DAMA-DMBOK2.pdf

【实例截图】DAMA-DMBOK_Data Management Body of Knowledge, 2nd.pdf

【核心代码】

Contents
Preface_________________________________________________________ 15
Chapter 1: Data Management_______________________________________ 17
1. Introduction ____________________________________________________________ 17
2. Essential Concepts _______________________________________________________ 18
2.1 Data ______________________________________________________________________ 18
2.2 Data and Information ________________________________________________________ 20
2.3 Data as an Organizational Asset _______________________________________________ 20
2.4 Data Management Principles __________________________________________________ 21
2.5 Data Management Challenges _________________________________________________ 23
2.6 Data Management Strategy ___________________________________________________ 31
3. Data Management Frameworks ____________________________________________ 33
3.1 Strategic Alignment Model ____________________________________________________ 33
3.2 The Amsterdam Information Model ____________________________________________ 34
3.3 The DAMA-DMBOK Framework _______________________________________________ 35
3.4 DMBOK Pyramid (Aiken) _____________________________________________________ 39
3.5 DAMA Data Management Framework Evolved ___________________________________ 40
4. DAMA and the DMBOK ___________________________________________________ 43
5. Works Cited / Recommended ______________________________________________ 46
Chapter 2: Data Handling Ethics ____________________________________ 49
1. Introduction ____________________________________________________________ 49
2. Business Drivers ________________________________________________________ 51
3. Essential Concepts _______________________________________________________ 52
3.1 Ethical Principles for Data ____________________________________________________ 52
3.2 Principles Behind Data Privacy Law ____________________________________________ 53
3.3 Online Data in an Ethical Context ______________________________________________ 56
3.4 Risks of Unethical Data Handling Practices ______________________________________ 56
3.5 Establishing an Ethical Data Culture ____________________________________________ 60
3.6 Data Ethics and Governance __________________________________________________ 64
4. Works Cited / Recommended ______________________________________________ 65
Chapter 3: Data Governance________________________________________ 67
1. Introduction ____________________________________________________________ 67
1.1 Business Drivers ____________________________________________________________ 70
1.2 Goals and Principles _________________________________________________________ 71
1.3 Essential Concepts __________________________________________________________ 72
2. Activities _______________________________________________________________ 79
2.1 Define Data Governance for the Organization ____________________________________ 79
2.2 Perform Readiness Assessment _______________________________________________ 79
2.3 Perform Discovery and Business Alignment _____________________________________ 80
2.4 Develop Organizational Touch Points ___________________________________________ 81
2.5 Develop Data Governance Strategy _____________________________________________ 82
2.6 Define the DG Operating Framework ___________________________________________ 82
2.7 Develop Goals, Principles, and Policies __________________________________________ 83
2.8 Underwrite Data Management Projects _________________________________________ 84
2.9 Engage Change Management __________________________________________________ 85
2 • DMBOK2
2.10 Engage in Issue Management ________________________________________________ 86
2.11 Assess Regulatory Compliance Requirements ___________________________________ 87
2.12 Implement Data Governance _________________________________________________ 88
2.13 Sponsor Data Standards and Procedures _______________________________________ 88
2.14 Develop a Business Glossary _________________________________________________ 90
2.15 Coordinate with Architecture Groups _________________________________________ 90
2.16 Sponsor Data Asset Valuation ________________________________________________ 91
2.17 Embed Data Governance ____________________________________________________ 91
3. Tools and Techniques____________________________________________________ 92
3.1 Online Presence / Websites ___________________________________________________ 92
3.2 Business Glossary ___________________________________________________________ 92
3.3 Workflow Tools ____________________________________________________________ 93
3.4 Document Management Tools _________________________________________________ 93
3.5 Data Governance Scorecards __________________________________________________ 93
4. Implementation Guidelines _______________________________________________ 93
4.1 Organization and Culture _____________________________________________________ 93
4.2 Adjustment and Communication ______________________________________________ 94
5. Metrics ________________________________________________________________ 94
6. Works Cited / Recommended _____________________________________________ 95
Chapter 4: Data Architecture_______________________________________ 97
1. Introduction ___________________________________________________________ 97
1.1 Business Drivers ____________________________________________________________ 99
