1. Chapter 13 The Data Warehouse
Database Systems: Design, Implementation, and Management, Seventh Edition, Rob and Coronel

2. In this chapter, you will learn:
How operational data and decision support data differ
What a data warehouse is, how data for it are prepared, and how it is implemented
What star schemas are and how they are constructed
What data mining is and what role it plays in decision support
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

3. In this chapter, you will learn (continued):
What online analytical processing (OLAP) is
How SQL extensions are used to support OLAP-type data manipulations
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

4. The Need for Data Analysis
Managers must be able to track daily transactions to evaluate how the business is performing
By tapping into operational database, management can develop strategies to meet organizational goals
Data analysis can provide information about short-term tactical evaluations and strategies
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

5. The Need for Data Analysis (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

6. The Need for Data Analysis (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

7. Decision Support Systems
Decision support is methodology (or series of methodologies) designed to extract information from data and to use such information as a basis for decision making
Decision support system (DSS)
Arrangement of computerized tools used to assist managerial decision making within business
Usually requires extensive data “massaging” to produce information
Used at all levels within organization
Often tailored to focus on specific business areas
Provides ad hoc query tools to retrieve data and to display data in different formats
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

8. Decision Support Systems (continued)
Composed of following four main components:
Data store component
Basically a DSS database
Data extraction and data filtering component
Used to extract and validate data taken from operational database and external data sources
End-user query tool
Used to create queries that access database
End-user presentation tool
Used to organize and present data
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

9. Decision Support Systems (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

10. Operational Data vs. Decision Support Data
Mostly stored in relational database
Optimized to support transactions representing daily operations
DSS Data
Give tactical and strategic business meaning to operational data
Differs from operational data in following three main areas:
Timespan
Granularity
Dimensionality
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

11. Operational Data vs. Decision Support Data (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

12. Operational Data vs. Decision Support Data (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

13. DSS Database Requirements
A specialized DBMS tailored to provide fast answers to complex queries.
Four main requirements:
Database schema
Data extraction and loading
End-user analytical interface
Database size
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

14. DSS Database Requirements (continued)
Database schema
Must support complex data representations
Must contain aggregated and summarized data
Queries must be able to extract multidimensional time slices
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

15. DSS Database Requirements (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

16. DSS Database Requirements (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

17. DSS Database Requirements (continued)
Data extraction
Should allow batch and scheduled data extraction
Should support different data sources
Flat files
Hierarchical, network, and relational databases
Multiple vendors
Data filtering
Must allow checking for inconsistent data
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

18. DSS Database Requirements (continued)
End-user analytical interface
One of most critical DSS DBMS components
Permits user to navigate through data to simplify and accelerate decision-making process
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

19. DSS Database Requirements (continued)
Database size
In 2005, Wal-Mart had 260 terabytes of data in its data warehouses
DBMS must support very large databases (VLDBs)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

20. The Data Warehouse
Integrated, subject-oriented, time-variant, nonvolatile collection of data that provides support for decision making
Usually a read-only database optimized for data analysis and query processing
Requires time, money, and considerable managerial effort to create
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

21. The Data Warehouse (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

22. The Data Warehouse (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

23. The Data Warehouse (continued)
Data mart
Small, single-subject data warehouse subset
Each is more manageable data set than data warehouse
Provides decision support to small group of people
Typically lower cost and lower implementation time than data warehouse
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

24. DSS Architectural Styles
Several are available
Provide advanced decision support features
Some capable of providing access to multidimensional data analysis
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

25. DSS Architectural Styles (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

26. Twelve Rules that Define a Data Warehouse
Data warehouse and operational environments are separated
Data warehouse data are integrated
Data warehouse contains historical data over long time horizon
Data warehouse data are snapshot data captured at given point in time
Data warehouse data are subject oriented
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

27. Twelve Rules that Define a Data Warehouse (continued)
Data warehouse data are mainly read-only with periodic batch updates from operational data
No online updates allowed
Data warehouse development life cycle differs from classical systems development
Data warehouse contains data with several levels of detail: current detail data, old detail data, lightly summarized data, and highly summarized data
Data warehouse environment is characterized by read-only transactions to very large data sets
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

28. Twelve Rules that Define a Data Warehouse (continued)
Data warehouse environment has system that traces data sources, transformations, and storage
Data warehouse’s metadata are critical component of this environment
Data warehouse contains chargeback mechanism for resource usage that enforces optimal use of data by end users
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

29. Online Analytical Processing
Advanced data analysis environment that supports decision making, business modeling, and operations research
OLAP systems share four main characteristics:
Use multidimensional data analysis techniques
Provide advanced database support
Provide easy-to-use end-user interfaces
Support client/server architecture
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

30. Multidimensional Data Analysis Techniques
Data are processed and viewed as part of a multidimensional structure
Particularly attractive to business decision makers
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

31. Multidimensional Data Analysis Techniques (continued)
Augmented by following functions:
Advanced data presentation functions
Advanced data aggregation, consolidation and classification functions
Advanced computational functions
Advanced data modeling functions
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel