Copyright 2007 John Wiley & Sons, Inc. Chapter 41 Data and Knowledge Management

Copyright 2007 John Wiley & Sons, Inc.

Chapter 4 1

Chapter 4

Data and Knowledge Management


Chapter 4 2

Chapter Outline

4.1 Managing Data 4.2 The Database Approach 4.3 Database Management Systems 4.4 Data Warehousing 4.5 Knowledge Management


Chapter 4 3

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. Explain the operation of data warehousing and its

role in decision support.


Chapter 4 4

Learning Objectives (Continued)

Understand the capabilities and benefits of data mining.

Describe data visualization. Explain geographic information systems and

virtual reality as decision support tools. Define knowledge and describe the different

types of knowledge.


Chapter 4 5

4.1 Managing Data

Difficulties of Managing Data. Amount of data increases exponentially. Data are scattered and collected by many

individuals using various methods and devices. Data come from many sources including internal

sources, personal sources and external sources. Data security, quality and integrity are critical.


Chapter 4 6

Managing Data (Continued)

Clickstream data. Data that visitors and customers produce when they visit a Website.

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


Chapter 4 7

Data Life Cycle


Chapter 4 8

Data Hierarchy

Bit (a binary digit): a circuit that is either on or off.

Byte: group of 8 bits, represents a single character.

Field: name, number, or characters that describe an aspect of a business object or activity.


Chapter 4 9

Data Hierarchy (Continued)

Record: collection of related data fields.

File (or table): collection of related records.

Database: a collection of integrated and related files.


Chapter 4 10

Let see fig. 4.2.


Chapter 4 11

4.2 Database Approach

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

DBMSs minimizes the following problems: Data redundancy: the same data stored in many places. Data isolation: applications cannot access data

associated with other applications. Data inconsistency: various copies of the data do not

agree.


Chapter 4 12

Database Approach (Continued)

DBMSs maximize the following issues: Data security. Data integrity: data meets certain constraints, no

alphabetic characters in zip code field. Data independence: applications and data are

independent of one another, all applications are able to access the same data.


Chapter 4 13

Let see fig. 4.3


Chapter 4 14

Designing the Database

Data model. Diagram that represents the entities in the database and their relationships. Entity is a person, place, thing or event. Attribute is a characteristic or quality of a particular entity. Primary key is a field that uniquely identifies that record. Secondary keys are fields that have identifying

information but may not identify with complete accuracy.


Chapter 4 15

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 are a group of entities of a given type, i.e. STUDENT.

Instance is the representation of a particular entity, i.e. STUDENT(John Smith, 123-45-6789, …).

Identifiers are attributes unique to that entity instance, i.e. StudentIDNumber.


Chapter 4 16

Let see fig. 4.4


Chapter 4 17

4.3 Database Management Systems

Database management system (DBMS) is a set of programs that provide users with tools to add, delete, access and analyze data stored in one location.

Online transaction processing (OLTP) is when transactions are processed as soon as they occur.

Relational database model is based on the concept of two-dimensional tables.

Popular examples of relational databases are Microsoft Access and Oracle.


Chapter 4 18

Let see fig. 4.5, 4.6, 4.7 and 4.8


Chapter 4 19

4.4 Data Warehousing

Data warehouse is a repository of historical data organized by subject to support decision makers in the organization and include: Online analytical processing which involves

the analysis of accumulated data by end users; Multidimensional data structure which allows

data to be represented in a three-dimensional matrix (or data cube).


Chapter 4 20

Let see fig. 4.9, 4.10, 4.11 and 4.12


Chapter 4 21

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.


Chapter 4 22

Data Marts & Data Mining

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

Data mining involves searching for valuable business information in a large database, data warehouse, or data mart. Used to predict trends and behaviors. Identify previously unknown patterns.


Chapter 4 23

Data Mining Applications

Retailing and sales. Predict sales, prevent theft and fraud, determine correct inventory levels and distribution schedules.

Banking. Forecast levels of bad loans, fraudulent credit card use, predict credit card spending by new customers, etc.

Manufacturing and production. Predict machinery failures, find key factors to help optimize manufacturing capacity.

Insurance. Forecast claim amounts, medical coverage costs, predict which customers will buy new insurance policies.


Chapter 4 24

Data Mining Applications (Continued)

Policework. Track crime patterns, locations, criminal behavior; identify attributes to assist in solving criminal cases.

Health care. Correlate demographics of patients with critical illnesses, develop better insight to identify and treat symptoms and their causes.

Marketing. Classify customer demographics to predict how customers will respond to mailing or buy a particular product.


Chapter 4 25

4.6 Knowledge Management

Knowledge management (KM) is a process that helps organizations manipulate important knowledge that is part of the organization’s memory, usually in an unstructured format.

Knowledge is information that is contextual, relevant and actionable; information in action.

Intellectual capital (or intellectual assets) is another term often used for knowledge.


Chapter 4 26

Knowledge Management (Continued)

Explicit knowledge deals with more objective, rational and technical knowledge.

Tacit knowledge is the cumulative store of subjective or experiential learning.

Knowledge management systems (KMSs) use modern information technologies – Internet, intranets, extranets, data warehouses - to systemize, enhance and expedite intrafirm and interfirm knowledge management.

Best practices are the most effective and efficient ways of doing things, readily available to a wide range of employees.


Chapter 4 27

Let see fig. 4.13


Chapter 4 28

Knowledge Management System Cycle

Create knowledge. Determine new ways. Capture knowledge. Identify as valuable. Refine knowledge. Make it actionable. Store knowledge. Store in a reasonable format. Manage knowledge. Verify it is relevant, accurate. Disseminate knowledge. Made available.


Chapter 4 29

THE END OF SESSION 8

Documents

Copyright 2007 John Wiley & Sons, Inc. Chapter 41 Data and Knowledge Management