1.2 Data Architecture Outcomes and Practices _____________________________________ 100
1.3 Essential Concepts _________________________________________________________ 101
2. Activities _____________________________________________________________ 109
2.1 Establish Data Architecture Practice __________________________________________ 110
2.2 Integrate with Enterprise Architecture ________________________________________ 115
3. Tools ________________________________________________________________ 115
3.1 Data Modeling Tools________________________________________________________ 115
3.2 Asset Management Software _________________________________________________ 115
3.3 Graphical Design Applications _______________________________________________ 115
4. Techniques ___________________________________________________________ 116
4.1 Lifecycle Projections _______________________________________________________ 116
4.2 Diagramming Clarity _______________________________________________________ 116
5. Implementation Guidelines ______________________________________________ 117
5.1 Readiness Assessment / Risk Assessment ______________________________________ 118
5.2 Organization and Cultural Change ____________________________________________ 119
6. Data Architecture Governance ___________________________________________ 119
6.1 Metrics ___________________________________________________________________ 120
7. Works Cited / Recommended ____________________________________________ 120
Chapter 5: Data Modeling and Design_______________________________ 123
1. Introduction __________________________________________________________ 123
1.1 Business Drivers ___________________________________________________________ 125
1.2 Goals and Principles ________________________________________________________ 125
1.3 Essential Concepts _________________________________________________________ 126
2. Activities _____________________________________________________________ 152
2.1 Plan for Data Modeling ______________________________________________________ 152
CONTENTS • 3
2.2 Build the Data Model _______________________________________________________ 153
2.3 Review the Data Models _____________________________________________________ 158
2.4 Maintain the Data Models ___________________________________________________ 159
3. Tools _________________________________________________________________ 159
3.1 Data Modeling Tools ________________________________________________________ 159
3.2 Lineage Tools _____________________________________________________________ 159
3.3 Data Profiling Tools ________________________________________________________ 160
3.4 Metadata Repositories ______________________________________________________ 160
3.5 Data Model Patterns ________________________________________________________ 160
3.6 Industry Data Models _______________________________________________________ 160
4. Best Practices __________________________________________________________ 161
4.1 Best Practices in Naming Conventions _________________________________________ 161
4.2 Best Practices in Database Design _____________________________________________ 161
5. Data Model Governance _________________________________________________ 162
5.1 Data Model and Design Quality Management ___________________________________ 162
5.2 Data Modeling Metrics ______________________________________________________ 164
6. Works Cited / Recommended _____________________________________________ 166
Chapter 6: Data Storage and Operations _____________________________ 169
1. Introduction ___________________________________________________________ 169
1.1 Business Drivers ___________________________________________________________ 171
1.2 Goals and Principles ________________________________________________________ 171
1.3 Essential Concepts _________________________________________________________ 172
2. Activities ______________________________________________________________ 193
2.1 Manage Database Technology ________________________________________________ 194
2.2 Manage Databases _________________________________________________________ 196
3. Tools _________________________________________________________________ 209
3.1 Data Modeling Tools ________________________________________________________ 209
3.2 Database Monitoring Tools __________________________________________________ 209
3.3 Database Management Tools _________________________________________________ 209
3.4 Developer Support Tools ____________________________________________________ 209
4. Techniques ____________________________________________________________ 210
4.1 Test in Lower Environments _________________________________________________ 210
4.2 Physical Naming Standards __________________________________________________ 210
4.3 Script Usage for All Changes _________________________________________________ 210
5. Implementation Guidelines _______________________________________________ 210
5.1 Readiness Assessment / Risk Assessment ______________________________________ 210
5.2 Organization and Cultural Change ____________________________________________ 211
6. Data Storage and Operations Governance ___________________________________ 212
6.1 Metrics ___________________________________________________________________ 212
6.2 Information Asset Tracking __________________________________________________ 213
6.3 Data Audits and Data Validation ______________________________________________ 213
