1) Data resource management involves using database management and data warehousing technologies to manage an organization's data resources and meet stakeholders' information needs. 2) There are different types of databases like operational databases, distributed databases, external databases, and data warehouses. Data warehouses store cleaned and organized data for analysis. 3) Data mining analyzes data in data warehouses to reveal hidden patterns and trends which can help with tasks like identifying new products, finding quality issues, cross-selling, and profiling customers.

1. Data Resource Management
Chapter 5
Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin

2. 5-2
Learning Objectives
Explain the business value of implementing
data resource management processes and
technologies in an organization
Outline the advantages of a database
management approach to managing the data
resources of a business, compared with a file
processing approach
Explain how database management software
helps business professionals and supports the
operations and management of a business

3. 5-3
Learning Objectives
Provide examples to illustrate the following
concepts
– Major types of databases
– Data warehouses and data mining
– Logical data elements
– Fundamental database structures
– Database development

4. 5-4
Fundamental Data
Concepts

5. 5-5
Database Management
In all Information Systems, data resources
must be organized in a logical manner so that:
1- They can be accessed easily
2- Processed efficiently
3- Retrieved quickly
4- Managed effectively

6. 5-6
Logical Data Elements
Field
(data item)
RecordCharacter
•a grouping of
related characters
•Represents an
attribute (quality
or characteristic)
of some entity
(object, person,
place, event)
•Examples…
salary, job title
•Grouping of all
the fields used to
describe the
attributes of an
entity
•Example… payroll
records with name,
SSN, pay rate
•A single
alphabetic,
numeric, or other
symbol

7. 5-7
Logical Data Elements
File
(table, flat file)
Database
•Group of related
records
•Integrated
collection of
logically related
data elements

8. 5-8
Logical Data Elements

9. 5-9
Database Structure

10. 5-105-10
Database Structures
1. Hierarchical
2. Network
3. Relational
4. Object-oriented
5. Multidimensional

11. 5-11
Common Database Structures: Hierarchical
– Early DBMS structure
– Records arranged in tree-like structure
– Relationships are one-to-many
– Access data elements by moving progressively downward from the
root and along the branches of the tree

12. 5-12Database Systems, 9th Edition
CEO
Marketing
manager
Operation
Manager
Finance
Manager
Employee
1
Employee
2
Employee
3
Employee
5
Employee
4
Employee
6

13. 5-13
Common Database Structures: Network
– Used in some mainframe DBMS packages
– Many-to-many relationships
 Any data element can be related to any number of other data
elements

14. 5-14Database Systems, 9th Edition
Ms. Ghada Ms. Anwar Ms. Mashael
Principles of
MIS
Microeconomics
Accounting Legal
Environment
Student 1 Student 2 Student 3 Student 4 Student 5 Student 6

15. 5-15
Common Database Structures: Relational
Most widely used structure
– Data elements are stored in tables
– Row represents a record; column is a field
– Can relate data in one file with data in another,
if both files share a common data element

16. 5-16

17. 5-17
Relational operations
 Relational operations include:
– Select…
 Create a subset of records that meet a stated criterion.
 Example: employees earning more than $30,000
– Join…
 Combine two or more tables temporarily.
 Looks like one big table.
– Project…
 Create a subset of columns in a table

18. 5-18
Common Database Structures: Multidimensional
Variation of relational model
– Uses multidimensional structures to
organize data
– Data elements are viewed as being in cubes
– Popular for analytical databases that support
Online Analytical Processing (OLAP)

19. 5-19
Multidimensional Model

20. 5-20
Common Database Structures: Object-Oriented
 An object consists of
– Data values describing the attributes of an entity
– Operations that can be performed on the data
 Encapsulation
– Combine data and operations
 Inheritance
– New objects can be created by replicating some or all of the
characteristics of parent objects
 Used in object-oriented database management systems (OODBMS)
 Supports complex data types more efficiently than relational
databases
– Examples: graphic images, video clips, web pages

21. 5-21
Common Database Structures: Object-Oriented
Source: Adapted from Ivar Jacobsen, Maria Ericsson, and Ageneta Jacobsen, The Object Advantage: Business Process
Reengineering with Object Technology (New York: ACM Press, 1995), p. 65.
Copyright @ 1995, Association for Computing Machinery. By permission.

22. 5-22
Evaluation of Database Structures
Hierarchical
•Works for
structured,
routine
transactions
•Can’t handle
many-to-many
relationship
•Unable to
handle ad hoc
requests
Network
•More flexible
than hierarchical
•Unable to
handle ad hoc
requests
Relational
•Easily
responds to ad
hoc requests
•Easier to work
with & maintain
•Not as efficient
or quick as
hierarchical or
network

23. 5-23
Database Development
Database Administrator (DBA)
In charge of enterprise-wide database development
Improves integrity and security of organizational databases
Uses Data Definition Language (DDL) to develop and
specify data content, relationships, and structure
Stores these specifications in a data dictionary
or metadata repository

24. 5-24
Data Dictionary
Data
Dictionary
Contains data about data (metadata)
Relies on specialized software component
to manage a database of data definitions
Contains
information
on…
Security
Database maintenance
Requirements for end users’ access and
use of applications
Names and descriptions of all types of data
records and their interrelationships

25. 5-25
Example of a Data Dictionary

26. 5-26
Data Resource Management
Data resource management is a managerial
activity
– Uses data management, data warehousing,
and other IS technologies
– Manages data resources to meet the
information needs of business stakeholders

27. 5-27
Types of Databases

28. 5-28
Operational Databases
Stores detailed data needed to support
businesses and operations
Also called subject area databases (SADB),
transaction databases, and
production databases
Database examples:
customer databases, human resource
databases, inventory databases

29. 5-29
Distributed Databases
Distributed databases are copies or parts of
databases stored on servers at multiple locations
Advantages Disadvantages
Protection of valuable data
Data can be distributed
into smaller databases
Each location has control
of its local data
All locations can access
any data, anywhere
Improved database
performance at worksites
Maintaining data
accuracy

30. 5-30
Distributed Databases
Look at each distributed database and find changes
Apply changes to each distributed database
Very complex
One database is master
Duplicate the master after hours, in all locations
Easier to accomplish
Requires extra computing power & bandwidth
Duplication
Replication
Updating data can be done in 2 ways:

31. 5-31
External Databases
Databases available for
a fee from the Web,
or from commercial
online services
Search engines like Google or Yahoo
are external databases
Hypermedia databases
Statistical databases
Bibliographic and
full-text databases

32. 5-32
Components of Web-Based System
A hypermedia database contains
– Website database
– Consist of hyperlinked pages of multimedia
– Interrelated hypermedia page elements,
rather than interrelated data records

33. 5-33
Data Warehouses
Central source of data that has been cleaned,
transformed, and cataloged
Stores static data that has been extracted from
other databases in an organization
Subsets of data that focus on specific aspects of a
company (department or process)
Data warehouses may be divided into data marts
Data is used for data mining, analytical processing,
analysis, research, decision support

34. 5-34
Data Warehouse Components

35. 5-35
Applications and Data Marts

36. 5-365-36
Data Mining
 Data in data warehouse are analyzed to reveal
hidden patterns and trends
Examples:
– Perform market-basket analysis to identify new
products
– Find root causes to quality problems
– Cross sell to existing customers
– Profile customers with more accuracy

37. 5-37

38. 5-38
Data Mining