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

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. 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. Fundamental Data Concepts

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. Logical Data Elements

7. Logical Data Elements Character Field (data item) Record
A single alphabetic, numeric, or other symbol
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

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

9. Electric Utility Database

10. Database Structure

11. Database Structures Hierarchical Network Relational Object-oriented
Multidimensional

12. 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

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. 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
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

15. 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
Three basic operations on relational databases

16. 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)
OLAP is used for answers to complex business queries

17. Multidimensional Model

18. 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.
1995, Association for Computing Machinery. By permission.

19. 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

20. 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
Gap in performance between relational and hierarchical and network is rapidly narrowing

21. 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
Data dictionary
Data base catalog containing metadata
Metadata – data about data
Stores these specifications in a data dictionary or metadata repository

22. Data Dictionary Data Dictionary Contains data about data (metadata)
Relies on specialized software component to manage a database of data definitions
Can be active or passive
Contains information on…
Names and descriptions of all types of data records and their interrelationships
Database development is a top-down process
Develop an enterprise model that defines the basic business process of the enterprise
Define the information needs of end users in a business process
Identify the key data elements that are needed to perform specific business activities (entity relationship diagrams)
Requirements for end users’ access and use of applications
Database maintenance
Security

23. Example of a Data Dictionary

24. Types of Databases

25. 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

26. Distributed Databases
Distributed databases are copies or parts of databases stored on servers at multiple locations
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
Advantages
Disadvantages

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

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

29. 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

30. Data warehouses may be divided into data marts
Stores static data that has been extracted from other databases in an organization
Central source of data that has been cleaned, transformed, and cataloged
Data is used for data mining, analytical processing, analysis, research, decision support
Stores data that has been extracted from the operational, external and other databases
Data has been cleaned, transformed and cataloged
Used by managers and professionals for
Data mining,
Online analytical processing,
Business analysis,
Market research,
Decision support
Data mart is subset of warehouse for specific use of department
Static data?
Data warehouses may be divided into data marts
Subsets of data that focus on specific aspects of a company (department or process)

31. Data Warehouse Components

32. Applications and Data Marts

33. Data Mining

34. Data Mining
Data in data warehouse are analyzed to reveal hidden patterns and trends
Examples:
Perform market-basket analysis to identify new business processes
Find root causes to quality problems
Cross sell to existing customers
Profile customers with more accuracy
Discussed in more detail in chapter 9

35. Data Mining
Data in data warehouses are analyzed to reveal hidden patterns and trends
Market-basket analysis to identify new product bundles
Find root cause of qualify or manufacturing problems
Prevent customer attrition
Acquire new customers
Cross-sell to existing customers
Profile customers with more accuracy

36. Database Management System
In mainframe and server computer systems, database management software is used to…
Create new databases and database applications
Maintain the quality of the data in an organization’s databases
Use the databases of an organization to provide the information needed by end users