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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