1. MANAGING INFORMATION TECHNOLOGY 7th EDITION
CHAPTER 4
THE DATA RESOURCE
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2. PART 1: IT BUILDING BLOCKS
Building Blocks of Information Technology
Hardware
Software
Network
Data
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3. Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall
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?
- Do you know all the contacts a customer has with your organization?
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4. 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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5. 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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7. DATA MODEL: LOGICAL DESIGN PHASE
NOTATION
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
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8. LOGICAL DESIGN NOTATION
DATA MODELING
LOGICAL DESIGN NOTATION
ERD Example: Convert ERD to relations:
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9. 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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10. Future-oriented Corporate Data Model
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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11. 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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12. 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.
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Source: Kenneth C. Laudon and Jane P. Laudon

13. 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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14. 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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15. 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 > ‘04/12/11’ AND
Customer.CustomerID = Order.CustomerID
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16. PRINCIPLES IN MANAGING DATA
MANAGERIAL ISSUES
PRINCIPLES IN MANAGING DATA
The need to manage data is permanent.
Data can exist at several levels within the organization.
Application software should be separate from the database.
Application software can be classified by how it treats data.
Application software should be considered disposable.
Data should be captured once.
There should be strict data standards.
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MANAGERIAL ISSUES
PRINCIPLES
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18. PRINCIPLES IN MANAGING DATA
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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19. 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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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
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21. PRINCIPLES IN MANAGING DATA
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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
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23. 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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24. 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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25. 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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26. 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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28. DATA MANAGEMENT PROCESS
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29. 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
One popular method to make data accessible is to create a Data Warehouse
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30. DATA MANAGEMENT PROCESS
Data Warehouse
a large data storage facility containing data on major aspects of the enterprise
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31. 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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32. DATA MANAGEMENT POLICIES
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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33. Corporate Information Policy:
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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36. IS unit accountable for data management in an organization
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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37. 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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38. Copyright Copyright © 2012 Pearson Education, Inc.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.
Copyright © 2012 Pearson Education, Inc.  
Publishing as Prentice Hall
Copyright © 2011 Pearson Education, Inc. publishing as Prentice Hall