7. Works Cited / Recommended _____________________________________________ 214
Chapter 7: Data Security__________________________________________ 217
1. Introduction ___________________________________________________________ 217
1.1 Business Drivers ___________________________________________________________ 220
1.2 Goals and Principles ________________________________________________________ 222
1.3 Essential Concepts _________________________________________________________ 223
4 • DMBOK2
2. Activities _____________________________________________________________ 245
2.1 Identify Data Security Requirements __________________________________________ 245
2.2 Define Data Security Policy __________________________________________________ 247
2.3 Define Data Security Standards _______________________________________________ 248
3. Tools ________________________________________________________________ 256
3.1 Anti-Virus Software / Security Software _______________________________________ 256
3.2 HTTPS ___________________________________________________________________ 256
3.3 Identity Management Technology ____________________________________________ 257
3.4 Intrusion Detection and Prevention Software ___________________________________ 257
3.5 Firewalls (Prevention) ______________________________________________________ 257
3.6 Metadata Tracking _________________________________________________________ 257
3.7 Data Masking/Encryption ___________________________________________________ 258
4. Techniques ___________________________________________________________ 258
4.1 CRUD Matrix Usage ________________________________________________________ 258
4.2 Immediate Security Patch Deployment ________________________________________ 258
4.3 Data Security Attributes in Metadata __________________________________________ 258
4.4 Metrics ___________________________________________________________________ 259
4.5 Security Needs in Project Requirements _______________________________________ 261
4.6 Efficient Search of Encrypted Data ____________________________________________ 262
4.7 Document Sanitization ______________________________________________________ 262
5. Implementation Guidelines ______________________________________________ 262
5.1 Readiness Assessment / Risk Assessment ______________________________________ 262
5.2 Organization and Cultural Change ____________________________________________ 263
5.3 Visibility into User Data Entitlement __________________________________________ 263
5.4 Data Security in an Outsourced World _________________________________________ 264
5.5 Data Security in Cloud Environments __________________________________________ 265
6. Data Security Governance _______________________________________________ 265
6.1 Data Security and Enterprise Architecture _____________________________________ 265
7. Works Cited / Recommended ____________________________________________ 266
Chapter 8: Data Integration and Interoperability______________________ 269
1. Introduction __________________________________________________________ 269
1.1 Business Drivers ___________________________________________________________ 270
1.2 Goals and Principles ________________________________________________________ 272
1.3 Essential Concepts _________________________________________________________ 273
2. Data Integration Activities _______________________________________________ 286
2.1 Plan and Analyze __________________________________________________________ 286
2.2 Design Data Integration Solutions ____________________________________________ 289
2.3 Develop Data Integration Solutions ___________________________________________ 291
2.4 Implement and Monitor _____________________________________________________ 293
3. Tools ________________________________________________________________ 294
3.1 Data Transformation Engine/ETL Tool ________________________________________ 294
3.2 Data Virtualization Server ___________________________________________________ 294
3.3 Enterprise Service Bus ______________________________________________________ 294
3.4 Business Rules Engine ______________________________________________________ 295
3.5 Data and Process Modeling Tools _____________________________________________ 295
3.6 Data Profiling Tool _________________________________________________________ 295
3.7 Metadata Repository _______________________________________________________ 296
4. Techniques ___________________________________________________________ 296
CONTENTS • 5
5. Implementation Guidelines _______________________________________________ 296
5.1 Readiness Assessment / Risk Assessment ______________________________________ 296
5.2 Organization and Cultural Change ____________________________________________ 297
6. DII Governance _________________________________________________________ 297
6.1 Data Sharing Agreements ___________________________________________________ 298
6.2 DII and Data Lineage _______________________________________________________ 298
6.3 Data Integration Metrics ____________________________________________________ 299
7. Works Cited / Recommended _____________________________________________ 299
Chapter 9: Document and Content Management_______________________ 303
1. Introduction ___________________________________________________________ 303
1.1 Business Drivers ___________________________________________________________ 305
1.2 Goals and Principles ________________________________________________________ 305
