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Data Warehouse (I): Introduction

Data Warehouse (I): Introduction. Lecture 2 Developed by: Dr Eddie Ip Modified By: Dr Arif Ansari. Overview. Functions of DW Definition & characteristics of DW Why DW and why it is possible now Business perspective Technical perspective Examples of analytical functions of DW

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Data Warehouse (I): Introduction

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  1. Data Warehouse (I): Introduction Lecture 2 Developed by: Dr Eddie Ip Modified By: Dr Arif Ansari

  2. Overview • Functions of DW • Definition & characteristics of DW • Why DW and why it is possible now • Business perspective • Technical perspective • Examples of analytical functions of DW • Evolution of DW

  3. Functions of a DW • Creates a physical separation between the collection of daily transaction data and a copy of it. Use copy for analytical purposes • Avoid engineering conflict • Challenges • E.g., Scattered data create political & technical challenges

  4. Definition of DW • “A collection of integrated, subject-oriented databases designed to supply the information required for decision-making.” - W. Inmon (1992)

  5. Integrated • From many operational systems, or OLTP’s(= On-Line-Transaction-Processing) to form an integrated view of the customer

  6. OLTP • Handle a business’s daily activities & commerce • “Bread and butter” activities

  7. OLTP • ATM, airline reservation, catalog order, supermarket (bar-code data)

  8. OLTP • Can think of it as “A data storage with blinking data items” (regular and frequent updating activities)

  9. OLTP • Based on well-defined business & technical requirement • Large volumes of simple transactions • Rigid specs • Maintained by IT professionals • Process oriented

  10. OLTP • Store in relational database tables or hierarchical files • Supports many users • Frequently updated

  11. OLTP • Does not support • analysis • ad hoc query & reporting • multiple platform • evolution

  12. OLTP

  13. Subject Oriented • Subject oriented (DW) vs process oriented (OLTP) • Subjects: customers, sales, profits

  14. Databases • DW requires a large repository (DB) • DW = internal DB + external DB + metadata DB

  15. Decision Making • Dimensional view of data • Start from a high level (summary data) & drill down to more detail to answer specific questions • Function similar to Executive Information System (EIS)

  16. Characteristics of DW • DW takes a snap-shot of operation & stores it away • Allows trend/ pattern analysis • Read-only

  17. Characteristics of DW • DW stores atomic & lightly summarized data • Summarized = aggregates • Trade-off consideration • performance • cost

  18. Why DW? • Business perspective • Quick decision

  19. Why DW? • Quick decisions • “The ultimate goal is simple: Give the battlefield commander access to all the information needed to win the war. And give it to him when he wants it, where he wants he and how he wants it.”-- Gen. Colin L. Powell, “Information Warriors,” BYTE, 1992

  20. Why DW? • A single, integrated view of customer • Data integrated from across, and even outside organization

  21. Why DW? • Business perspective • Users are business domain experts, not computer professionals • Leverage fast growing data assets (new source of information, new way of looking) • Competitive edge, e.g. mass customization, operational excellence • Infrastructure forms backbone in supporting relationship management*

  22. Why DW? • Technological perspective • Information scattering (stovepipe DB) • Price of computing dropping • Network bandwidth increasing • Advances in technology, e.g. web, DM tools, distributed computing • Integration of legacy systems • Security enhancement

  23. DW : summary

  24. Analytical functions • Examples • Sales analysis : sales analysis, inventory analysis, identify inactive customers • Financial analysis: review past cash flow trends & forecast future needs, real-time financial reporting

  25. Evolution of DW as a decision support tool • Long history of analytic system • 1970 DSS = decision support systems • 1980 EIS = executive information systems • Reporting system for senior managers

  26. EIS example • Frito-Lay

  27. Pre-DW DSS • Lack DB component • Extract programs subset files (predetermined criteria) • Problems: reliable joins, synchronization, credibility, consistency • Process controlled by IT, not business users • Silos (one OLTP, one DSS)

  28. Modern DSS Example • Stock Screen • Used by security analysts to inform & support business decision

  29. Modern DSS Example • A simple web-based mutual fund screen • Charles Schwab

  30. The Ins and Outs of DW • OLTP bring data in • DSS draw data out • Many still consider DW a kind of DSS

  31. Summary • DW is physically separated from OLTP • DW provides the basis (memory, analytics) to achieve enterprise intelligence • Business & technical requirements (e.g., user interface, subject orientation) • DW is a kind of DSS

  32. Glossary • DW: Data Warehousing • OLTP: Online Transaction Processing • EIS: Executive Information System • DSS: Decision Support System

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