Managing information systems 7th edition brow ch04

publishing as Prentice Hall 4-4TECHNICAL ASPECTS OF MANAGING DATA • An overall “map” for business data • Involves: • A methodology process to identify and describe data entities • A nota

MANAGEMENT INFORMATION SYSTEMS

CHAPTER 4

THE DATA RESOURCE

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-2

PART 1: IT BUILDING BLOCKS

WHY MANAGE DATA?

- What costs would your company incur if it did not comply with

SOX or other financial reporting laws?

- What would your company do if its critical business data were

destroyed?

- What costs would your company incur if sensitive data were stolen

or you violated HIPAA requirements to protect healthcare data?

- How much time does your company spend reconciling inconsistent data?

- How difficult is it to determine what data are stored about the part

of the business you manage?

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-4

TECHNICAL ASPECTS OF MANAGING DATA

• An overall “map” for business data

• Involves:

• A methodology (process) to identify and describe data entities

• A notation = a way to describe data entities

DATA MODELS

DATA MODEL: CONCEPTUAL DESIGN PHASE

- 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)

ENTITY-RELATIONSHIP DIAGRAM (ERD)

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-6

TECHNICAL ASPECTS

• 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

METADATA

DATA MODEL: LOGICAL DESIGN PHASE

• ERDs are converted into sets of Relations, or Tables:

– Structure consisting of rows and columns– Each row represents a single entity

– Each column represents an attribute

NOTATION

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-8

DATA MODELING

ERD Example: Convert ERD to relations:

LOGICAL DESIGN NOTATION

TECHNICAL ASPECTS: DATA 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

ENTERPRISE MODELING

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-10

to each activity – Check for consistent names

Future-oriented Corporate Data Model

TECHNICAL ASPECTS: DATA MODELING

– Normalize user views– Combine user views – Reconcile any differences with enterprise model

VIEW INTEGRATION

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-12

• The process of creating simple data structures from more complex ones

using a set of rules that yields a stable structure.

NORMALIZATION

Source: Kenneth C Laudon and Jane P Laudon

TECHNICAL ASPECTS: DATA MODELING TECHNICAL ASPECTS: DATA MODELING

TECHNICAL ASPECTS: DATA MODELING

• 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

PACKAGED (UNIVERSAL) DATA MODELS

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-14

TECHNICAL ASPECTS: DATA MODELING

Objective Some overriding need

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

DATA MODELING GUIDELINES

TECHNICAL ASPECTS: DATA MODELING

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

DATABASE PROGRAMMING

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-16

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

MANAGERIAL ISSUES

PRINCIPLES

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-18

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

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-20

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

PRINCIPLES IN MANAGING DATA

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-22

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

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-24

PRINCIPLES IN MANAGING DATA

6 Data should be captured once

• Too costly to capture data multiple times and reconcile across

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-26

PRINCIPLES IN MANAGING DATA

5 TYPES OF DATA STANDARDS

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-28

DATA MANAGEMENT PROCESS

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-30

DATA MANAGEMENT PROCESS

Data Warehouse

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

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-32

- 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

DATA MANAGEMENT POLICIES

MANAGERIAL ISSUES

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

DATA OWNERSHIP

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-34

MANAGERIAL ISSUES

Example: Corporate Information Policy for Data Access

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

Copyright © 2011 Pearson Education, Inc publishing as Prentice Hall 4-36

MANAGERIAL ISSUES

• 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

DATA ADMINISTRATION UNIT

MANAGERIAL ISSUES

• 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

DATABASE ADMINISTRATOR (DBA)