1.3 Essential Concepts _________________________________________________________ 307
2. Activities ______________________________________________________________ 323
2.1 Plan for Lifecycle Management _______________________________________________ 323
2.2 Manage the Lifecycle _______________________________________________________ 326
2.3 Publish and Deliver Content _________________________________________________ 329
3. Tools _________________________________________________________________ 330
3.1 Enterprise Content Management Systems ______________________________________ 330
3.2 Collaboration Tools ________________________________________________________ 333
3.3 Controlled Vocabulary and Metadata Tools _____________________________________ 333
3.4 Standard Markup and Exchange Formats ______________________________________ 333
3.5 E-discovery Technology _____________________________________________________ 336
4. Techniques ____________________________________________________________ 336
4.1 Litigation Response Playbook ________________________________________________ 336
4.2 Litigation Response Data Map ________________________________________________ 337
5. Implementation Guidelines _______________________________________________ 337
5.1 Readiness Assessment / Risk Assessment ______________________________________ 338
5.2 Organization and Cultural Change ____________________________________________ 339
6. Documents and Content Governance _______________________________________ 340
6.1 Information Governance Frameworks _________________________________________ 340
6.2 Proliferation of Information _________________________________________________ 342
6.3 Govern for Quality Content __________________________________________________ 342
6.4 Metrics ___________________________________________________________________ 343
7. Works Cited / Recommended _____________________________________________ 344
Chapter 10: Reference and Master Data _____________________________ 347
1. Introduction ___________________________________________________________ 347
1.1 Business Drivers ___________________________________________________________ 349
1.2 Goals and Principles ________________________________________________________ 349
1.3 Essential Concepts _________________________________________________________ 350
2. Activities ______________________________________________________________ 370
2.1 MDM Activities ____________________________________________________________ 371
2.2 Reference Data Activities ____________________________________________________ 373
3. Tools and Techniques ___________________________________________________ 375
4. Implementation Guidelines _______________________________________________ 375
4.1 Adhere to Master Data Architecture ___________________________________________ 376
4.2 Monitor Data Movement ____________________________________________________ 376
6 • DMBOK2
4.3 Manage Reference Data Change ______________________________________________ 376
4.4 Data Sharing Agreements ___________________________________________________ 377
5. Organization and Cultural Change ________________________________________ 378
6. Reference and Master Data Governance ____________________________________ 378
6.1 Metrics ___________________________________________________________________ 379
7. Works Cited / Recommended ____________________________________________ 379
Chapter 11: Data Warehousing and Business Intelligence_______________ 381
1. Introduction __________________________________________________________ 381
1.1 Business Drivers ___________________________________________________________ 383
1.2 Goals and Principles ________________________________________________________ 383
1.3 Essential Concepts _________________________________________________________ 384
2. Activities _____________________________________________________________ 394
2.1 Understand Requirements __________________________________________________ 394
2.2 Define and Maintain the DW/BI Architecture ___________________________________ 395
2.3 Develop the Data Warehouse and Data Marts ___________________________________ 396
2.4 Populate the Data Warehouse ________________________________________________ 397
2.5 Implement the Business Intelligence Portfolio __________________________________ 398
2.6 Maintain Data Products _____________________________________________________ 399
3. Tools ________________________________________________________________ 402
3.1 Metadata Repository _______________________________________________________ 402
3.2 Data Integration Tools ______________________________________________________ 403
3.3 Business Intelligence Tools Types ____________________________________________ 403
4. Techniques ___________________________________________________________ 407
4.1 Prototypes to Drive Requirements ____________________________________________ 407
4.2 Self-Service BI _____________________________________________________________ 408
4.3 Audit Data that can be Queried _______________________________________________ 408
5. Implementation Guidelines ______________________________________________ 408
5.1 Readiness Assessment / Risk Assessment ______________________________________ 408
5.2 Release Roadmap __________________________________________________________ 409
5.3 Configuration Management __________________________________________________ 409
5.4 Organization and Cultural Change ____________________________________________ 410
