Enterprise knowledge management: the data quality approach
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
San Diego [u.a.]
Morgan Kaufmann
2001
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| Subjects: | |
| Online Access: | Publisher description Table of contents Inhaltsverzeichnis |
| Item Description: | Includes index |
| Physical Description: | xviii, 493 S. graph. Darst. |
| ISBN: | 0124558402 9780124558403 |
Staff View
MARC
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| 650 | 4 | |a Business enterprises |x Data processing |x Management | |
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Record in the Search Index
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|---|---|
| adam_text | CONTENTS
PREFACE xiii
1
INTRODUCTION
1.1 Data Quality Horror Stories 2
1.2 Knowledge Management and Data Quality 7
1.3 Reasons for Caring About Data Quality 10
1.4 Knowledge Management and Business Rules 15
1.5 Structure of This Book 17
2
WHO OWNS INFORMATION?
2.1 The Information Factory 26
2.2 Complicating Notions 28
2.3 Responsibilities of Ownership 31
2.4 Ownership Paradigms 33
2.5 Centralization, Decentralization, and Data
Ownership Policies 38
2.6 Ownership and Data Quality 43
2.7 Summary 46
3
DATA QUALITY IIM PRACTICE
3.1 Data Quality Defined: Fitness for Use 47
3.2 The Data Quality Improvement Program 49
3.3 Data Quality and Operations 54
3.4 Data Quality and Databases 55
3.5 Data Quality and the Data Warehouse 61
3.6 Data Mining 63
3.7 Data Quality and Electronic Data Interchange 65
3.8 Data Quality and the World Wide Web 67
3.9 Summary 71
4
ECONOMIC FRAMEWORK OF DATA QUALITY AND
THE VALUE PROPOSITION
4.1 Evidence of Economic Impact 74
4.2 Data Flows and Information Chains 76
4.3 Examples of Information Chains 80
4.4 Impacts 83
4.5 Economic Measures 85
4.6 Impact Domains 85
4.7 Operational Impacts 87
4.8 Tactical and Strategic Impacts 90
4.9 Putting It All Together — The Data Quality
Scorecard 93
4.10 Adjusting the Model for Solution Costs 97
4.11 Example 97
4.12 Summary 98
5
DIMENSIONS OF DATA QUALITY
5.1 Sample Data Application 102
5.2 Data Quality of Data Models 102
5.3 Data Quality of Data Values 113
5.4 Data Quality of Data Domains 116
5.5 Data Quality of Data Presentation 117
5.6 Data Quality of Information Policy 122
5.7 Summary: Importance of the Dimensions
of Data Quality 123
6
STATISTICAL PROCESS CONTROL AND THE IMPROVEMENT CYCLE
6.1 Variation and Control 126
6.2 Control Chart 128
6.3 The Pareto Principle 128
6.4 Building a Control Chart 131
6.5 Kinds of Control Charts 132
6.6 Example: Invalid Records 135
6.7 The Goal of Statistical Process Control 137
6.8 Interpreting a Control Chart 138
6.9 Finding Special Causes 140
6.10 Maintaining Control 140
6.11 Summary 140
7
DOMAINS, MAPPINGS, AND ENTERPRISE REFERENCE DATA
7.1 Data Types 144
7.2 Operations 146
7.3 Domains 148
7.4 Mappings 154
7.5 Example: Social Security Numbers 159
7.6 Domains, Mappings, and Metadata 161
7.7 The Publish/Subscribe Model of Reference
Data Provision 163
7.8 Summary: Domains, Mappings, and
Reference Data 167
8
DATA QUALITY ASSERTIONS AND BUSINESS RULES
8.1 Data Quality Assertions 169
8.2 Data Quality Assertions as Business Rules 171
8.3 The Nine Classes of Data Quality Rules 171
8.4 Null Value Rules 171
8.5 Value Manipulation Operators and Functions 174
8.6 Value Rules 174
8.7 Domain Membership Rules 177
8.8 Domain Mappings and Relations on Finite
Defined Domains 179
8.9 Relation Rules 186
8.10 Table, Cross-Table, and Cross-Message Assertions 188
8.11 In-Process Rules 192
8.12 Operational Rules 195
