CHAPTER 5

Data and Knowledge Management

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

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.

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.

Annual Flood of Data from…..

Credit card swipes

E-mails

Digital video

Online TV

RFID tags

Blogs

Digital video surveillance

Radiology scans

Source: Media Bakery

Annual Flood of New Data!

In the zettabyte range

A zettabyte is 1000 exabytes

© Fanatic Studio/Age Fotostock America, Inc.

5.1 Managing Data

The Difficulties of Managing Data

Data Governance

Difficulties in Managing Data

Source: Media Bakery

Data Governance

See video

•Data Governance

•Master Data Management

•Master Data

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 DataJohn Stevens StudentIntro to Management Information Systems CourseISMN 3140 Course No.10 AM until 11 AM TimeMondays and Wednesdays WeekdayRoom 41 Smith Hall LocationProfessor Rainer Instructor

5.2 The Database Approach

Database management system (DBMS) 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 modelEntity

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

Relationships Between Entities

Entity-relationship diagram model

5.3 Database Management Systems

Database management system (DBMS)

Relational database model

Structured Query Language (SQL)

Query by Example (QBE)

Student Database Example

Normalization

Normalization

Minimum redundancy

Maximum data integrity

Best processing performance

Normalized data occurs 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

5.4 Data Warehousing

Data warehouses and Data Marts Organized by business dimension or subject

Multidimensional

Historical

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.

5.5 Knowledge Management

Knowledge management (KM)

Knowledge

Intellectual capital (or intellectual assets)

© Peter Eggermann/Age Fotostock America, Inc.

Knowledge Management (continued)

Tacit Knowledge(below the waterline)

Explicit Knowledge (above the waterline)

© Ina Penning/Age Fotostock America, Inc.

Knowledge Management (continued)

Knowledge management systems (KMSs)

Best practices

© Peter Eggermann/Age Fotostock America, Inc.

Knowledge Management System Cycle

Create knowledge

Capture knowledge

Refine knowledge

Store knowledge

Manage knowledge

Disseminate knowledge

Knowledge Management System Cycle

Chapter Closing Case

• The Problem

• The Solution

• The Results