Data Quality For The Information Age

By: Thomas C. Redman, A. Blanton Godfrey (Foreword by)

Write A Review

Hardcover | 1 January 1997

At a Glance

Hardcover
332 Pages

Dimensions(cm)
24.13 x 16.51 x 2.54

Hardcover

$211.99

or 4 interest-free payments of $53.00 with

Ships in 15 to 25 business days

This informative book goes beyond the technical aspects of data management to provide detailed analyses of quality problems and their impacts, potential solutions and how they are combined to form an overall data quality program, senior management's role, methods used to make improvements, and the life-cycle of data quality. It concludes with case studies, summaries of main points, roles and responsibilities for each individual, and a helpful listing of "dos and don'ts".

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $79.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.

Defective items

If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.

For more info please visit our Help Centre.

You Can Find This Book In

Non-Fiction Computing & I.T.Databases Database Design & Theory Business Applications Enterprise Software Business & Management Business Mathematics & Systems Data Capture & Analysis

Acknowledgments	p. xiii
Foreword	p. xvii
Preface	p. xxi
	p. 1
Why Care About Data Quality?	p. 3
Introduction	p. 3
Poor Data Quality Is Pervasive	p. 4
Poor Data Quality Impacts Business Success	p. 6
Poor Data Quality Lowers Customer Satisfaction	p. 6
Poor Data Quality Leads to High and Unnecessary Costs	p. 7
Poor Data Quality Lowers Job Satisfaction and Breeds Organizational Mistrust	p. 9
Poor Data Quality Impacts Decision Making	p. 9
Poor Data Quality Impedes Re-engineering	p. 10
Poor Data Quality Hinders Long-Term Business Strategy	p. 11
Data Fill the White Space on the Organization Chart	p. 11
The Enabling Role of Information Technology	p. 12
Data Quality Can Be a Unique Source of Competitive Advantage	p. 12
Summary	p. 13
References	p. 14
Strategies for Improving Data Accuracy	p. 17
Introduction	p. 17
Background	p. 19
Quality, Data, and Data Quality	p. 19
Choice 1: Error Detection and Correction	p. 22
Process Control and Improvement	p. 25
Process Design	p. 27
Which Data to Improve?	p. 27
Improving Data Accuracy for One Database	p. 29
Improving Data Accuracy for Two Databases	p. 30
Improving Data Accuracy in the Data Warehouse	p. 32
Summary	p. 33
References	p. 34
Data Quality Policy	p. 37
Introduction	p. 37
What Should a Data Policy Cover?	p. 38
The Data Asset in a Typical Enterprise	p. 38
What a Data Policy Can Cover	p. 40
Needed Background on Data	p. 41
Differences Between Data and Other Assets	p. 41
Who Uses the Data	p. 44
A Model Data Policy	p. 46
Model Data Policy	p. 47
Deploying the Policy	p. 49
Summary	p. 52
References	p. 53
Starting and Nurturing a Data Quality Program	p. 55
Introduction	p. 55
A Model for Successful Change	p. 58
Pressure for Change	p. 58
Clear, Shared Vision	p. 59
Capacity for Change	p. 60
Actionable First Steps	p. 61
Getting Started	p. 61
Growth Stages	p. 63
Becoming Part of the Mainstream	p. 64
The Role of Senior Management	p. 66
Summary	p. 67
References	p. 67
Data Quality and Re-engineering at ATandT	p. 69
Introduction	p. 69
Background	p. 70
First Steps	p. 73
Improve Bill Verification	p. 73
Prototype with Cincinnati Bell	p. 77
Re-engineering	p. 77
Business Direction	p. 78
Program Administration	p. 79
Management Responsibilities	p. 80
Operational Plan for Improvement	p. 81
Summary	p. 83
References	p. 84
Data Quality Across the Corporation: Telstra's Experiences	p. 85
Introduction	p. 85
Program Definition	p. 87
First Steps	p. 89
Full Program	p. 90
Results	p. 94
Summary	p. 95
References	p. 96
	p. 97
Managing Information Chains	p. 99
Introduction	p. 99
Future Performance of Processes	p. 104
Step 1: Establish a Process Owner and Management Team	p. 105
Step 2: Describe the Process and Understand Customer Needs	p. 107
Step 3: Establish a Measurement System	p. 110
Step 4: Establish Statistical Control and Check Conformance to Requirements	p. 111
Step 5: Identify Improvement Opportunities	p. 112
Step 6: Select Opportunities	p. 113
Step 7: Make and Sustain Improvements	p. 114
Summary	p. 117
References	p. 118
Process Representation and the Functions of Information Processing Approach	p. 119
Introduction	p. 119
Basic Ideas	p. 120
The Information Model/The FIP Chart	p. 122
