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Copyright 2009 Pearson Education, Inc. Publishing as Prentice Hall 1
Managing Information Technology
6th Edition
CHAPTER 5
THE DATA RESOURCE
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Building Blocks of InformationTechnology
Hardware Software Network Data
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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:
Cataloged
Named in standard ways
Protected
Accessible to those with a need to know
Maintained with high quality
There are technical and managerial issues tomanaging data
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TECHNICAL ASPECTS OF DM
Data model is an overall map for business data
Data modeling involves:
Methodology, or steps followed to identify and
describe data entities
Notation, or a way to illustrate data entities
graphically
The Data Model
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TECHNICAL ASPECTS OF DM
Development process for data management system
involves six basic steps
Requ
irements Analysis
Conceptual Design
Logical Design
Physical Design
Implementation
Maintenance
The Data Model: Methodology
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TECHNICAL ASPECTS OF DM
User requirements usually gathered in text format
through personal interviews with users
Data modeled in conceptual design phase as entity-relationship diagram (ERD)
Data modeled in logical design phase as a set of
relations (tables)
The Data Model: Methodology
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TECHNICAL ASPECTS OF DM
Entity-relationship diagram (ERD)
Most common method for representing a data
model and organizational data needs
Entities: things about which data are collected
Attributes: actual elements of data that are to be
collected Relationships: relevant associations between
organizational entities
The Data Model: Notation
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TECHNICAL ASPECTS OF DM
ERD example:
Entities are Customer, Order, and Product
Relationships are Submits and Includes
The Data Model: Notation
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TECHNICAL ASPECTS OF DM
Relations (tables)
Structure consisting of rows and columns
Each row represents a single entity
Each column represents an attribute
ERDs are converted into sets of relations
The Data Model: Notation
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TECHNICAL ASPECTS OF DM
ERD example:
The Data Model: Notation
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TECHNICAL ASPECTS OF DM
Convert ERD to relations:The Data Model: Notation
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TECHNICAL ASPECTS OF DM
Data about data
Needed to unambiguously describe data for
the enterprise
Documents the meaning of all the business
rules that govern data
Cannot have quality data without high-quality
metadata
Metadata
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TECHNICAL ASPECTS OF DM
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
Data Modeling
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TECHNICAL ASPECTS OF DM
Enterprise modelingsteps: Divide work into major
functions Divide each function into
processes
Divide processes intoactivities
List data entities assignedto each activity
Identify relationshipsbetween entities
Data Modeling
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TECHNICAL ASPECTS OF DM
View integration
Bottom-up approach
Each report, screen, form, and document
produced from databases (called user views)
identified first
Data Modeling
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TECHNICAL ASPECTS OF DM
View integration steps:
Create user views
Identify data elements in each user view and put intoa 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
Data Modeling
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TECHNICAL ASPECTS OF DM
Prepackaged data models an alternative toenterprise data modeling
Advantages: Developed using proven, up-to-date components
Require less time and money
Easier to evolve data model Greater application compatibility
Easier to share data across organizations
Data Modeling
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TECHNICAL ASPECTS OF DM
Data Modeling Guidelines
Objective Modeling effort must be justified by
some overriding need
Scope Coverage for a data model must be
carefully considered
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
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TECHNICAL ASPECTS OF DM
1. Database processing activity can be specified
with a procedural language (3GL) or
2. Special-purpose language Structured query language (e.g., SQL)
Data exchange language (e.g., XML)
Example SQL Query
SELECT ORDER_ID, CUSTOMER_ID, CUST-NAME, ORDER_DATE
FROM CUSTOMER, ORDER
WHERE ORDER_DATE > 04/12/08 AND
CUSTOMER.CUSTOMER_ID = ORDER.CUSTOMERID;
Data Programming
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MANAGERIAL ISSUES OF DM
Data values may change, but a company willalways 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
The need to manage data is permanent
Principles in Managing Data
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MANAGERIAL ISSUES OF DM
Most new data are captured in operationaldatabases
Managerial and strategic databases typicallysubsets, summaries, or aggregates of operational
databases If managerial databases are constructed from
external sources, there may be problems withdata consistency
Data can exist at several levels
Principles in Managing Data
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MANAGERIAL ISSUES OF DM
Principles in Managing Data
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MANAGERIAL ISSUES OF DM
Application independence: separation or decouplingof 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 otherapplications
Application software should be separate from the database
Principles in Managing Data
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MANAGERIAL ISSUES OF DM
Principles in Managing Data
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MANAGERIAL ISSUES OF DM
Data capture: gather data and populate thedatabase
Data transfer: move data from onedatabase to another or otherwise bring data
together
Data analysis and presentation: providedata and information to authorized persons