6. DW/BI Governance _____________________________________________________ 411
6.1 Enabling Business Acceptance _______________________________________________ 411
6.2 Customer / User Satisfaction _________________________________________________ 412
6.3 Service Level Agreements ___________________________________________________ 412
6.4 Reporting Strategy _________________________________________________________ 412
6.5 Metrics ___________________________________________________________________ 413
7. Works Cited / Recommended ____________________________________________ 414
Chapter 12: Metadata Management ________________________________ 417
1. Introduction __________________________________________________________ 417
1.1 Business Drivers ___________________________________________________________ 420
1.2 Goals and Principles ________________________________________________________ 420
1.3 Essential Concepts _________________________________________________________ 421
2. Activities _____________________________________________________________ 434
2.1 Define Metadata Strategy____________________________________________________ 434
2.2 Understand Metadata Requirements __________________________________________ 435
2.3 Define Metadata Architecture ________________________________________________ 436
CONTENTS • 7
2.4 Create and Maintain Metadata________________________________________________ 438
2.5 Query, Report, and Analyze Metadata __________________________________________ 440
3. Tools _________________________________________________________________ 440
3.1 Metadata Repository Management Tools _______________________________________ 440
4. Techniques ____________________________________________________________ 441
4.1 Data Lineage and Impact Analysis_____________________________________________ 441
4.2 Metadata for Big Data Ingest _________________________________________________ 443
5. Implementation Guidelines _______________________________________________ 444
5.1 Readiness Assessment / Risk Assessment ______________________________________ 444
5.2 Organizational and Cultural Change ___________________________________________ 445
6. Metadata Governance ___________________________________________________ 445
6.1 Process Controls ___________________________________________________________ 445
6.2 Documentation of Metadata Solutions _________________________________________ 446
6.3 Metadata Standards and Guidelines ___________________________________________ 446
6.4 Metrics ___________________________________________________________________ 447
7. Works Cited / Recommended _____________________________________________ 448
Chapter 13: Data Quality _________________________________________ 449
1. Introduction ___________________________________________________________ 449
1.1 Business Drivers ___________________________________________________________ 452
1.2 Goals and Principles ________________________________________________________ 452
1.3 Essential Concepts _________________________________________________________ 453
2. Activities ______________________________________________________________ 473
2.1 Define High Quality Data ____________________________________________________ 473
2.2 Define a Data Quality Strategy ________________________________________________ 474
2.3 Identify Critical Data and Business Rules _______________________________________ 474
2.4 Perform an Initial Data Quality Assessment _____________________________________ 475
2.5 Identify and Prioritize Potential Improvements _________________________________ 476
2.6 Define Goals for Data Quality Improvement ____________________________________ 477
2.7 Develop and Deploy Data Quality Operations ___________________________________ 477
3. Tools _________________________________________________________________ 484
3.1 Data Profiling Tools ________________________________________________________ 485
3.2 Data Querying Tools ________________________________________________________ 485
3.3 Modeling and ETL Tools _____________________________________________________ 485
3.4 Data Quality Rule Templates _________________________________________________ 485
3.5 Metadata Repositories ______________________________________________________ 485
4. Techniques ____________________________________________________________ 486
4.1 Preventive Actions _________________________________________________________ 486
4.2 Corrective Actions _________________________________________________________ 486
4.3 Quality Check and Audit Code Modules ________________________________________ 487
4.4 Effective Data Quality Metrics ________________________________________________ 487
4.5 Statistical Process Control ___________________________________________________ 488
4.6 Root Cause Analysis ________________________________________________________ 490
5. Implementation Guidelines _______________________________________________ 490
5.1 Readiness Assessment / Risk Assessment ______________________________________ 491
5.2 Organization and Cultural Change ____________________________________________ 492
6. Data Quality and Data Governance _________________________________________ 493
6.1 Data Quality Policy _________________________________________________________ 493
6.2 Metrics ___________________________________________________________________ 494
8 • DMBOK2
7. Works Cited / Recommended ____________________________________________ 494
