CHAPTER 4

Data and Knowledge Management

CHAPTER OUTLINE

4.1 Managing Data

4.2 The Database Approach

4.3 Database Management Systems

4.4 Data Warehousing

4.5 Data Governance

4.6 Knowledge Management

LEARNING OBJECTIVES

Recognize the importance of data, issues involved in managing data and their lifecycle.

Describe the sources of data and explain how data are collected.

Explain the advantages of the database approach.

Learning Objectives (continued)

Explain the operation of data warehousing and its role in decision support.

Explain data governance and how it helps to produce high-quality data.

Define knowledge, and describe different types of knowledge.

Chapter Opening Case

Chapter Opening Case (continued)

Push Model

Products

Chapter Opening Case (continued)

Pull Model

Orders

Examples of Data Sources

E-mails

Credit card swipes

RFID tags

Digital video surveillance

Radiology scans

Blogs

4.1 Managing Data

Difficulties in Managing DataAmount of data increases

exponentially.

Data are scattered and collected by many individuals using various methods and devices.

Data come from many sources.

Data security, quality and integrity are critical.

Difficulties in Managing Data (continued)

An ever-increasing amount of data needs to be considered in making organizational decisions.

The Data Deluge

Data Life Cycle (Figure 4.1)

Data, Information, Knowledge, Wisdom

4.2 The Database Approach

Database management system (DBMS) provides all users with access to all the data.

DBMSs minimize the following problems: Data redundancy Data isolation Data inconsistency

Database Approach (continued)

DBMSs maximize the following issues: Data security Data integrity Data independence

Database Management Systems

Data Hierarchy

Bit

Byte

Field

Record

File (or table)

Database

Hierarchy of Data for a Computer-Based File

Data Hierarchy (continued)

Bit (binary digit)

Byte (eight bits)

Data Hierarchy (continued)

Example of Field and Record

Data Hierarchy (continued)

Example of Field and Record

Designing the Database

Data model Entity Attribute Primary key Secondary keys

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

Entity-Relationship Diagram Model

4.3 Database Management Systems

Database management system (DBMS)

Relational database model

Structured Query Language (SQL)

Query by Example (QBE)

Student Database Example

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.

Non-Normalized Relation

Normalizing the Database (part A)

Normalizing the Database (part B)

Normalization Produces Order

Turnitin (IT’s About Business 4.1)

A Turnitin originality report

4.4 Data Warehousing

Data warehouse Data warehouses are organized by business

dimension or subject. Data warehouses are multidimensional.

A Data Cube

Data Warehousing (continued)

Data warehouses are historical. Data warehouses use online analytical

processing.

Data Warehouse Framework & Views

Relational Databases

Multidimensional Database

Equivalence Between Relational and Multidimensional Databases

Equivalence Between Relational and Multidimensional Databases

Equivalence Between Relational and Multidimensional Databases

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.

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.

4.5 Data Governance

Data governance Master data management Master data

Data Governance (continued)

Data Governance (continued)

4.6 Knowledge Management

Knowledge management (KM) Knowledge Intellectual capital (or intellectual assets)

Knowledge Management (continued)

Tacit Knowledge(below the waterline)

Explicit Knowledge (above the waterline)

Knowledge Management (continued)

Knowledge management systems (KMSs) Best practices

Knowledge Management System Cycle

Create knowledge Capture knowledge Refine knowledge Store knowledge Manage knowledge Disseminate knowledge

Knowledge Management System Cycle

Chapter Closing Case

High CVM passengerstravel in style