8.13 Other Rules 197
8.14 Rule Management, Compilation, and Validation 197
8.15 Rule Ordering 199
8.16 Summary 201
9
MEASUREMENT AND CURRENT STATE ASSESSMENT
9.1 Identify Each Data Customer 204
9.2 Mapping the Information Chain 205
9.3 Choose Locations in the Information Chain 207
9.4 Choose a Subset of the DQ Dimensions 208
9.5 Identify Sentinel Rules 208
9.6 Measuring Data Quality 209
9.7 Measuring Data Quality of Data Models 210
9.8 Measuring Data Quality of Data Values 217
9.9 Measuring Data Quality of Data Domains 219
9.10 Measuring Data Quality of Data Presentation 221
9.11 Measuring Data Quality of Information Policy 225
9.12 Static vs Dynamic Measurement 229 /
9.13 Compiling Results 230
9.14 Summary 230
10
DATA QUALITY REQUIREMENTS
10.1 The Assessment Process, Reviewed 235
10.2 Reviewing the Assessment 237
10.3 Determining Expectations 238
10.4 Use Case Analysis 240
10.5 Assignment of Responsibility 245
10.6 Creating Requirements 245
10.7 The Data Quality Requirements 248
10.8 Summary 250
11
METADATA, GUIDELINES, AND POLICY
11.1 Generic Elements 254
11.2 Data Types and Domains 256
11.3 Schema Metadata 260
11.4 Use and Summarization 265
11.5 Historical 266
11.6 Managing Data Domains 268
11.7 Managing Domain Mappings 269
11.8 Managing Rules 270
11.9 Metadata Browsing 275
11.10 Metadata as a Driver of Policy 276
11.11 Summary 276
12
RULE-BASED DATA QUALITY
12.1 Rule Basics 280
12.2 What Is a Business Rule? 281
12.3 Data Quality Rules Are Business Rules
(and Vice Versa) 282
12.4 What Is a Rule-Based System? 283
12.5 Advantages of the Rule-Based Approach 284
12.6 Integrating a Rule-Based System 286
12.7 Rule Execution 287
12.8 Deduction vs Goal-Orientation 289
12.9 Evaluation of a Rules System 291
12.10 Limitations of the Rule-based Approach 294
12.11 Rule-Based Data Quality 295
12.12 Summary 300
13
METADATA AND RULE DISCOVERY
13.1 Domain Discovery 302
13.2 Mapping Discovery 31B
13.3 Clustering for Rule Discovery 319
13.4 Key Discovery 326
13.5 Decision and Classification Trees 327
13.6 Association Rules and Data Quality Rules 330
13.7 Summary 331
14
DATA CLEANSING
14.1 Standardization 335
14.2 Common Error Paradigms 340
14.3 Record Parsing 344
14.4 Metadata Cleansing 348
14.5 Data Correction and Enhancement 349
14.6 Approximate Matching and Similarity 354
14.7 Consolidation 364
14.8 Updating Missing Fields 372
14.9 Address Standardization 373
14.10 Summary 379
15
ROOT CAUSE ANALYSIS AND SUPPLIER MANAGEMENT
15.1 What Is Root Cause Analysis? 382
15.2 Debugging the Process 385
15.3 Debugging the Problem 389
15.4 Corrective Measures — Resolve or Not? 391
15.5 Supplier Management 394
15.6 Summary 396
16
DATA ENRICHMENT/ENHANCEMENT
16.1 What Is Data Enhancement? 400
16.2 Examples of Data Enhancement 400
16.3 Enhancement Through Standardization 404
16.4 Enhancement Through Provenance 405
16.5 Enhancement Through Context 406
16.6 Enhancement Through Data Merging 407
16.7 Data Matching, Merging, and Record Linkage 412
16.8 Large-Scale Data Aggregation and Linkage 413
16.9 Improving Linkage with Approximate Matching 416
16.10 Enhancement Through Inference 420
16.11 Data Quality Rules for Enhancement 421
16.12 Business Rules for Enhancement 422
16.13 Summary 423
17
DATA QUALITY AND BUSINESS RULES IN PRACTICE
17.1 Turning Rules into Implementation 426
17.2 Operational Directives 445
17.3 Data Quality and the Transaction Factory 451
17.4 Data Quality and the Data Warehouse 455
17.5 Rules and EDI 456
17.6 Data Quality Rules and Automated Uls 457
17.7 Summary 460
18
BUILDING THE DATA QUALITY PRACTICE