Chapter 14: Big Data and Data Science ______________________________ 497
1. Introduction __________________________________________________________ 497
1.1 Business Drivers ___________________________________________________________ 498
1.2 Principles ________________________________________________________________ 500
1.3 Essential Concepts _________________________________________________________ 500
2. Activities _____________________________________________________________ 511
2.1 Define Big Data Strategy and Business Needs ___________________________________ 511
2.2 Choose Data Sources _______________________________________________________ 512
2.3 Acquire and Ingest Data Sources______________________________________________ 513
2.4 Develop Data Hypotheses and Methods ________________________________________ 514
2.5 Integrate / Align Data for Analysis ____________________________________________ 514
2.6 Explore Data Using Models __________________________________________________ 514
2.7 Deploy and Monitor ________________________________________________________ 516
3. Tools ________________________________________________________________ 517
3.1 MPP Shared-nothing Technologies and Architecture _____________________________ 518
3.2 Distributed File-based Databases _____________________________________________ 519
3.3 In-database Algorithms _____________________________________________________ 520
3.4 Big Data Cloud Solutions ____________________________________________________ 520
3.5 Statistical Computing and Graphical Languages _________________________________ 520
3.6 Data Visualization Tools ____________________________________________________ 520
4. Techniques ___________________________________________________________ 521
4.1 Analytic Modeling __________________________________________________________ 521
4.2 Big Data Modeling _________________________________________________________ 522
5. Implementation Guidelines ______________________________________________ 523
5.1 Strategy Alignment _________________________________________________________ 523
5.2 Readiness Assessment / Risk Assessment ______________________________________ 523
5.3 Organization and Cultural Change ____________________________________________ 524
6. Big Data and Data Science Governance _____________________________________ 525
6.1 Visualization Channels Management __________________________________________ 525
6.2 Data Science and Visualization Standards ______________________________________ 525
6.3 Data Security ______________________________________________________________ 526
6.4 Metadata _________________________________________________________________ 526
6.5 Data Quality ______________________________________________________________ 527
6.6 Metrics ___________________________________________________________________ 527
7. Works Cited / Recommended ____________________________________________ 528
Chapter 15: Data Management Maturity Assessment __________________ 531
1. Introduction __________________________________________________________ 531
1.1 Business Drivers ___________________________________________________________ 532
1.2 Goals and Principles ________________________________________________________ 534
1.3 Essential Concepts _________________________________________________________ 534
2. Activities _____________________________________________________________ 539
2.1 Plan Assessment Activities __________________________________________________ 540
2.2 Perform Maturity Assessment ________________________________________________ 542
2.3 Interpret Results __________________________________________________________ 543
2.4 Create a Targeted Program for Improvements __________________________________ 544
2.5 Re-assess Maturity _________________________________________________________ 545
CONTENTS • 9
3. Tools _________________________________________________________________ 545
4. Techniques ____________________________________________________________ 546
4.1 Selecting a DMM Framework _________________________________________________ 546
4.2 DAMA-DMBOK Framework Use ______________________________________________ 546
5. Guidelines for a DMMA __________________________________________________ 547
5.1 Readiness Assessment / Risk Assessment ______________________________________ 547
5.2 Organizational and Cultural Change ___________________________________________ 548
6. Maturity Management Governance ________________________________________ 548
6.1 DMMA Process Oversight ____________________________________________________ 548
6.2 Metrics ___________________________________________________________________ 548
7. Works Cited / Recommended _____________________________________________ 549
Chapter 16: Data Management Organization and Role Expectations_______ 551
1. Introduction ___________________________________________________________ 551
2. Understand Existing Organization and Cultural Norms ________________________ 551
3. Data Management Organizational Constructs ________________________________ 553
3.1 Decentralized Operating Model _______________________________________________ 553
3.2 Network Operating Model ___________________________________________________ 554
3.3 Centralized Operating Model _________________________________________________ 555
3.4 Hybrid Operating Model ____________________________________________________ 556
3.5 Federated Operating Model __________________________________________________ 557
3.6 Identifying the Best Model for an Organization __________________________________ 557
