1. 13 Chapter 13 The Data Warehouse Hachim Haddouti

2. 13 2 Hachim Haddouti and Rob & Coronel, Ch13 In this chapter, you will learn: How operational data and decision support differ What a data warehouse is and how its data are prepared What star schemas are and how they are constructed ROLAP, MOLAP What data mining is and what role it plays in decision support

3. 13 3 Hachim Haddouti and Rob & Coronel, Ch13 External and internal forces require tactical and strategic decisions Search for competitive advantage Business environments are dynamic Decision-making cycle time is reduced Different managers require different decision support systems (DSS) The Need for Data Analysis

4. 13 4 Hachim Haddouti and Rob & Coronel, Ch13 Decision Support –Is a methodology –Extracts information from data –Uses information as basis for decision making Decision Support Systems

5. 13 5 Hachim Haddouti and Rob & Coronel, Ch13 Decision Support Systems Decision support system (DSS) –Arrangement of computerized tools –Used to assist managerial decision –Extensive data “massaging” to produce information –Used at all levels in organization –Tailored to focus on specific areas and needs –Interactive –Provides ad hoc query tools

6. 13 6 Hachim Haddouti and Rob & Coronel, Ch13 DSS Components

7. 13 7 Hachim Haddouti and Rob & Coronel, Ch13 Operational data –Relational, normalized database –Optimized to support transactions –Real time updates DSS –Snapshot of operational data –Summarized –Large amounts of data Data analyst viewpoint –Timespan –Granularity –Dimensionality Operational vs. Decision Support Data

8. 13 8 Hachim Haddouti and Rob & Coronel, Ch13 MIS (=Manage- ment Informa- tionssystem) MAIS (=Marke- ting Informations- system) 60' 70'80', Begin 90'Mid 90' DSS (=Decision Support System) EIS (=Executive Information System) Data-Ware- housesystem EIS (=Enter- prise Intelligence System) IDF (=Informa- tion Delivery Facility) Information Warehouse EIS (=Enter- prise Information System) Unchanged Vision: right information to the right time and place History

9. 13 9 Hachim Haddouti and Rob & Coronel, Ch13 Integrated –Centralized –Holds data retrieved from entire organization Subject-Oriented –Optimized to give answers to diverse questions –Used by all functional areas Time Variant –Flow of data through time –Projected data Non-Volatile –Data never removed –Always growing Data Warehouse

10. 13 10 Hachim Haddouti and Rob & Coronel, Ch13 Creating a Data Warehouse

11. 13 11 Hachim Haddouti and Rob & Coronel, Ch13 PurchaseStoragePersonnel Financial Sales CustomerSupllier Market competition Internal Information Sources External information sources Data Warehouse Analyzes, Trends Berichte Data Warehouse Shape

12. 13 12 Hachim Haddouti and Rob & Coronel, Ch13 Single-subject data warehouse subset Decision support to small group Can be test for exploring potential benefits of Data warehouses Address local or departmental problems Data Marts

13. 13 Hachim Haddouti and Rob & Coronel, Ch13 1.Separated from operational environment 2.Data are integrated 3.Contains historical data over long time horizon 4.Snapshot data captured at given time 5.Subject-oriented data 6.Mainly read-only data with periodic batch updates from operational source, no online updates 7.Development life cycle differs from classical one, data driven not process driven Twelve Data Warehouse Rules

14. 13 14 Hachim Haddouti and Rob & Coronel, Ch13 8. Contains different levels of data detail –Current and old detail –Lightly and highly summarized 9. Characterized by read-only transactions to large data sets 10. Environment has system to trace data resources, transformation, and storage 11. Metadata critical components –Identify and define data elements –Provide the source, transformation, integration, storage, usage, relationships, and history of data elements 12. Contains charge-back mechanism for usage –Enforces optimal use of data Twelve Data Warehouse Rules (Con’t.)

15. 13 15 Hachim Haddouti and Rob & Coronel, Ch13 Advanced data analysis environment Supports decision making, business modeling, and operations research activities Characteristics of OLAP –Use multidimensional data analysis techniques –Provide advanced database support –Provide easy-to-use end-user interfaces –Support client/server architecture Online Analytical Processing (OLAP)

16. 13 16 Hachim Haddouti and Rob & Coronel, Ch13 OLAP Client/Server Architecture

17. 13 17 Hachim Haddouti and Rob & Coronel, Ch13 OLAP Server Arrangement

18. 13 18 Hachim Haddouti and Rob & Coronel, Ch13 OLAP Server with Multidimensional Data Store Arrangement

19. 13 19 Hachim Haddouti and Rob & Coronel, Ch13 OLAP Server with Local Mini-Data-Marts

20. 13 20 Hachim Haddouti and Rob & Coronel, Ch13 OLAP functionality Uses relational DB query tools Extensions to RDBMS –Multidimensional data schema support –Data access language and query performance optimized for multidimensional data –Support for very large databases (VLDBs) Relational OLAP (ROLAP)

21. 13 21 Hachim Haddouti and Rob & Coronel, Ch13 Typical ROLAP Client/Server Architecture

22. 13 22 Hachim Haddouti and Rob & Coronel, Ch13 OLAP functionality to multidimensional databases (MDBMS) Stored data in multidimensional data cube N-dimensional cubes called hypercubes Cube cache memory speeds processing Affected by how the database system handles density of data cube called sparsity Multidimensional OLAP (MOLAP)

23. 13 23 Hachim Haddouti and Rob & Coronel, Ch13 MOLAP Client/Server Architecture

24. 13 24 Hachim Haddouti and Rob & Coronel, Ch13 Data-modeling technique Maps multidimensional decision support into relational database Yield model for multidimensional data analysis while preserving relational structure of operational DB Four Components: –Facts –Dimensions –Attributes –Attribute hierarchies Star Schema

25. 13 25 Hachim Haddouti and Rob & Coronel, Ch13 Simple Star Schema Figure 13.12

26. 13 26 Hachim Haddouti and Rob & Coronel, Ch13 Slice and Dice View of Sales

27. 13 27 Hachim Haddouti and Rob & Coronel, Ch13 Facts and dimensions represented by physical tables in data warehouse DB Fact table related to each dimension table (M:1) Fact and dimension tables related by foreign keys Subject to the primary/foreign key constraints Star Schema Representation

28. 13 28 Hachim Haddouti and Rob & Coronel, Ch13 Star Schema for Sales

29. 13 29 Hachim Haddouti and Rob & Coronel, Ch13 Seeks to discover unknown data characteristics Automatically searches data for anomalies and relationships Data mining tools –Analyze data –Uncover problems or opportunities –Form computer models based on findings –Predict business behavior with models –Require minimal end-user intervention Data Mining

30. 13 30 Hachim Haddouti and Rob & Coronel, Ch13 Extraction of Knowledge from Data

31. 13 31 Hachim Haddouti and Rob & Coronel, Ch13 Example 1

32. 13 32 Hachim Haddouti and Rob & Coronel, Ch13 Example 2