Modern Data Strategy (eBook)

Mike Fleckenstein, Lorraine Fellows (Autoren)

eBook Download: PDF

2018 | 1st ed. 2018
XIX, 263 Seiten
Springer International Publishing (Verlag)
978-3-319-68993-7 (ISBN)

Infos

This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits.

Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains-data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues.

This book is intended for business users who work with data, who need to manage one or more aspects of the organization's data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management.

Inhalt

Foreword 5
Acknowledgments 7
Disclaimer 8
Purpose and Introduction 13
Purpose of This Book 13
How to Navigate This Book 14
Introduction 15
Contents 9
Part I: Data Strategy Considerations 20
Chapter 1: Evolution to Modern Data Management 21
Chapter 2: Big Data and Data Management 24
Chapter 3: Valuing Data As an Asset 28
Chapter 4: Physical Asset Management vs. Data Management 32
4.1 Cost 33
4.2 Quality Fit for Use 35
4.3 Stewardship 35
4.4 Architecture 36
4.5 Obsolescence 37
4.6 Additional Considerations 37
Part II: Data Strategy 40
Chapter 5: Leading a Data Strategy 41
5.1 Process, Technology, and Data People 41
5.2 CIO Role 43
5.3 Emerging CDO Role 46
5.4 Alternative Executives to Lead a Data Strategy Effort 49
Chapter 6: Implementing a Data Strategy 51
6.1 Business Strategy As a Driver for Data Strategy 56
6.2 Existing Data Management Infrastructure As the Driver of Data Strategy 60
6.3 Determining the Scope of the Data Strategy Initiative 64
6.4 Skills Needed for a Data Strategy 68
6.5 Change Management 70
Chapter 7: Overview of Data Management Frameworks 71
7.1 DAMA DMBOK 72
7.2 CMMI DMM Model 72
7.3 Additional Frameworks 74
Part III: Data Management Domains 76
Chapter 8: Data Governance 77
8.1 What Is Data Governance? 77
8.1.1 Vision, Goals, and Priorities 79
8.1.2 Data Management Principles 80
8.1.3 Data Policies, Standards, and Guidelines 81
8.1.4 Data Governance and Assurance 82
8.1.5 Authoritative Sources and Other Resources for Staff 83
8.1.6 Communications Infrastructure and Periodic Outreach Campaigns 83
8.2 Who Is Data Governance? 84
8.2.1 Data Governance Framework 85
8.2.2 Data Governance Operations 85
8.2.3 Executive Level 86
8.2.4 Management Level 86
8.2.5 Data Stewards Level 87
8.3 Benefits of Data Governance 88
8.4 Implementing Data Governance 88
8.4.1 A Data Governance Framework 88
8.4.2 Assessments 89
8.4.2.1 Current State Assessment 89
8.4.2.2 Maturity Assessment 89
8.5 Data Governance Tools 90
Chapter 9: Data Architecture 91
9.1 What Is Data Architecture? 91
9.1.1 Business Glossary 91
9.1.2 Data Asset Inventory 92
9.1.3 Data Standards 93
9.1.4 Data Models 94
9.1.5 Data Lifecycle Diagrams 97
9.2 Who Is Data Architecture? 100
9.3 Benefits of Data Architecture 101
9.4 Data Architecture Framework 102
9.5 Implementing Data Architecture 102
9.6 Data Architecture Tools 104
Chapter 10: Master Data Management 106
10.1 What Is Master Data Management? 106
10.2 Who Is Master Data Management? 107
10.3 Benefits of Master Data Management 108
10.4 Master Data Management Framework 108
10.5 Implementing Master Data Management 110
10.6 Master Data Management Tools 111
Chapter 11: Data Quality 113
11.1 What Is Data Quality? 113
11.1.1 Data Quality Dimensions 114
11.1.1.1 Accuracy 114
11.1.1.2 Completeness 114
11.1.1.3 Consistency 114
11.1.1.4 Latency 115
11.1.1.5 Reasonableness 115
11.1.2 Trusting Your Data 117
11.1.3 Data Quality Challenges 119
11.1.3.1 Inadequate Controls at the Point of Origin 119
11.1.3.2 Volume, Variety, Velocity 120
11.1.3.3 Environment Complexity 120
11.1.3.4 Too Much Proliferation and Duplication 120
11.1.3.5 Poor Metadata, Unclear Definitions, and Multiple Interpretations 120
11.2 Who Is Data Quality? 121
11.2.1 Data Quality Controls 123
11.3 Implementing Data Quality 124
11.3.1 Defining Data Quality 124
11.3.2 Deploying Data Quality 124
11.3.3 Monitoring Data Quality 125
11.3.4 Resolving Data Quality Issues 126
11.3.5 Measuring Data Quality 127
11.3.6 Data Classification 127
11.3.7 Data Certification 128
11.3.8 Data Quality—Trends and Challenges 128
11.4 Data Quality Tools 130
Chapter 12: Data Warehousing and Business Intelligence 132
12.1 What Are Data Warehousing and Business Intelligence? 132
12.1.1 Data Warehouse Architectural Components 133
12.1.1.1 Staging Area 133
12.1.1.2 Extract Transform Load 133
12.1.1.3 Operational Data Store 134
12.1.1.4 Data Mart 134
12.1.1.5 Business Intelligence 134
