Chapter 5
Foundations of Business
Intelligence: Databases
and Information
Management
5.1
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
STUDENT OBJECTIVES
• Describe how a relational database organizes
data and compare its approach to an objectoriented database.
• Identify and describe the principles of a database
management system.
• Evaluate tools and technologies for providing
information from databases to improve business
performance and decision making.
5.2
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
STUDENT OBJECTIVES (Continued)
• Assess the role of information policy and data
administration in the management of
organizational data resources.
• Assess the importance of data quality assurance
for the business.
5.3
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
7-Eleven Stores Ask the Customer by Asking the Data
• Problem: Detached view of customer base,
inadequate sales data.
• Solutions: Implement retail information system and
database and deploy POS workstations to analyze
customer preferences and analyze sales trends.
• HP servers and Retail Information System leads to
reduced inventory and increased sales revenue.
• Demonstrates IT’s role in establishing customer
intimacy and managing inventory.
• Illustrates digital technology’s role in forging
success in business from data harvesting.
5.4
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
7-Eleven Stores Ask the Customer by Asking the Data
Interactive Session: 7-Eleven
• What are your experiences with shopping at your local
convenience store? Does the store ever run out of your
favorite items? If so, how quickly are they replaced?
• Does the store proprietor have a relationship with his or
her customers? Are you aware of purchase data being
collected?
• Are you more or less likely to shop at a convenience
store when you know that your purchase data are being
collected? Are you more or less likely to frequent a
store that caters to your personal buying habits?
5.5
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
The Database Approach to Data Management
• Database: a collection of related files containing
records on people, places, or things
• Entities and attributes
• Organizing data in a relational database
• Fields, records, key fields, primary key, foreign key
• Establishing relationships
• Entity-relationship diagram, normalization, join table
5.6
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
The Database Approach to Data Management
A Relational Database Table
A relational database organizes data in the form
of two-dimensional tables. Illustrated here is a
table for the entity SUPPLIER showing how it
represents the entity and its attributes.
Supplier_Number is the key field.
5.7
Figure 5-1
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
The Database Approach to Data Management
Normalized Database Design
Figure 5-5
5.8
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
The Database Approach to Data Management
Example Entity-Relationship Diagram
Figure 5-6
5.9
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
Database Management Systems
DBMS
• A specific type of software for creating, storing,
organizing, and accessing data from a database
• Separates the logical and physical views of the data
• Logical view: how end users view data
• Physical view: how data are actually structured and
organized
• Examples of DBMS: Microsoft Access, DB2, Oracle
Database, Microsoft SQL Server, MYSQL
5.10
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
Database Management Systems
Capabilities of Database Management Systems
• Data definition
• Data dictionary
• Querying and reporting
• Data manipulation language
• Structured query language (SQL)
• Object-oriented databases
5.12
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
Using Databases to Improve Business Performance and Decision Making
Data Warehouses
• What is a data warehouse?
• A database that stores current and historical data
that may be of interest to decision makers
• Data marts
• Subsets of data warehouses that are highly focused
and isolated for a specific population of users
5.13
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
Using Databases to Improve Business Performance and Decision Making
Components of a Data Warehouse
The data warehouse extracts current and
historical data from multiple operational
systems inside the organization. These data are
combined with data from external sources and
reorganized into a central database designed for
management reporting and analysis. The
information directory provides users with
information about the data available in the
warehouse.
5.14
Figure 5-13
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
Using Databases to Improve Business Performance and Decision Making
Business Intelligence, Multidimensional Data
Analysis, and Data Mining
• Business intelligence: tools for consolidating,
analyzing, and providing access to large amounts of
data to improve decision making
• Online analytical processing (OLAP)
• Data mining and predictive analysis
•
•
•
•
•
5.15
Associations
Sequences
Classifications
Clusters
Forecasts
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
Using Databases to Improve Business Performance and Decision Making
Business Intelligence
A series of analytical tools works with data
stored in databases to find patterns and
insights for helping managers and employees
make better decisions to improve organizational
performance.
5.16
Figure 5-14
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
Using Databases to Improve Business Performance and Decision Making
Databases and the Web
• Firms use the Web to make information from their
internal databases available to customers and
partners
• Middleware and other software make this
possible
• Database servers
• CGI
• Web interfaces provide familiarity to users and
savings over redesigning and rebuilding legacy
systems
5.18
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
Managing Data Resources
Establishing an Information Policy
• An information policy states an organization’s
rules for managing and storing information
• Data administration is responsible for the specific
policies and procedures through which data can be
managed as a resource
• Large organizations use a database design and
management group to perform database
administration
5.19
© 2007 by Prentice Hall
Essentials of Business Information Systems
Chapter 5 Foundations of Business Intelligence: Databases and
Information Management
Managing Data Resources
Ensuring Data Quality
• Poor data quality is a major obstacle to successful
customer relationship management
• Data quality problems can be caused by redundant
and inconsistent data produced by multiple systems
• Data input errors are the cause of many data quality
problems
• A data quality audit is a structured survey of the
accuracy and completeness of data
• Data cleansing detects and corrects incorrect,
incomplete, improperly formatted, and redundant data
5.20
© 2007 by Prentice Hall