The FIP Row	p. 122
The Process Instruction Row	p. 123
The IIPs/OIPs Rows	p. 124
The Physical Devices Row	p. 125
The Person/Organization Row	p. 125
An Example--an Employee Move	p. 125
Enhancements to the Basic Information Model	p. 129
Pictorial Representation	p. 130
Exception, Alternative, and Parallel Processes	p. 131
Measurement and Improvement Opportunities	p. 134
Accuracy	p. 134
Timeliness	p. 134
Cues for Improvement	p. 134
Summary	p. 136
References	p. 137
Data Quality Requirements	p. 139
Introduction	p. 139
Quality Function Deployment	p. 140
Data Quality Requirements for an Existing Information Chain	p. 141
Step 1: Understand Customers' Requirements	p. 142
Step 2: Develop a Set of Consistent Customer Requirements	p. 142
Step 3: Translate Customer Requirements into Technical Language	p. 145
Step 4: Map Data Quality Requirements into Individual Performance Requirements	p. 146
Step 5: Establish Performance Specifications for Processes	p. 148
Summary Remarks	p. 148
Data Quality Requirements at the Design Stage	p. 149
Background and Motivation	p. 149
The Complete Job--the Entire Data Life Cycle	p. 150
The Methodology Applied at the Design Stage	p. 151
Summary	p. 152
References	p. 154
Statistical Quality Control	p. 155
Introduction	p. 155
Variation	p. 158
Sources of Variation	p. 159
Stable Processes	p. 162
Judgment of Stability	p. 164
Control Limits: Statistical Theory and Methods of SQC	p. 165
The Underlying Theory	p. 165
Formulae	p. 167
Interpreting Control Charts	p. 174
Conformance to Requirements	p. 181
Summary	p. 181
Notes on References	p. 182
References	p. 182
Measurement Systems, Data Tracking, and Process Improvement	p. 185
Introduction	p. 185
Measurement Systems	p. 186
Process Requirements	p. 189
What to Measure	p. 190
The Measuring Device and Protocol: Data Tracking	p. 191
Philosophy	p. 191
Step 1: Sampling	p. 193
Step 2: Tracking	p. 194
Step 3: Identify Errors and Calculate Process Cycle Times	p. 194
Step 4: Summarize Results	p. 196
Implementation	p. 209
Summary	p. 211
References	p. 212
	p. 213
Just What Is (or Are) Data?	p. 215
Introduction	p. 215
The Data Life Cycle	p. 217
Preliminaries	p. 218
Acquisition Cycle	p. 219
Usage Cycle	p. 222
Checkpoints, Feedback Loops, and Data Destruction	p. 224
Discussion	p. 225
Data Defined	p. 227
Preliminaries	p. 227
Competing Definitions	p. 227
A Set of Facts	p. 228
The Result of Measurement	p. 228
Raw Material for Information	p. 228
Surrogates for Real-World Objects	p. 229
Representable Triples	p. 229
Discussion	p. 230
Management Properties of Data	p. 232
How Data Differ From Other Resources	p. 233
Implications for Data Quality	p. 235
A Model of an Enterprise's Data Resource	p. 236
Information	p. 237
Summary	p. 239
References	p. 240
Dimensions of Data Quality	p. 245
Introduction	p. 245
Quality Dimensions of a Conceptual View	p. 246
Content	p. 248
Scope	p. 249
Level of Detail	p. 249
Composition	p. 250
View Consistency	p. 252
Reaction to Change	p. 252
Quality Dimensions of Data Values	p. 254
Accuracy	p. 255
Completeness	p. 256
Currency and Related Dimensions	p. 258
Value Consistency	p. 259
Quality Dimensions of Data Representation	p. 260
Appropriateness	p. 261
Interpretability	p. 261
Portability	p. 262
Format Precision	p. 262
Format Flexibility	p. 262
Ability to Represent Null Values	p. 262
Efficient Usage of Recording Media	p. 263
Representation Consistency	p. 263
More on Data Consistency	p. 263
Summary	p. 266
References	p. 267
	p. 271
Summary: Roles and Responsibilities	p. 273
Introduction	p. 273
Roles for Leaders	p. 274
Roles for Process Owners	p. 277
Roles for Information Professionals	p. 281
Design Principle: Process Management	p. 283
Design Principle: Measurement Systems	p. 284
Design Principle: Data Architecture	p. 284
Design Principle: Cycle Time	p. 285
Design Principle: Data Values	p. 285
Design Principle: Redundancy in Data Storage	p. 285
Design Principle: Computerization	p. 286
Design Principle: Data Transformations and Transcription	p. 286
Design Principle: Value Creation	p. 286
Design Principle: Data Destruction	p. 287
Design Principle: Editing	p. 287
Design Principle: Coding	p. 287
Design Principle: Single-Fact Data	p. 288
Design Principle: Data Dictionaries	p. 288
Final Remarks--The Three Most Important Points	p. 288
Glossary	p. 289
About the Author	p. 295
Index	p. 297
Table of Contents provided by Syndetics. All Rights Reserved.

