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
•Data Governance
•Master Data Management
•Master Data
See video
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
John Stevens
Intro to Management Information Systems
ISMN 3140
10 AM until 11 AM
Mondays and Wednesdays
Room 41 Smith Hall
Professor Rainer
Master Data
Student
Course
Course No.
Time
Weekday
Location
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 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
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)
Explicit Knowledge
(above the waterline)
Tacit Knowledge
(below 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