1. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-1 Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition

2. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-2 Learning Objectives Describe the issues in management of data. Understand the concepts and use of DBMS. Learn about data warehousing and data marts. Explain business intelligence/business analytics. Examine how decision making can be improved through data manipulation and analytics. Understand the interaction between the Web and database technologies. Explain how database technologies are used in business analytics. Understand the impact of the Web on business intelligence and analytics.

3. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Many organizations have amassed vast amounts of data that employees use to unlock valuable secrets to enable the organization to compete successfully. Some organizations do this extremely well, but others are quite ineffective. To use analytic tools to improve organization decision making, a foundation data architecture & enterprise architecture must be in place to facilitate effective decision analysis. Enabling decision analysis through access to all relevant information is known as BI which include data warehouse, online analytical processing, data mining, visualization and multidimensionality. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-3

4. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-4 Information Sharing a Principle Component of the National Strategy for Homeland Security Vignette Network of systems that provide knowledge integration and distribution Horizontal and vertical information sharing Improved communications Mining of data stored in Web-enabled warehouse

5. Data warehouse provide a strategic data architecture to enable decision support analysis. It enables data mining, the ability to automatically synthesize vast amounts of information in order to discover the hidden truth. It has been emerged in the form of web enabled data warehouse as a direct response to the Sep 11, 2001. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-5 Information Sharing a Principle Component of the National Strategy for Homeland Security Vignette

6. The main purpose was to build a national environment that enables the sharing of essential homeland security information. We must build a system of systems that can provide the right information to the right people at all times. Information will be shared “horizontally” across each level of government and “vertically” among: 1.Federal government. 2.State government. 3.Local government. 4.Private industry. 5.Citizens. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-6 Information Sharing a Principle Component of the National Strategy for Homeland Security Vignette

7. The five major initiatives that are identified within the strategy include: 1.To integrate information sharing across the federal government. 2.To extend the integration of information sharing across the vertical levels. 3.To adopt common metadata standards of electronic information relevant to homeland security. 4.To improve public safety communication. 5.To ensure reliable public health information. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-7 Information Sharing a Principle Component of the National Strategy for Homeland Security Vignette

8. The Nature & Source of data In order to understand a situation, a decision- maker needs data, information, and knowledge. This must be integrated and organized in a manner that makes them useful. Then the decision maker must be able to apply analysis tool processing so that data, information and knowledge can be utilized to full benefit. In the right hands, these tools provide great decision makers with great capabilities. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-8

9. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-9 Data, Information, Knowledge Data –Items that are the most elementary descriptions of things, events, activities, and transactions –May be internal or external. Information –Organized data that has meaning and value Knowledge –Processed data or information that conveys understanding or learning applicable to a problem or activity

10. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-10 Data Raw data collected manually or by instruments Quality is critical –Quality determines usefulness the 4 categories of data quality are: 1.Contextual data quality 2.Intrinsic data quality 3.Accessibility data quality 4.Representation data quality –Often neglected or casually handled –Problems exposed when data is summarized

11. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-11

12. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-12 Data / Cleanse data 1.When populating warehouse 2.Data quality action plan p.221

13. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-13 Data / Cleanse data 3. Best practices for data quality 4. Measure results

14. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-14 Data/ integrity issues –Data itself should has no contradiction. –Uniformity, within limits. –Version, when moving through computers and there is different versions. –Completeness check  Check if summaries are correct  Check if all needed values to generate summary are included. –Conformity check, the summaries are similar to what expected and previous summaries. –Genealogy or drill-down

15. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-15 Data Integration Data Integration is the process of collecting data from more than one source.

16. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-16 Data Integration

17. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-17 Data Integration Access needed to multiple sources –Often enterprise-wide –Disparate and heterogeneous databases –XML becoming language standard

18. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-18 External Data Sources Web –Intelligent agents –Document management systems –Content management systems Commercial databases –Sell access to specialized databases

19. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-19 Database Management Systems DBMS is :- 1.Software program 2.Supplements operating system 3.Manages data 4.Queries data and generates reports 5.Data security 6.Combines with modeling language like spreadsheets for construction of DSS

20. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-20 Database Models Hierarchical –Top down, like inverted tree –Fields have only one “parent”, each “parent” can have multiple “children” –Fast used in transaction (insert, delete, update) processing. Network –Relationships created through linked lists, using pointers –“Children” can have multiple “parents” –Greater flexibility, substantial overhead

21. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-21 Database Models Relational –Flat, two-dimensional tables with multiple access queries –Examines relations between multiple tables –Flexible, quick, and extendable with data independence –Best used in retrieving data. Object oriented –Data analyzed at conceptual level –Inheritance, abstraction, encapsulation

22. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-22

23. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-23 Database Models, continued Multimedia Based –Multiple data formats JPEG, GIF, bitmap, PNG, sound, video, virtual reality –Requires specific hardware for full feature availability Document Based –Document storage and management Intelligent –Intelligent agents and ANN Inference engines

24. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-24 Data Warehouse Subject oriented Scrubbed so that data from heterogeneous sources are standardized Time series; no current status Nonvolatile –Read only Summarized Not normalized; may be redundant Data from both internal and external sources is present Metadata included –Data about data Business metadata Semantic metadata

25. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-25 Data Warehouse

26. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-26 Architecture May have one or more tiers –Determined by warehouse, data acquisition (back end), and client (front end) One tier, where all run on same platform, is rare Two tier usually combines DSS engine (client) with warehouse –More economical Three tier separates these functional parts

27. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-27

28. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-28 Data Warehouse

29. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-29

30. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-30 Data Warehouse

31. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-31 Migrating Data Business rules –Stored in metadata repository –Applied to data warehouse centrally Data extracted from all relevant sources –Loaded through data-transformation tools or programs –Separate operation and decision support environments Correct problems in quality before data stored –Cleanse and organize in consistent manner

32. Data Warehouse Design Star schema The data warehouse is based on the concept of dimensional modeling. Dimensional modeling is a retrieval based model that supports high volume query access. The star schema is the means of implementing the dimensional modeling. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-32

33. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-33 Data Warehouse Design Dimensional modeling –Retrieval based –Implemented by star schema Central fact table Dimension tables Grain –Highest level of detail –Drill-down analysis is not supported if the data warehouse can give high summarized level.

34. Data Warehouse Design © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-34

35. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-35 Data Warehouse

36. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-36 Data Warehouse Development Data warehouse implementation techniques –Top down –Bottom up –Hybrid –Federated Projects may be data centric or application centric Implementation factors –Organizational issues –Project issues –Technical issues Scalable = expandable. Flexible

37. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-37 Data Warehouse

38. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-38 Data Marts Data marts is a subset of a data warehouse, typically consisting of a single subject area (e.g., marketing, personnel…). Data mart can be either dependent or independent

39. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-39 Data Marts Dependent is : –Created from warehouse by replication. It is copied from data warehouse, it is built after the building of warehouse. Functional subset of warehouse Independent –Scaled down, less expensive version of data warehouse –Designed for a department or SBU –Organization may have multiple data marts Difficult to integrate

40. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-40 Business Intelligence and Analytics Business intelligence –Acquisition of data and information for use in decision-making activities Business analytics –Models and solution methods Data mining –Applying models and methods to data to identify patterns and trends

41. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-41 Business Intelligence and Analytics

42. © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 5-42