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DW (III): The Wal-mart Example

Explore Walmart's data warehouse evolution from the 1970s to the 2000s, types of DW models used, and its impact on operations. Learn how detailed data enabled Walmart's success and the various DW applications in action.

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DW (III): The Wal-mart Example

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  1. DW (III): The Wal-mart Example Developed by: Dr Eddie Ip Modified by: Dr Arif Ansari

  2. Outline • Wal-mart case • Evolution of its DW • Several types of DW models • Apply the concept at Wal-mart • What kind of information can the Walmart DW produce?

  3. Objective: to see the world’s largest & most successful DW in action • How do they get there • What information can the DW produce • How does it impact operation

  4. Wal-mart: Background • Annual revenue 150B • 4,000 stores, 800,000 employees • High growth but faces stiff competition • Realize that they have to manage “one store at a time” to meet divergent needs • Data on averages are not enough

  5. Wal-mart: Background • Randy Mott (CIO, Wal-mart): very detailed data, not summary data, is the key to success • Information support enables “Every store to operate as a local store.”

  6. Walmart: Background • Leader in DW and gain huge competitive advantage by leveraging its IS • E.g. 96% percentage in-stock, one of highest in industry • 1990 : ~700G POS data • 1999: 101 Terabytes on NCR’s Teradata • 50+ applications running

  7. The Wal-mart Case • Evolution leading up to DW: 1970 -- 2000 Automation Controlling Informative

  8. Wal-mart • Automation – e.g. scanning bar codes • Controlling – e.g. inventory control • Informative – e.g. simple summary statistics from OLTP >> EIS (executive information system)

  9. Wal-mart • Pre-DW : request directly from DB could cause performance problem (e.g. order entry delayed) PC Use SQL to extract information Operational DB

  10. Wal-mart • Process laborious • E.g. planning group BIM (basic inventory management) : create SQL, download files in small files, (wait), import into spreadsheet, and perform forecast. • Must overcome data quality problem (e.g. data consistency across platforms)

  11. Wal-mart • First creation of DW (around 1990) DW Orders Article DSS Receiving

  12. 5 Types of DW • DW • Data mart • Enterprise DW • Operational Data Store (ODS) • Federated DW Historically, Walmart has developed DW, Dmart & EDW

  13. Data mart • POS basket-level analysis data mart • DB2>VM/SQL>Teradata • 1.2 B records in POS • Different data marts exist before Enterprise DW for historical reasons

  14. Data mart Data marts DW Orders store Article dept Receiving

  15. Data mart • Pros • Accommodate different information needs • E.g. manufacturing and distributing divisions • Restrict information access (privacy) • Enhance performance by taking load off DW • Cons • Consistency • No cross boundary information access

  16. Enterprise DW • Company wide DW • 99% of Wal-mart’s data captured in EDW • Maintain 65 weeks of data • Closed loop concept • E.g. new article allocation. Use similar article sales data > analyze and estimate size > feed back into purchasing order (transaction) system

  17. Wal-mart • DW is a DSS, not a functional system • E.g. merchandise manager review a historic chart helps her identify inventory problem • Better replenishing and allocation lead to higher profit

  18. Wal-mart • Retail enterprise DW (p. 27) • Multi-function decision support • Cyclical feedback loop • Planning • Allocation • Dynamic purchasing • Replensishment • Category management

  19. Wal-mart • More sophisticated analysis when customer data are available • E.g. Sam’s club • Data as much as Wal-mart’s • Number of customers only a quarter of Wal-mart’s

  20. Example of DW app: Store Sales Analyses • Examples of reports • Example 1: Store sales report of region 2 district 10 week 9634 (8 stores) • Store size • Retail sales • Customer count • Average customer transaction

  21. Report Example 1 • Data elements • Store information • Store ID, name/address, ZIP, manager, sqft, open date, remodel date, #fuel pumps… • Daily store sales information • Store identifer, year, week, date, sales, customer count (note: detail is down to day), fuel sales, labor costs, …

  22. Example 2 Report • Comparable store sales • Extend on report 1, include sales from last year and figures of comparison (% increase/decrease on each attribute) • New data elements • Data from comparable weeks from last year (must therefore keep at least 53 weeks of data)

  23. Other store level reports • Planned sales versus actual sales report • Flash sales report (by the hour!) • E.g., wine sales usually increase after 5pm. If one store consistently showed a decline after 5, then there could be problems such as not stocking properly • Departmental sales report

  24. Summary • Wal-mart Case study • Evolution of DW at Wal-mart • Types of DW • Examples of DW applications

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