CHAPTER 5. Data and Knowledge Management

Transcription

1 CHAPTER 5 Data and Knowledge Management

2 CHAPTER OUTLINE 5.1 Managing Data 5.2 The Database Approach 5.3 Database Management Systems 5.4 Data Warehouses and Data Marts 5.5 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 (continued) 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 Annual Flood of Data from Credit card swipes s Digital video Online TV RFID tags Blogs Digital video surveillance Radiology scans

6 Annual Flood of New Data! In the zettabyte range A zettabyte is 1,000 exabytes or or 2 60 Just under 64-bit

7 5.1 Managing Data The Difficulties of Managing Data Data Governance

8 Difficulties in Managing Data Growing exponentially Scattered Multiple sources Timeliness Data Security Data Quality Data Integrity Data Consistency Federal regulations Source: Media Bakery

9 Data Governance Data Governance Master Data Management Master Data See video

10 Master Data Management John Stevens registers for Introduction to Management Information Systems (ISMN 3140) from 10 AM until 11 AM on Mondays and Wednesdays in Room 41 Smith Hall, taught by Professor Rainer. Transaction Data Master Data John Stevens Student Intro to Management Information Systems Course ISMN 3140 Course No. 10 AM until 11 AM Time Mondays and Wednesdays Weekday Room 41 Smith Hall Location Professor Rainer Instructor

11 5.2 The Database Approach Database management system (DBMS) minimize the following problems: Data redundancy Data isolation Data inconsistency

12 Database Approach (continued) DBMSs maximize the following issues: Data security Data integrity Data independence

13 Database Management Systems

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

15 Hierarchy of Data for a Computer-Based File

16 Data Hierarchy (continued) Bit (binary digit) Byte (eight bits)

17 Data Hierarchy (continued) Example of Field and Record

18 Data Hierarchy (continued) Example of Field and Record

19 Designing the Database Data model Entity Attribute Primary key Secondary keys

20 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

21 Relationships Between Entities

22 Entity-relationship diagram model

23 5.3 Database Management Systems Database management system (DBMS) Relational database model Structured Query Language (SQL) Query by Example (QBE)

24 Student Database Example

25 Normalization Normalization Minimum redundancy Maximum data integrity Best processing performance (most of the time) Normalized data occurs when attributes in the table depend only on the primary key.

26 Non-Normalized Relation

27 Normalizing the Database (part A)

28 Normalizing the Database (part B)

29 Normalization Produces Order

30 5.4 Data Warehousing Data warehouses and Data Marts Organized by business dimension or subject Multidimensional Historical Use online analytical processing

31 Data Warehouse Framework & Views

32 Relational Databases

33 Multidimensional Database

34 Equivalence Between Relational and Multidimensional Databases

35 Equivalence Between Relational and Multidimensional Databases

36 Equivalence Between Relational and Multidimensional Databases

37 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.

38 5.5 Knowledge Management Knowledge management (KM) Knowledge Peter Eggermann/Age Fotostock America, Inc. Intellectual capital (or intellectual assets)

39 Knowledge Management (continued) Explicit Knowledge (above the waterline) Tacit Knowledge (below the waterline) Ina Penning/Age Fotostock America, Inc.

40 Knowledge Management (continued) Knowledge management systems (KMSs) Peter Eggermann/Age Fotostock America, Inc. Best practices

41 Knowledge Management System Cycle 1. Create knowledge 2. Capture knowledge 3. Refine knowledge 4. Store knowledge 5. Manage knowledge 6. Disseminate knowledge

42 Knowledge Management System Cycle