Application software can be classified by how it treats data
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MANAGERIAL ISSUES OF DM
Significant result of applicationindependence- Company can replace the capture, transfer, and
presentation software modules separately ifnecessary
- Applications and data are not intertwined
Obsolete systems do not need to be keptalive only to access data
Application software should be considered disposable
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MANAGERIAL ISSUES OF DM
Too costly to capture data multiple times andreconcile across applications
Instead, data should be captured once andsynchronized across different databases
Data architecture should include inventory ofdata and plan to distribute data
Data should be captured once
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MANAGERIAL ISSUES OF DM
Data mu
st be clearly identified and defined sothat all users know exactly what they aremanipulating
Only business managers have the knowledgenecessary to set data standards
Data steward: a business manager responsiblefor the quality of data in a particular subject orprocess area
There should be strict data standards
Principles in Managing Data
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MANAGERIAL ISSUES OF DM
Five types of data standards
- Identifier: Unique value for each business entity- Naming: Unique name or label for each type of
data- Definition: Unambiguous description for each type
of data
- Integrity rule: Specification of legitimate values fora type of data
- Usage rights: Security clearances for a type ofdata
There should be strict data standards (contd)
Principles in Managing Data
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MANAGERIAL ISSUES OF DM
Data standards should be stored in standardsdatabase called a metadata repository or datadictionary/directory (DD/D)
Master data man
agemen
t (MDM): disciplines,technologies, and methods to ensure thecurrency, meaning, and quality of referencedata within and across subject areas
There should be strict data standards (contd)
Principles in Managing Data
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MANAGERIAL ISSUES OF DM
The Data Management Process
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MANAGERIAL ISSUES OF DM
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 capturesystems to acquire and maintain data
The Data Management Process
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MANAGERIAL ISSUES OF DM
Define/describe and inventory: define each dataentity, element, and relationship that is beingmanaged
Organize and make accessible: design thedatabase so that data can be retrieved andreported efficiently in the format that businessmanagers require
One popular method for making data accessible is bycreating a data warehouse
A data warehouse is a large data storage facilitycontaining data on all (or at least many) aspects of theenterprise
The Data Management Process
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MANAGERIAL ISSUES OF DM
The Data Management Process
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MANAGERIAL ISSUES OF DM
Controlqualityand integrity: controls must bestored as part of data definitions and enforced
during data capture and maintenance Protectand secure: define rights that each
manager has to access each type of data
Account for use: cost to capture, maintain, andreport data must be identified and reportedwith an accounting system
The Data Management Process
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MANAGERIAL ISSUES OF DM
Recover/restore and upgrade: establishprocedures for recovering damaged and
upgrading obsolete hardware and software Determine retention and dispose: decide, on
legal and other grounds, how much datahistory needs to be kept
Train and consult for effective use: train usersto use data effectively
The Data Management Process
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MANAGERIAL ISSUES OF DM
Data governance:
Organizational process for establishing strategy,
objectives, and policies for organizational data Data governance council sets standards about
metadata, data ownership and access, and datainfrastructure and architecture
Two key policy areas for data governance: Data ownership
Data administration
Data Management Policies
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MANAGERIAL ISSUES OF DM
Data sharing requires business managementparticipation
Commitment to quality data is essential forobtaining the greatest benefits from a dataresource
Data must also be made accessible to decrease
data processing costs for the enterprise Corporate information policy: foundation for
managing the ownership of data
Data Ownership
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MANAGERIAL ISSUES OF DM
Data Ownership
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MANAGERIAL ISSUES OF DM
Transborder data flows: electronic flows ofdata that cross a countrys national boundary
Data are subject to laws of exporting country Laws justified by perceived need to:
Prevent economic and cultural imperialism
Protect domestic indu
stry Protect individual privacy
Foster international trade
Data Ownership
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MANAGERIAL ISSUES OF DM
Example transborder issue
U.S. Company Fined by E.U. for Improper Cross-
Border Data Transfer
Data Ownership
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MANAGERIAL ISSUES OF DM
Data administration group: leads data management
efforts in an organization
Key Functions ofthe Data Administration Group
Promote and control data sharing
Analyze the impact of changes to application systems when
data definitions change
Maintain metadata
Redu
ce redu
ndant data and processing Reduce system maintenance costs and improve systems
development productivity
Improve quality and security of data
Insure data integrity
Data Administration
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MANAGERIAL ISSUES OF DM
Database administrator (DBA): IS role with the
responsibility for managing computer databases
Key Functions ofthe 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
Data Administration
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permission of the publisher. Printed in the United States of America.
Copyright 2009 Pearson Education, Inc.Publishing as Prentice Hall