Transcript Ch 4 - Data Resource

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
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Network
Data
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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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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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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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DATA MODELING
LOGICAL DESIGN NOTATION
ERD Example:
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Convert ERD to relations:
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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.
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Source: Kenneth C. Laudon and Jane P. Laudon
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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
Start with high-level model and fill in
details as major systems projects
undertaken
Timing
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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 > ‘04/12/11’ AND
Customer.CustomerID = Order.CustomerID
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MANAGERIAL ISSUES
PRINCIPLES IN MANAGING DATA
1.
2.
3.
4.
5.
6.
7.
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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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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COPYRIGHT
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
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