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Data Quality for the Information Age [Kõva köide]

  • Formaat: Hardback, 332 pages, kõrgus x laius x paksus: 229x152x22 mm, kaal: 657 g, black & white illustrations
  • Sari: Computer Science Library
  • Ilmumisaeg: 01-Jan-1997
  • Kirjastus: Artech House Publishers
  • ISBN-10: 0890068836
  • ISBN-13: 9780890068830
Teised raamatud teemal:
Data Quality for the Information AgeSuurem pilt
  • Formaat: Hardback, 332 pages, kõrgus x laius x paksus: 229x152x22 mm, kaal: 657 g, black & white illustrations
  • Sari: Computer Science Library
  • Ilmumisaeg: 01-Jan-1997
  • Kirjastus: Artech House Publishers
  • ISBN-10: 0890068836
  • ISBN-13: 9780890068830
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780890068830.html
Märksõnad:
Provides business leaders, process owners, and information professionals with background and information for setting up a data quality program, making and sustaining order-of-magnitude improvements, and creating a sneaky business advantage. Discusses problems of data quality and their impact, potential solutions and how they dovetail into an overall program, the role of senior management, and life cycles and dimensions of data quality. Includes a glossary without pronunciation. Annotation c. by Book News, Inc., Portland, Or.
Acknowledgments xiii(4)
Foreword xvii(4)
Preface xxi
Part I 1(96)
Chapter 1 Why Care About Data Quality?
3(14)
1.1 Introduction
3(1)
1.2 Poor Data Quality Is Pervasive
4(2)
1.3 Poor Data Quality Impacts Business Success
6(6)
1.3.1 Poor Data Quality Lowers Customer Satisfaction
6(1)
1.3.2 Poor Data Quality Leads to High and Unnecessary Costs
7(2)
1.3.3 Poor Data Quality Lowers Job Satisfaction and Breeds Organizational Mistrust
9(1)
1.3.4 Poor Data Quality Impacts Decision Making
9(1)
1.3.5 Poor Data Quality Impedes Re-engineering
10(1)
1.3.6 Poor Data Quality Hinders Long-Term Business Strategy
11(1)
1.3.7 Data Fill the White Space on the Organization Chart
11(1)
1.3.8 The Enabling Role of Information Technology
12(1)
1.4 Data Quality Can Be a Unique Source of Competitive Advantage
12(1)
1.5 Summary
13(1)
References
14(3)
Chapter 2 Strategies for Improving Data Accuracy
17(20)
2.1 Introduction
17(2)
2.2 Background
19(8)
2.2.1 Quality, Data, and Data Quality
19(3)
2.2.2 Choice 1: Error Detection and Correction
22(3)
2.2.3 Process Control and Improvement
25(2)
2.2.4 Process Design
27(1)
2.3 Which Data to Improve?
27(2)
2.4 Improving Data Accuracy for One Database
29(1)
2.5 Improving Data Accuracy for Two Databases
30(2)
2.6 Improving Data Accuracy in the Data Warehouse
32(1)
2.7 Summary
33(1)
References
34(3)
Chapter 3 Data Quality Policy
37(18)
3.1 Introduction
37(1)
