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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.
Big Data Case – pages 112 & 113
 Walmart processes over 1,000,000
transactions per hour
 From 2006 to 2010 IBM invested over
$12,000,000,000 for setting up business
intelligence centers
 Using big data to spot trends before your
competitors spot them can be a strategic
advantage (Best Buy success, Nestle failure)
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 a
trillion gigabytes
© Fanatic Studio/Age Fotostock America, Inc.
5.1 Managing Data
The Difficulties of Managing Data
Data Governance
Data Governance
Big data can have big data errors
•Data Governance – manage data
across the entire organization
• Master Data Management – have
•
all organization processes access a
single version of the data
Master Data – an enterprise system
of core 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 A zero or a one
Byte 8 bits, a single character or number
Field A column in a spreadsheet like a name
row in a spreadsheet like name and address and
Record Aphone
#
File (or table) A collection of related records
Database A collection of related files
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
The data model is a diagram that
represents the entities in the database
and their relationships.
An entity is a person, place, thing, or
event about which information is
maintained. A record generally
describes an entity.
An attribute is a particular
characteristic or quality of a particular
entity.
The primary key is a field that
uniquely identifies a record.
Secondary keys are other field that
have some identifying information but
may not identify the file with complete
accuracy.
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 (see page 120)
Maximum number of instances
Minimum number of instances
Entity-relationship diagram model
5.3 Database Management Systems
Database management system (DBMS)
[defines both the data structure and the data relationships]
Relational database model
Structured Query Language (SQL)
Query by Example (QBE)
One table is a “flat file”, it is the relationship
between tables that make a database
Student Database Example
Can you determine
an attribute?
A primary key?
A secondary key?
An instance?
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
Non-Normalized Relation
5.4 Data Warehousing
Data warehouses and Data Marts
Organized by business dimension or subject
Multidimensional
Historical
Use online analytical processing
A data warehouse is a repository of historical data organized by subject to support
decision makers in the organization.
Historical data in data warehouses can be used for identifying trends, forecasting, and
making comparisons over time.
Online analytical processing (OLAP) involves the analysis of accumulated data by
end users (usually in a data warehouse).
In contrast to OLAP, online transaction processing (OLTP) typically involves a
database, where data from business transactions are processed online as soon as they
occur.
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 Concepts
 Metadata – data about data such as
relationships between tables or table
definitions
 Data quality – data is seldom 100% “clean”
 Data governance (link)
 Users include information producers and
consumers
5.5 Knowledge Management
 Knowledge management (KM)

Knowledge (should be contextual, relevant, and
actionable)

Intellectual capital (a.k.a. knowledge or
intellectual assets)
© Peter Eggermann/Age Fotostock America, Inc.
Knowledge Management (continued)
Explicit Knowledge
(above the waterline)
Tacit Knowledge
(below the waterline, all
the stuff you know but
that you don’t explicitly
realize you know)
© 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
Chapter Closing Case
• The Problem
• The Solution
• The Results