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MANAGING
INFORMATION
TECHNOLOGY
FIFTH EDITION
CHAPTER 5
THE DATA RESOURCE
E. Wainright Martin  Carol V. Brown  Daniel W. DeHayes
Jeffrey A. Hoffer  William C. Perkins
WHY MANAGE DATA?
 Organizations could not function long
without critical business data
 Cost to replace data would be very high
 Time to reconcile inconsistent data may
be too long
 Data often needs to be accessed quickly
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WHY MANAGE DATA?
 Data should be:
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Cataloged
Named in standard ways
Protected
Accessible to those with a need to know
Maintained with high quality
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The Data Model
Data model –
overall map for business data needed to effectively
manage the data
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The Data Model
 Data modeling involves:
Methodology, or steps followed to identify
and describe data entities
 Notation, or a way to illustrate data entities
graphically

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The Data Model
 Entity-relationship diagram (ERD)
Most common method for representing a
data model and organizational data needs
 Captures entities and their relationships

Entities – things about which data are
collected
 Attributes – actual elements of data that are
to be collected

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The Data Model
NOTE:
• Entities are Customer, Order, and Product.
• Attributes of the Customer entity could be
customer last name, first name, street, city, …
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Figure 5.1 Entity-Relationship Diagram
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Data Modeling
 Enterprise modeling
Top-down approach
Describes organization and data
requirements at high level, independent of
reports, screens, or detailed specifications
 Not biased by how business operates today


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Data Modeling
Enterprise Modeling Steps:

Divide work into major
functions

Divide each function into
processes
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Divide processes into
activities

List data entities
assigned to each activity

Identify relationships
between entities
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Figure 5.2 Enterprise Decomposition
for Data Modeling
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Data Modeling
 View integration


Bottom-up approach
Each report, screen, form, document
produced from databases first … each
called a user view
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Data Modeling
View Integration Steps:
 Create user views
 Identify data elements in each user view and put into a
structure called a normal form
 Normalize user views
 Integrate set of entities from normalization into one
description
Normalization –
process of creating simple data structures from more complex
ones
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Data Modeling
 Data modeling guidelines:
Objective – effort must be justified by need
Scope – broader scope, more chance of
failure
 Outcome – uncertainty leads to failure
 Timing – consider an evolutionary approach
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
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MANAGING THE DATA RESOURCE
Database Architecture
Database –
shared collection of logically related data, organized to
meet needs of an organization
Database Architecture –
way in which the data are structured and stored in the
database
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Figure 5.3 The Data Pyramid
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Database Architecture
 Six basic database architectures:
1.
2.
3.
4.
Hierarchical (top-down organization)
Network (high-volume transaction processing)
Relational (data arranged in simple tables)
Object-oriented (data and methods encapsulated in object
classes)
5.
6.
Object-relational (hybrid of relational and objectoriented)
Multidimensional (used by data warehouses)
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Tools for Managing Data
Database Management System (DBMS) –
support software used to create, manage, and protect
organizational data
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Tools for Managing Data
 A DBMS helps manage data by providing
seven functions:
Data storage, retrieval, update
2. Backup
3. Recovery
4. Integrity control
5. Security control
6. Concurrency control
7. Transaction control
1.
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Tools for Managing Data
 Most popular type of database architecture
is relational
 Not all relational systems are identical.
 Best effort to date for standardizing
relational databases is SQL
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Tools for Managing Data
Data Dictionary/Directory (DD/D) –
central encyclopedia of data definitions and usage
information … a database about data
 Contains:
 Definition of each entity,
relationship, and data
element
 Display formats
 Integrity rules
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


Security restrictions
Volume and sizes
List of applications that use
the data
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Database Programming
Query language –
a 4 GL, nonprocedural programming language to obtain
data from a database, often provided by the DBMS
SQL query language example:
SELECT ORDER#, CUSTOMER#, CUSTNAME,
ORDER-DATE FROM CUSTOMER, ORDER
WHERE ORDER-DATE > ’04/12/05’
AND CUSTOMER.CUSTOMER# =
ORDER.CUSTOMER#
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Principles in Managing Data
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The need to manage data is permanent
Data can exist at several levels
Application software should be separate from the
database
 Application software can be classified by how they
treat data
1. Data capture
2. Data transfer
3. Data analysis and presentation
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Figure 5.4
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Principles in Managing Data
 Application software should be
considered disposable
 Data should be captured once
 There should be strict data standards
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Principles in Managing Data
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Figure 5.5 Types of Data Standards
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The Data Management Process
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Figure 5.6 Asset Management Functions
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Figure 5.7 The Data Warehouse
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Data Management Policies
 Organizations should have policies regarding:
 Data
ownership
 Data administration
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Data Ownership
Corporate information policy –
foundation for managing the ownership of data
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© 2005 Pearson Prentice-Hall
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Figure 5.8 Example Data Access Policy
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Data Administration
Key functions of the data administration group:
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Promote and control data sharing

Analyze the impact of changes to application systems when data
definitions change
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Maintain the data dictionary
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Reduce redundant data and processing
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Reduce system maintenance costs and improve system
development productivity
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Improve quality and security of data
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Insure data integrity
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Data Administration
Key functions of the database administrator (DBA):
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Tuning database management systems.
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Selection and evaluation of and training on database technology.
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Physical database design.
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Design of methods to recover from damage to databases.
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Physical placement of databases on specific computers and
storage devices.
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The interface of databases with telecommunications and other
technologies.
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