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· 제목 : The Chief Data Officer Management Handbook: Set Up and Run an Organization's Data Supply Chain (Paperback)
· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9781484261149
· 쪽수 : 435쪽
· 출판일 : 2020-09-19
· 분류 : 외국도서 > 과학/수학/생태 > 수학 > 확률과 통계 > 일반
· ISBN : 9781484261149
· 쪽수 : 435쪽
· 출판일 : 2020-09-19
목차
1 Understand your organisation 22
1.1 Five implicit Data Governance models 24
1.2 Behavioural patterns in data matters 37
2 Aspects of effective Data Management 50
2.1 Maturity Assessment 51
2.2 The two main gaps 52
2.3 Subsidiarity 53
2.4 Business-orientation 54
2.5 Commercial Orientation 59
2.6 Collaboration 60
2.7 Motivation 66
2.8 The Data Supply Chain 66
2.9 Cross-functionality 83
2.10 Change Management 90
2.11 Data Literacy 903 Data Vision, Mission and Strategy 95
3.1 Data strategy ? seriously? 96
3.2 Vision 103
3.3 Mission 108
3.4 Strategy 113
3.5 Your individual measure of success 115
4 Masterdata Management 118
4.1 Isn’t static data old-fashioned? 119
4.2 What does Masterdata cover? 121
4.3 Managing Masterdata 137
4.4 MDM and Masterdata software 141
5 Data Governance 152
5.1 Shape a set of Data Principles 153
5.2 Develop data policies 156
5.3 The target state of managed data 163
5.4 Scope of Data Governance 164
5.5 Decision-making and collaboration 166
6 The Data Language 175
6.1 Don’t we all speak English? 176
6.2 The Data Glossary 176
6.3 Data Rules and Standards 187
6.4 The Data Model 192
6.5 Choosing a software solution 203
7 Data processes 206
7.1 Why prescribing processes? 207
7.2 Process Development Aspects 213
7.3 General considerations 214
7.4 Concrete Process Groups 219
7.5 Manage Data in business processes 231
8 Roles & Responsibilities 235
8.1 Introduction 236
8.2 Data Owners and Data Champions 237
8.3 Data Creators and Consumers 240
8.4 Other business roles 242
8.5 Centralised roles 244
9 Data Quality 255
9.1 Why is Data Quality important? 256
9.2 Dangerous Data Quality standpoints 258
9.3 How to deal with Data Quality? 269
9.4 Management of Business Metrics 279
10 Shaping Data Office Teams 288
10.1 The effective creation of data teams 289
10.2 Data Architecture and Glossary 290
10.3 Analytics 297
10.4 Document Management 306
10.5 Data Quality 311
10.6 Organising Masterdata Management 313
10.7 Data Project Office 317
10.8 Data Service function 322
10.9 Attracting and retaining experts 326
10.10 Six Sigma 336
11 Typical Challenges of a CDO 349
11.1 Why is it so hard to be a CDO? 350
11.2 Struggle for Supremacy 356
11.3 Lack of Awareness 359
11.4 Business Silos 362
11.5 Lack of Ownership 365
11.6 Opt-Out Attitude 367
11.7 Disengagement 370
11.8 Scepticism 372
11.9 Business Arrogance 37311.10 Summary: Prerequisites for success 375
12 How (not) to behave as a CDO 383
12.1 Don’t rely on formal authority 384
12.2 Start small, and pick your battles 384
12.3 Be humble 385
12.4 Present yourself as a facilitator 386
12.5 Avoid suboptimal language 388
12.6 Go out and talk to people 389
13 Stakeholders 391
13.1 Determine your Executive allies 392
13.2 Have the right stories 393
13.3 Manage stakeholders at all levels 406
13.4 Know the motives of your allies 408
13.5 Shape your Data Network 409
13.6 Orchestrate your Data Network 418
13.7 Plan to consider different audiences 419
13.8 Frequently stated concerns 421
14 Psychology of Governance 441
14.1 Don't claim covered ground 442
14.2 Design an acceptable starting setup 443
14.3 Base your authority on accepted authorities 443
14.4 Balancing two extremes 445
14.5 Shape your Data brand 450
14.6 Elevator pitch 451
15 Data Business Cases 457
15.1 Business Cases for data - Why? 458
15.2 Business Cases in a perfect world 460
15.3 General Challenges 466
15.4 Data-specific Challenges 473
15.5 Eight Secrets of data business cases 479
15.6 Use Cases for Data as an Asset 495
16 Data Ethics and Compliance 50216.1 Ethical behaviour and data? 503
16.2 GDPR ? All done? 516
17 The Outside World 521
17.1 Why look beyond my organisation? 522
17.2 Sharing Data across organisations 523
17.3 External Data 527
17.4 The CDM and external data 531
17.5 Data Quality as a Service? 537
17.6 Global Standards 540
17.7 Cloud Strategy for Data 545
17.8 Blockchain 562
18 Handling Data 569
18.1 The Virtual Single Source of Truth” 570
18.2 Single Source of Logic 574
18.3 Configuration vs Standardisation 582
18.4 “Effective Date” Concept 584
18.5 Making Data international 587
18.6 Data Debt Management 592
18.7 Agile and Data 597
18.8 Starting with the Happy Flow? 601
19 Analysing data 613
19.1 Preconditions of meaningful Analytics 614
19.2 General limits of AI 619
19.3 Recommendations around Analytics 636
19.4 Explainable AI (XAI) 655
20 Data in mergers and acquisitions 667
20.1 What is going wrong today? 668
20.2 Integration Planning 669
20.3 The Data Approach 673
20.4 Data Mapping 679
21 Data for Innovation 687
21.1 How can data drive innovation? 688
21.2 Supporting data-driven innovation 697
21.3 Commercialising Data Ideas 709
22 APPENDIX 715
22.1 Table of Figures 715
22.2 List of Theorems 718
22.3 Index 721
23 Bibliography 730
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