MANAGING INFORMATION TECHNOLOGY

7th EDITION

CHAPTER 4
THE DATA RESOURCE

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 PART 1: IT BUILDING BLOCKS

Building Blocks of Information Technology

Hardware Software Network Data

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Why Manage Data?
• Organizations win by making good decisions fast, and
organizations cannot do so without a high-quality data
resource.
• Although managing data as a resource has many general
business dimensions, it is also important for the cost-effective
development and operation of information systems.
• Poor systems development productivity is frequently due to a
lack of data management, and some methods, such as
prototyping, cannot work unless the source of data is clear
and the data are available.
• Systems development time is greatly enhanced by the reuse
of data and programs as new applications are designed and
built.
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 • Unless data are cataloged, named in standard ways,
protected but accessible to those with a need to know, and
maintained with high quality, the data and the programs that
capture and maintain them cannot be reused.

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 TECHNICAL ASPECTS OF MANAGING DATA
DATA MODELS

• An overall “map” for business data

• Involves:
• A methodology (process) to identify and describe data entities
• A notation = a way to describe data entities

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 DATA MODEL: CONCEPTUAL DESIGN PHASE

ENTITY-RELATIONSHIP DIAGRAM (ERD)

- Entities = things about which data are collected

(e.g., Customer, Order, Product)
- Attributes = actual elements of data to be collected
- Relationships = associations between entities
(e.g., Submits, Includes)

MOST COMMON DATA MODEL FOR CONCEPTUAL DESIGN PHASE

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 TECHNICAL ASPECTS
METADATA

• Data about data

• Unambiguous data description

• Documents “business rules” that govern data (e.g., type of data

such as alphanumeric; whether a name can change; etc.

• Quality data requires high-quality metadata

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 TECHNICAL ASPECTS: DATA MODELING
ENTERPRISE MODELING

- Top-down approach

- High-level model

- Describes organization and data requirements at high level,

independent of reports, screens, or detailed descriptions of
data processing requirements

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 ENTERPRISE MODELING
Future-oriented Corporate Data Model

– Divide work into major

functions
– Divide each function into
processes
– Divide processes into
activities (e.g., forecast
sales for next quarter)
– List data entities assigned
to each activity
– Check for consistent
names

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 TECHNICAL ASPECTS: DATA MODELING
VIEW INTEGRATION

• Bottom-up approach
• Each report, screen, form, and document produced from
databases (called user views) is identified
– Create user views
– Identify data element in each user view and put into a
structure called a normal form
– Normalize user views
– Combine user views
– Reconcile any differences with enterprise model

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 TECHNICAL ASPECTS: DATA MODELING
TECHNICAL ASPECTS: DATA MODELING
NORMALIZATION
• The process of creating simple data structures from more complex ones
using a set of rules that yields a stable structure.

Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall Source: Kenneth C. Laudon and Jane P. Laudon 4-11
 TECHNICAL ASPECTS: DATA MODELING
PACKAGED (UNIVERSAL) DATA MODELS

• Advantages:
- Developed using proven components
- Requires less time and money
- Easier to evolve
- Will easily work with other applications from the same
vendor
- Provides a starting point for requirements
- Promotes holistic and flexible views
- Easier to share data across organizations in same industry

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 TECHNICAL ASPECTS: DATA MODELING
DATA MODELING GUIDELINES

Objective Some overriding need

Scope Coverage for a data model

Outcome The more uncertain the outcome, the

lower the chances for success
Timing Start with high-level model and fill in
details as major systems projects
undertaken

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 TECHNICAL ASPECTS: DATA MODELING
DATABASE PROGRAMMING

Database processing activity can be specified with a:

- Procedural language (3GL)

- One or more special purpose languages (4GL)

Structured query language (SQL)
Data exchange language (XML)
Example: SQL Query
SELECT OrderID, CustomerID, CustomerName, OrderDate
FROM Customer, Order
WHERE OrderDate > ‘04/12/2020’ AND
Customer.CustomerID = Order.CustomerID
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 MANAGERIAL ISSUES

PRINCIPLES IN MANAGING DATA

1. The need to manage data is permanent.

2. Data can exist at several levels within the organization.
3. Application software should be separate from the database.
4. Application software can be classified by how it treats data.
5. Application software should be considered disposable.
6. Data should be captured once.
7. There should be strict data standards.

