1. CHAPTER 5 Data and Knowledge Management

2. CHAPTER OUTLINE 5.1 Managing Data 5.2 Big Data 5.3 The Database Approach 5.4 Database Management Systems 5.5 Data Warehouses and Data Marts 5.6 Knowledge Management

3. LEARNING OBJECTIVES 1. Identify three common challenges in managing data, and describe one way organizations can address each challenge using data governance. 2. Name six problems that can be minimized by using the database approach. 3. Demonstrate how to interpret relationships depicted in an entity-relationship diagram. 4. Discuss at least one main advantage and one main disadvantage of relational databases.

4. LEARNING OBJECTIVES 5. Identify the six basic characteristics of data warehouses, and explain the advantages of data warehouses and marts to organizations. 6. Demonstrate the use of a multidimensional model to store and analyze data. 7. List two main advantages of using knowledge management, and describe the steps in the knowledge management system cycle.

5. 5.1 Managing Data

6. Managing Data Difficulties in Managing Data Data Governance Master Data Management

7. 5.2 Big Data

8. Defining Big Data Big Data Generally Consist of: –Traditional enterprise data –Machine-generated/sensor data –Social Data –Images captured by billions of devices located around the world

9. Characteristics of Big Data Volume Velocity Variety

10. 5.3 The Database Approach

11. The Database Approach Database management system (DBMS) minimize the following problems: –Data redundancy –Data isolation –Data inconsistency

12. Data Hierarchy Bit Byte Field Record File (or table) Database

13. Designing the Database Data model Entity Attribute Primary key Secondary keys

14. Entity-Relationship Modeling Database designers plan the database design in a process called entity-relationship (ER) modeling. ER diagrams consists of entities, attributes and relationships. –Entity classes –Instance –Identifiers

15. 5.4 Database Management Systems

16. Database Management Systems Database management system (DBMS) Relational database model Structured Query Language (SQL) Query by Example (QBE)

17. Normalization Normalization is a method for analyzing and reducing a relational database to its most streamlined form for: –Minimum redundancy –Maximum data integrity –Best processing performance Normalized data is when attributes in the table depend only on the primary key.

18. 5.5 Data Warehousing

19. Data Warehousing Data warehouses and Data Marts Organized by business dimension or subject. Multidimensional. Historical. Use online analytical processing.

20. Benefits of Data Warehousing End users can access data quickly and easily via Web browsers because they are located in one place. End users can conduct extensive analysis with data in ways that may not have been possible before. End users have a consolidated view of organizational data.

21. Data Marts A data mart is a small data warehouse, designed for the end-user needs in a strategic business unit (SBU) or a department.

22. 5.6 Knowledge Management

23. Knowledge Management Knowledge management (KM) Knowledge Intellectual capital (or intellectual assets)

24. Knowledge Management System Cycle Create knowledge Capture knowledge Refine knowledge Store knowledge Manage knowledge Disseminate knowledge

25. Closing Case The Problem The Solution The Results