Data Quality For The Information Age

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

More in Data Capture & Analysis

SPSS Survival Manual

7th Edition - A step by step guide to data analysis using IBM SPSS

Emerging Trends in Information Systems and Technologies

WorldCIST 2025 Volume 2

Storytelling with Data

A Data Visualization Guide for Business Professionals, 10th Anniversary Edition

Tools and Applications of Data Mining

Big Data Analytics

A Practical Guide

The Data Science and Psychology

Data Mining

Concepts, Techniques and Applications

The Data Warehouse Toolkit 3ed

The Definitive Guide to Dimensional Modeling

Microsoft Power BI For Dummies

For Dummies (Computer/Tech)

Hands-On Programming with R

Write Your Own Functions and Simulations

Mindf*ck

Inside Cambridge Analytica's Plot to Break the World

Social Research Methods

4th Edition

Data Analytics for Accounting ISE

3rd Edition

Don't Make Me Think, Revisited

A Common Sense Approach to Web Usability

Apache Iceberg: The Definitive Guide

Data Lakehouse Functionality, Performance, and Scalability on the Data Lake

Data Governance: The Definitive Guide

People, Processes, and Tools to Operationalize Data Trustworthiness

Modern Data Analytics in Excel

Using Power Query, Power Pivot and More for Enhanced Data Analytics

Web Scraping with Python

3rd Edition - Data Extraction from the Modern Web

Interactively exploring high-dimensional data and models in R

1st Edition

Interactively exploring high-dimensional data and models in R

1st Edition

Data Visualization with Microsoft Power Bi

How to Design Savvy Dashboards

Learning Data Science

Data Wrangling, Exploration, Visualization, and Modeling with Python

Microsoft Power BI For Dummies

For Dummies (Computer/Tech)

Python Polars: The Definitive Guide

Transforming, Analyzing, and Visualizing Data with a Fast and Expressive Dataframe API

This product is categorised by