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 MANAGERIAL ISSUES
PRINCIPLES

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 PRINCIPLES IN MANAGING DATA

1. The Need to Manage Data is Permanent

• Data values may change, but a company will always have

customers, products, employees, etc. about which it needs to
keep current data

• Business processes will change, but only the programs will

need to be rewritten

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 PRINCIPLES IN MANAGING DATA

2. Data can exist at several levels within an organization

• Most new data are captured in operational databases

• Managerial and strategic databases typically subsets,

summaries, or aggregates of operational databases

• If managerial databases are constructed from external sources,

there may be problems with data consistency

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 PRINCIPLES IN MANAGING DATA

3. Application Software should be separate from the database

• Application independence = separation or decoupling of

data from application systems
- Raw data captured and stored
- When needed, data are retrieved but not consumed
- Data are transferred to other parts of the organization when
authorized

• Meaning and structure of data not hidden from other

applications

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 PRINCIPLES IN MANAGING DATA

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 PRINCIPLES IN MANAGING DATA

4. Application Software can be classified by how it treats data

Data capture: gather data and populate the database

Data transfer: move data from one database to another or

otherwise bring data together

Data analysis and presentation: provide data and information to

authorized persons

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 PRINCIPLES IN MANAGING DATA

5. Application Software should be considered disposable

Due to application independence:

- Company can replace the capture, transfer, and presentation

software modules separately if necessary

- Applications and data are not intertwined

- Aging systems do not need to be retained because of the

need to access the data stored in them

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 PRINCIPLES IN MANAGING DATA

6. Data should be captured once

• Too costly to capture data multiple times and reconcile across

applications

• Instead, data should be captured once and synchronized across

different databases

• Data architecture should include inventory of data and plan to

distribute data

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 PRINCIPLES IN MANAGING DATA

7. There should be strict data standards

• Data must be clearly identified and defined so that all users know
exactly what they are manipulating
• Only business managers have the knowledge necessary to set data
standards
• Database contents must be unambiguously described, and stored
in a metadata repository or data dictionary/directory (DD/D)

Data steward
A business manager responsible for the quality of data in a
particular subject or process area

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 PRINCIPLES IN MANAGING DATA
5 TYPES OF DATA STANDARDS

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 MANAGERIAL ISSUES
• Master data management (MDM):

disciplines, technologies, and methods

to ensure the currency, meaning, and quality
of reference data
within and across subject areas

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 DATA MANAGEMENT PROCESS

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 DATA MANAGEMENT PROCESS
• Plan: develop a blueprint for data and the relationships among data
across business units and functions

• Source: identify the timeliest and highest-quality source for each

data element

• Acquire and maintain: build data capture systems to acquire and

maintain data

• Define/describe and inventory: define each data entity, element, and

relationship that is being managed

• Organize and make accessible: design the database so that data can
be retrieved and reported efficiently in the format that business
managers require
o One popular method to make data accessible is to create a Data Warehouse

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 DATA MANAGEMENT PROCESS

Data Warehouse
a large data storage facility containing data on major
aspects of the enterprise

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 DATA MANAGEMENT PROCESS, CONT.
• Control quality and integrity: controls must be stored as part of data
definitions and enforced during data capture and maintenance

• Protect and secure: define rights that each manager has to access
each type of data

• Account for use: cost to capture, maintain, and report data must be
identified and reported with an accounting system

• Recover/restore and upgrade: establish procedures for recovering

damaged and upgrading obsolete hardware and software

• Determine retention and dispose: decide, on legal and other grounds,

how much data history needs to be kept

• Train and consult for effective use: train users to use data effectively

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 MANAGERIAL ISSUES
DATA MANAGEMENT POLICIES

• Two key policy areas for data governance:

- Data ownership
- Data administration

• Data governance
- Data governance council sets standards about metadata, data
ownership and access, and data infrastructure and architecture
- High-level oversight for establishing strategy, objectives, and
policies for organizational data

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 MANAGERIAL ISSUES
DATA OWNERSHIP

Rationales for data ownership:

- The need to protect personal privacy, trade secrets, etc.

Data sharing requires business management participation

- Commitment to quality data is essential for obtaining the greatest
benefits from a data resource
- Data must also be made accessible to decrease data processing
costs for the enterprise

Corporate Information Policy:

provides the foundation for managing the ownership of data

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 MANAGERIAL ISSUES
Example: Corporate Information Policy for Data Access

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 MANAGERIAL ISSUES

• Transborder data flows:

electronic movements of data that cross a country’s national
boundary for processing, storage, or data retrieval

• Data are subject to laws of exporting country

• Laws to control flows are justified by perceived need to:

- Prevent economic and cultural imperialism
- Protect domestic industry
- Protect individual privacy
- Foster international trade

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 MANAGERIAL ISSUES
DATA ADMINISTRATION UNIT

• IS unit accountable for data management in an organization

Key Functions of the Data Administration Group

• Promote and control data sharing
• Analyze the impact of changes to application systems
when data definitions change
• Maintain metadata
• Reduce redundant data and processing
• Reduce system maintenance costs and improve systems
development productivity
• Improve quality and security of data
• Insure data integrity
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 MANAGERIAL ISSUES
DATABASE ADMINISTRATOR (DBA)

• IS position with the responsibility for managing an

organization’s electronic databases

Key Functions of the Database Administrator

• Tuning database management systems
• Selection and evaluation of and training on database technology
• Physical database design
• Design of methods to recover from damage to databases
• Physical placement of databases on specific computers and storage devices
• The interface of databases with telecommunications and other technologies

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