3.2 What Should a Data Policy Cover?
38(3)
3.2.1 The Data Asset in a Typical Enterprise
38(2)
3.2.2 What a Data Policy Can Cover
40(1)
3.3 Needed Background on Data
41(5)
3.3.1 Differences Between Data and Other Assets
41(3)
3.3.2 Who Uses the Data
44(2)
3.4 A Model Data Policy
46(3)
3.4.1 Model Data Policy
47(2)
3.5 Deploying the Policy
49(3)
3.6 Summary
52(1)
References
53(2)
Chapter 4 Starting and Nurturing a Data Quality Program
55(14)
4.1 Introduction
55(3)
4.2 A Model for Successful Change
58(3)
4.2.1 Pressure for Change
58(1)
4.2.2 Clear, Shared Vision
59(1)
4.2.3 Capacity for Change
60(1)
4.2.4 Actionable First Steps
61(1)
4.3 Getting Started
61(2)
4.4 Growth Stages
63(1)
4.5 Becoming Part of the Mainstream
64(2)
4.6 The Role of Senior Management
66(1)
4.7 Summary
67(1)
References
67(2)
Chapter 5 Data Quality and Re-engineering at AT&T
69(16)
5.1 Introduction
69(1)
5.2 Background
70(3)
5.3 First Steps
73(4)
5.3.1 Improve Bill Verification
73(4)
5.3.2 Prototype with Cincinnati Bell
77(1)
5.4 Re-engineering
77(6)
5.4.1 Business Direction
78(1)
5.4.2 Program Administration
79(1)
5.4.3 Management Responsibilities
80(1)
5.4.4 Operational Plan for Improvement
81(2)
5.5 Summary
83(1)
References
84(1)
Chapter 6 Data Quality Across the Corporation: Telstra's Experiences
85(12)
6.1 Introduction
85(2)
6.2 Program Definition
87(2)
6.3 First Steps
89(1)
6.4 Full Program
90(4)
6.5 Results
94(1)
6.6 Summary
95(1)
References
96(1)
Part II 97(116)
Chapter 7 Managing Information Chains
99(20)
7.1 Introduction
99(5)
7.2 Future Performance of Processes
104(13)
7.2.1 Step 1: Establish a Process Owner and Management Team
105(2)
7.2.2 Step 2: Describe the Process and Understand Customer Needs
107(3)
7.2.3 Step 3: Establish a Measurement System
110(1)
7.2.4 Step 4: Establish Statistical Control and Check Conformance to Requirments
111(1)
7.2.5 Step 5: Identify Improvement Opportunities
112(1)
7.2.6 Step 6: Select Opportunities
113(1)
7.2.7 Step 7: Make and Sustain Improvements
114(3)
7.3 Summary
117(1)
References
118(1)
Chapter 8 Process Representation and the Functions of Information Processing Approach
119(20)
8.1 Introduction
119(1)
8.2 Basic Ideas
120(2)
8.3 The Information Model/The FIP Chart
122(7)
8.3.1 The FIP Row
122(1)
8.3.2 The Process Instruction Row
123(1)
8.3.3 The IIPs/OIPs Rows
124(1)
8.3.4 The Physical Devices Row
125(1)
8.3.5 The Person/Organization Row
125(1)
8.3.6 An Example--an Employee Move
125(4)
8.4 Enhancements to the Basic Information Model
129(5)
8.4.1 Pictorial Representation
130(1)
8.4.2 Exception, Alternative, and Parallel Processes
131(3)
8.5 Measurement and Improvement Opportunities
134(2)
8.5.1 Accuracy
134(1)
8.5.2 Timeliness
134(1)
8.5.3 Cues for Improvement
134(2)
8.6 Summary
136(1)
References
137(2)
Chapter 9 Data Quality Requirements
139(16)
9.1 Introduction
139(1)
9.2 Quality Function Deployment
140(1)
9.3 Data Quality Requirements for an Existing Information Chain
141(8)
9.3.1 Step 1: Understand Customers' Requirements
142(1)
9.3.2 Step 2: Develop a Set of Consistent Customer Requirements
142(3)
9.3.3 Step 3: Translate Customer Requirements into Technical Language
145(1)
9.3.4 Step 4: Map Data Quality Requirements into Individual Performance Requirments
146(2)
9.3.5 Step 5: Establish Performance Specifications for Processes
148(1)
9.3.6 Summary Remarks
148(1)
9.4 Data Quality Requirements at the Design Stage
149(3)
9.4.1 Background and Motivation
149(1)
9.4.2 The Complete Job--the Entire Data Life Cycle
150(1)
9.4.3 The Methodology Applied at the Design Stage
151(1)
9.5 Summary
152(2)
References
154(1)
Chapter 10 Statistical Quality Control
155(30)
10.1 Introduction
155(3)
10.2 Variation
158(4)
10.2.1 Sources of Variation
159(3)
10.3 Stable Processes
162(3)
10.3.1 Judgment of Stability
164(1)
10.4 Control Limits: Statistical Theory and Methods of SQC
165(9)
10.4.1 The Underlying Theory
165(2)
10.4.2 Formulae
167(7)
10.5 Interpreting Control Charts
174(7)
10.6 Conformance to Requirements
181(1)
10.7 Summary
181(1)
10.8 Notes on References
182(1)
References
182(3)
Chapter 11 Measurement Systems, Data Tracking, and Process Improvement
185(28)
11.1 Introduction
185(1)
11.2 Measurement Systems
186(3)
11.3 Process Requirements
189(1)
11.4 What to Measure
190(1)
11.5 The Measuring Device and Protocol: Data Tracking
191(18)
11.5.1 Philosophy
191(2)
11.5.2 Step 1: Sampling
193(1)
11.5.3 Step 2: Tracking
194(1)
11.5.4 Step 3: Identify Errors and Calculate Process Cycle Times
194(2)
11.5.5 Step 4: Summarize Results
196(13)
11.6 Implementation
209(2)
11.7 Summary
211(1)
References
212(1)
Part III 213(58)
Chapter 12 Just What Is (or Are) Data?