18.1 Step 1: Recognize the Problem 463
18.2 Step 2: Management Support and the
Data Ownership Policy 464
18.3 Step 3: Spread the Word 467
18.4 Step 4: Mapping the Information Chain 468
18.5 Step 5: Data Quality Scorecard 470
18.6 Step 6: Current State Assessment 472
18.7 Step 7: Requirements Assessment 473
18.8 Step 8: Choose a Project 474
18.9 Step 9: Build Your Team 475
18.10 Step 10: Build Your Arsenal 476
18.11 Step 11: Metadata Model 479
18.12 Step 12: Define Data Quality Rules 479
18.13 Step 13: Archaeology/Data Mining 480
18.14 Step 14: Manage Your Suppliers 481
18.15 Step 15: Execute the Improvement 481
18.16 Step 16: Measure Improvement 483
18.17 Step 17: Build on Each Success 483
18.18 Conclusion 484
INDEX 485
BIBLIOGRAPHY 494
|
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| spelling | Loshin, David Verfasser aut Enterprise knowledge management the data quality approach David Loshin San Diego [u.a.] Morgan Kaufmann 2001 xviii, 493 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes index Data warehousing Datenverarbeitung Unternehmen Business enterprises Data processing Management Industrial management Data processing Information technology Management Knowledge management Wissensmanagement (DE-588)4561842-2 gnd rswk-swf Data-Warehouse-Konzept (DE-588)4406462-7 gnd rswk-swf Informationsmanagement (DE-588)4114012-6 gnd rswk-swf Wissensmanagement (DE-588)4561842-2 s Informationsmanagement (DE-588)4114012-6 s Data-Warehouse-Konzept (DE-588)4406462-7 s DE-604 http://www.loc.gov/catdir/description/els031/00112074.html Publisher description http://www.loc.gov/catdir/toc/els031/00112074.html Table of contents HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017064223&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Loshin, David Enterprise knowledge management the data quality approach Data warehousing Datenverarbeitung Unternehmen Business enterprises Data processing Management Industrial management Data processing Information technology Management Knowledge management Wissensmanagement (DE-588)4561842-2 gnd Data-Warehouse-Konzept (DE-588)4406462-7 gnd Informationsmanagement (DE-588)4114012-6 gnd |
| subject_GND | (DE-588)4561842-2 (DE-588)4406462-7 (DE-588)4114012-6 |
| title | Enterprise knowledge management the data quality approach |
| title_auth | Enterprise knowledge management the data quality approach |
| title_exact_search | Enterprise knowledge management the data quality approach |
| title_full | Enterprise knowledge management the data quality approach David Loshin |
| title_fullStr | Enterprise knowledge management the data quality approach David Loshin |
| title_full_unstemmed | Enterprise knowledge management the data quality approach David Loshin |
| title_short | Enterprise knowledge management |
| title_sort | enterprise knowledge management the data quality approach |
| title_sub | the data quality approach |
| topic | Data warehousing Datenverarbeitung Unternehmen Business enterprises Data processing Management Industrial management Data processing Information technology Management Knowledge management Wissensmanagement (DE-588)4561842-2 gnd Data-Warehouse-Konzept (DE-588)4406462-7 gnd Informationsmanagement (DE-588)4114012-6 gnd |
| topic_facet | Data warehousing Datenverarbeitung Unternehmen Business enterprises Data processing Management Industrial management Data processing Information technology Management Knowledge management Wissensmanagement Data-Warehouse-Konzept Informationsmanagement |
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