Data Sources Overview:
    • Detailed orders, customer substitutions, master customer/product/distribution lists, order packing history, and customer complaints from various channels.

Steps for BI Functionality Design

  1. Identify BI Requirements:

    • Develop reports, visualizations, and dashboards that align with business processes.
    • Analyze current vs future states; aim to quantify these differences.

  2. Functionality Components:

    • Perfect Order Index (POI): Track data trends and identify areas for analysis.
    • Customer demographics reporting issues.
    • Product issue trends and resolution durations.

  3. Data Audit:

    • Assess available data sources and ensure completeness for BI needs:
      • Detailed orders, customer data, product lists, order packing history, complaint logs.

  4. Report Design:

    • Outline desired data, layout, calculations, and business rules for reporting purposes.

Key Performance Indicator: Perfect Order Index (POI)

  • Role of POI:
    • Serves as a KPI vital for operational success.
    • Regularly reviewed by company executives and distribution directors to assess performance.
  • Data Visualizations:
    • Dashboard presenting POI with slices by distribution center, historical measurements, and comparison against targets.

Report Visualization Insights

  • Use maps and tables to represent customer complaints and product mispack reports over given periods (e.g., Jan 2023).

ETL (Extract, Transform, Load) Design Mechanics

  1. Defining Key Dimensions:

    • Customer Dimension: Continually update customer information based on enrollment and engagement.
    • Product Dimension: Track changes in product details without deleting discontinued items.
    • Distribution Center Dimension: Update as new centers are opened but reconsider necessity for refreshes.

  2. Geography and Time Dimensions:

    • Utilize standard sources for these dimensions while maintaining existing data relevance.

  3. Key Facts/Measurements for BI:

    • Define detailed orders, customer complaints, and packing processes in terms of measurable facts for analysis.