215(30)
12.1 Introduction
215(2)
12.2 The Data Life Cycle
217(10)
12.2.1 Preliminaries
218(1)
12.2.2 Acquisition Cycle
219(3)
12.2.3 Usage Cycle
222(2)
12.2.4 Checkpoints, Feedback Loops, and Data Destruction
224(1)
12.2.5 Discussion
225(2)
12.3 Data Defined
227(5)
12.3.1 Preliminaries
227(1)
12.3.2 Competing Definitions
227(1)
12.3.3 A Set of Facts
228(1)
12.3.4 The Result of Measurement
228(1)
12.3.5 Raw Material for Information
228(1)
12.3.6 Surrogates for Real-World Objects
229(1)
12.3.7 Representable Triples
229(1)
12.3.8 Discussion
230(2)
12.4 Management Properties of Data
232(4)
12.4.1 How Data Differ From Other Resources
233(2)
12.4.2 Implications for Data Quality
235(1)
12.5 A Model of an Enterprise's Data Resource
236(1)
12.6 Information
237(2)
12.7 Summary
239(1)
References
240(5)
Chapter 13 Dimensions of Data Quality
245(26)
13.1 Introduction
245(1)
13.2 Quality Dimensions of a Conceptual View
246(8)
13.2.1 Content
248(1)
13.2.2 Scope
249(1)
13.2.3 Level of Detail
249(1)
13.2.4 Composition
250(2)
13.2.5 View Consistency
252(1)
13.2.6 Reaction to Change
252(2)
13.3 Quality Dimensions of Data Values
254(6)
13.3.1 Accuracy
255(1)
13.3.2 Completeness
256(2)
13.3.3 Currency and Related Dimensions
258(1)
13.3.4 Value Consistency
259(1)
13.4 Quality Dimensions of Data Representation
260(3)
13.4.1 Appropriateness
261(1)
13.4.2 Interpretability
261(1)
13.4.3 Portability
262(1)
13.4.4 Format Precision
262(1)
13.4.5 Format Flexibility
262(1)
13.4.6 Ability to Represent Null Values
262(1)
13.4.7 Efficient Usage of Recording Media
263(1)
13.4.8 Representation Consistency
263(1)
13.5 More on Data Consistency
263(3)
13.6 Summary
266(1)
References
267(4)
Part IV 271(18)
Chapter 14 Summary: Roles and Responsibilities
273(16)
14.1 Introduction
273(1)
14.2 Roles for Leaders
274(3)
14.3 Roles for Process Owners
277(4)
14.4 Roles for Information Professionals
281(7)
14.4.1 Design Principle: Process Management
283(1)
14.4.2 Design Principle: Measurement Systems
284(1)
14.4.3 Design Principle: Data Architecture
284(1)
14.4.4 Design Principle: Cycle Time
285(1)
14.4.5 Design Principle: Data Values
285(1)
14.4.6 Design Principle: Redundancy in Data Storage
285(1)
14.4.7 Design Principle: Computerization
286(1)
14.4.8 Design Principle: Data Transformations and Transcription
286(1)
14.4.9 Design Principle: Value Creation
286(1)
14.4.10 Design Principle: Data Destruction
287(1)
14.4.11 Design Principle: Editing
287(1)
14.4.12 Design Principle: Coding
287(1)
14.4.13 Design Principle: Single-Fact Data
288(1)
14.4.14 Design Principle: Data Dictionaries
288(1)
14.5 Final Remarks--The Three Most Important Points
288(1)
Glossary 289(6)
About the Author 295(2)
Index 297


Dr. Thomas C. Redman is president of the Navesink Consulting Group. He led data quality programs at AT&T and AT&T Bell Labs. He holds a Ph.D. and M.S. in statistics from Florida State University. He is a member of the American Statistical Association, holder of a patent, and is published extensively.