3.7 DMO Alternatives and Design Considerations ___________________________________ 558
4. Critical Success Factors __________________________________________________ 559
4.1 Executive Sponsorship ______________________________________________________ 559
4.2 Clear Vision _______________________________________________________________ 559
4.3 Proactive Change Management _______________________________________________ 559
4.4 Leadership Alignment ______________________________________________________ 560
4.5 Communication ____________________________________________________________ 560
4.6 Stakeholder Engagement ____________________________________________________ 560
4.7 Orientation and Training ____________________________________________________ 560
4.8 Adoption Measurement _____________________________________________________ 561
4.9 Adherence to Guiding Principles ______________________________________________ 561
4.10 Evolution Not Revolution __________________________________________________ 561
5. Build the Data Management Organization ___________________________________ 562
5.1 Identify Current Data Management Participants _________________________________ 562
5.2 Identify Committee Participants ______________________________________________ 562
5.3 Identify and Analyze Stakeholders ____________________________________________ 563
5.4 Involve the Stakeholders ____________________________________________________ 563
6. Interactions Between the DMO and Other Data-oriented Bodies ________________ 564
6.1 The Chief Data Officer_______________________________________________________ 564
6.2 Data Governance ___________________________________________________________ 565
6.3 Data Quality _______________________________________________________________ 566
6.4 Enterprise Architecture _____________________________________________________ 566
6.5 Managing a Global Organization ______________________________________________ 567
7. Data Management Roles _________________________________________________ 568
7.1 Organizational Roles _______________________________________________________ 568
7.2 Individual Roles ___________________________________________________________ 568
8. Works Cited / Recommended _____________________________________________ 571
10 • DMBOK2
Chapter 17: Data Management and Organizational Change Management __ 573
1. Introduction __________________________________________________________ 573
2. Laws of Change ________________________________________________________ 574
3. Not Managing a Change: Managing a Transition _____________________________ 575
4. Kotter’s Eight Errors of Change Management _______________________________ 577
4.1 Error #1: Allowing Too Much Complacency ____________________________________ 577
4.2 Error #2: Failing to Create a Sufficiently Powerful Guiding Coalition ________________ 578
4.3 Error #3: Underestimating the Power of Vision _________________________________ 578
4.4 Error #4: Under Communicating the Vision by a Factor of 10, 100, or 1000 __________ 579
4.5 Error #5: Permitting Obstacles to Block the Vision _______________________________ 580
4.6 Error #6: Failing to Create Short-Term Wins ___________________________________ 580
4.7 Error #7: Declaring Victory Too Soon _________________________________________ 581
4.8 Error # 8: Neglecting to Anchor Changes Firmly in the Corporate Culture____________ 581
5. Kotter’s Eight Stage Process for Major Change ______________________________ 582
5.1 Establishing a Sense of Urgency ______________________________________________ 583
5.2 The Guiding Coalition _______________________________________________________ 586
5.3 Developing a Vision and Strategy _____________________________________________ 590
5.4 Communicating the Change Vision ____________________________________________ 594
6. The Formula for Change _________________________________________________ 598
7. Diffusion of Innovations and Sustaining Change _____________________________ 599
7.1 The Challenges to be Overcome as Innovations Spread ___________________________ 601
7.2 Key Elements in the Diffusion of Innovation ____________________________________ 601
7.3 The Five Stages of Adoption _________________________________________________ 601
7.4 Factors Affecting Acceptance or Rejection of an Innovation or Change ______________ 602
8. Sustaining Change _____________________________________________________ 603
8.1 Sense of Urgency / Dissatisfaction ____________________________________________ 604
8.2 Framing the Vision _________________________________________________________ 604
8.3 The Guiding Coalition _______________________________________________________ 605
8.4 Relative Advantage and Observability _________________________________________ 605
9. Communicating Data Management Value __________________________________ 605
9.1 Communications Principles __________________________________________________ 605
9.2 Audience Evaluation and Preparation _________________________________________ 606
9.3 The Human Element ________________________________________________________ 607
9.4 Communication Plan _______________________________________________________ 608
9.5 Keep Communicating _______________________________________________________ 609
10. Works Cited / Recommended ___________________________________________ 609
Acknowledgements _____________________________________________ 611
Index_________________________________________________________ 615