1. Data Warehousing Building a database to support the decision making activities of a department or business unit

2. Data Warehouse A read-only database for decision analysis –Subject Oriented –Integrated –Time variant –Nonvolatile consisting of time stamped operational and external data.

3. Data Warehouse Purpose zIdentify problems in time to avoid them zLocate opportunities you might otherwise miss

4. Data Warehouse: New Approach An old idea with a new interest because of: Cheap Computing Power Special Purpose Hardware New Data Structures Intelligent Software

5. Warehousing Problems Business Issues Data Quantity Data Accuracy Maintenance Ownership Cost

6. Warehousing Problems Business Issues Database Issues DBMS Software Technology Complexity

7. Business Issues Data Issues Analysis Issues User Interface Intelligent Processing Warehousing Problems

8. EUC Data Architecture Enterprise Data Warehouse Data Marts Business Packages

9. Needed Environment zUnderstand DSS technology zUnderstand relational databases zKnow company data resources zTransformation and integration tools zPossess DBA skills zInstall network and telecommunication hardware and software zPossess front end software zPossess meta-data navigation tools

10. Two Approaches zClassical Enterprise Database Typically contains operational data that integrates information from all areas of the organization. zData Mart Extracted and managerial support data designed for departmental or EUC applications

11. Data Warehouse vs Operational Databases zHighly tuned zReal time Data zDetailed records zCurrent values zAccesses small amounts of data in a predictable manner z Flexible access z Consistent timing z Summarized as appropriate z Historical z Access large amounts of data in unexpected ways

13. Separate Read Only Database Operational and decision support processing are fundamentally different. zFlexible access zConsistent timing zHistorical data zEnvironmental references zCommon reference

14. Front End Access Tailored access programs in user form, usually client-server zGeneral purpose GUI products (e.g. Access, PowerBuilder) zCustom access routines

15. Development Cycles Enterprise Traditional SDLC Requirements Driven Warehouse Iterative Development Data Driven

16. Data Warehouse A database for departmental decision support. Contains detail data brought in through a feed and clean process. zData extracted from source files zData is integrated and transformed zData resides in a read-only database zUser access via a front end tool or application

17. Data Warehouse Design Basics Must provide flexible access, “what if” processing, and extensive reporting on data subsets. zCan tables be scanned in a reasonable time zCan indexes be added as needed. If not, consider partitioning or summary tables

18. Data Warehouse Design Basics zIterative Development with only part of the warehouse built at one time zUsed and modified with frequent user feedback and modification 1.Implement; test bias & completeness 2.Create and execute applications 3.Identify Requirements 4.Iterate

19. Data Development Roles zData Analyst: Works with user groups to understand business and technical requirements. zData Architect: Manages company data. Must understand business needs as well as data implications.

20. Analyst-Architect Communication Requirements zFormal lines of communication zData warehouse council that meets monthly zAnalyst and architect retreat zFormally identify analysts and architects zEnsure comparable levels of personnel zBoth understand business requirements zArchitect goals available to analysts zAnalyst strategy and issues available to architects

21. Data Warehouse Design Issues Small systems are easier to manage than large ones. Adding attributes is easier than changing or deleting them.

22. Data WarehouseDesign: Proposed Changes zWill the change disrupt the current system? zWill the change add significantly more detail? zWill the change disregard existing data? zWill the change add a new table or change an existing one? zWhat granularity? zDoes it fit the current data model? zHow much programming is required? zHow many resources will it consume?

23. Data Warehouses Extracted Data Clean and feed. zInternal data ySummarized yHistorical yAccuracy yIntegration zExternal data zGenerated data

24. Data Warehouse Extraction Issues Internal data often comes from separate operational databases. zReconcile formats zRemove intelligent codes zVerify accuracy zUnify time stamps

25. OLAP and MDBMS zOnline Analytic Processing Primarily relational models from which targeted extractions can be developed Complex for users zMultidimensional DBMS Pre-modeled data cubes for efficient access Complex for design

26. References zV. Poe, Data Warehouse: Architecture is not Infrastructure, Database Programming and Design, July 1995 zJ. Bischoff, Achieving Warehouse Success, Database Programming and Design, July, 1994. zW. Inmon, For Managers Only - A Tale of Two Cycles: Iterative development in the information warehouse environment, Database Programming and Design, Dec 1991.