12.2 Who Is Data Warehousing and Business Intelligence? 137
12.3 Implementing Data Warehousing and Business Intelligence 138
12.4 Data Warehousing and Business Intelligence Tools 139
Chapter 13: Data Analytics 143
13.1 What Is Data Analytics? 143
13.2 Who Is Data Analytics? 145
13.3 Implementing Data Analytics 147
13.4 Data Analytics Framework 150
13.5 Data Analytics Tools 152
Chapter 14: Data Privacy 153
14.1 What Is Data Privacy 153
14.2 Who Is Data Privacy 156
14.2.1 Privacy Components 158
14.3 Privacy Operations 162
14.4 Implementing Privacy 165
14.4.1 Collection 165
14.4.2 Creation/Transformation 168
14.4.3 Usage/Processing 169
14.4.4 Disclosure/Dissemination 170
14.4.5 Retention/Storage 171
14.4.6 Disposition/Destruction 171
14.5 Privacy Tools 172
Chapter 15: Data Security 174
15.1 What Is Data Security? 174
15.2 Who Is Data Security 176
15.3 Implementing Data Security 178
15.4 Using the Cybersecurity Framework to Implement Data Security 179
15.4.1 Using the RMF to Implement Data Security 181
15.4.2 Data System Security Control Standards 183
15.4.3 Linkages to Other Processes 184
15.4.4 Piecing Together Data Security Implementation Considerations 185
15.5 Data Security Tools 186
Chapter 16: Metadata 187
16.1 What Are Metadata and Metadata Management? 188
16.1.1 Metadata Management 189
16.1.2 Metadata vs. Data 189
16.2 Who Is Metadata Management? 191
16.3 Benefits of Metadata Management 192
16.4 Metadata Frameworks 194
16.5 Implementing Metadata 195
16.6 Metadata Management Tools 199
Chapter 17: Records Management 202
17.1 What Is Records Management 202
17.2 Who Is Records Management 205
17.3 Benefits of Records Management 206
17.4 Components of Records Management 207
17.4.1 Records Management and Data Management 208
17.4.2 Records Management Frameworks 210
17.4.3 Implementing Records Management Programs 211
17.4.4 Records Management and Other Tools 213
Appendices 215
Appendix A: Frameworks 215
Data Management Frameworks 215
DAMA Data Management Body of Knowledge (DMBOK) 215
CMMI Data Management Maturity Model 216
MITRE DMDF 218
EDMC FIBO and DCAM 219
Enterprise Architecture Frameworks 220
FEAF-II Data Reference Model 220
The Open Group Architecture Framework (TOGAF) 221
The DOD Architecture Framework (DODAF) 222
Additional Frameworks, Models, and Standards Bodies 222
Appendix B: Examples of Industry Drivers 224
Examples of Public Sector Data Strategy Drivers 224
Open Data Policy: Managing Information as an Asset 224
The DATA Act: Government-Wide Financial Data Standards 225
National Strategy for Information Sharing and Safeguarding 225
National Mandate for Data Center Consolidation 225
Electronic Health Records (EHR) and Interoperability 225
Federal CIO Roadmap 226
Federal Data Protection 226
White House Digital Service Playbook 226
President’s Memorandum on Transparency and Open Government 227
Executive Order: Making Open and Machine Readable the New Default for Government Information 227
Executive Order: Improving Public Access to and Dissemination of Government Information and Using the Federal Enterprise Architecture Data Reference Model 227
Additional Examples 228
Examples of Private Sector Data Strategy Drivers 228
Appendix C: Additional References 228
Data Governance References 228
Questions Data Management Helps to Answer 228
Data Management Principle Examples 229
Additional Topics for Data Policies, Standards, or Guidelines 230
Data Governance Charter Examples 231
Executive Data Governance Charter 231
Management Level Data Governance Charter 232
Data Architecture References 235
Exchange Standards 235
Data Quality References 236
Data Warehousing and Business Intelligence References 237
Data Security References 237
Data Security Frameworks 237
Data Security Operations 240
Metadata References 242
Catalog Standards and Metamodels 242
Vocabulary Standards 242
ISO Standards 244
Data Analytics References 244
Records Management References 248
Appendix D: Acronyms and Glossary of Terms 251
Acronym List 251
Glossary of Terms 254
References 263

Produkt-Details

• Erscheint lt. Verlag: 12.2.2018
• Zusatzinfo: XIX, 263 p. 22 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Informatik ► Netzwerke ► Sicherheit / Firewall
• Schlagworte: Data • data analytics • data architecture • Data asset • data governance • Data Management • data privacy • Data Security • Data strategy • Information Management • Information Strategy • Metadata
• ISBN-10: 3-319-68993-2 / 3319689932
• ISBN-13: 978-3-319-68993-7 / 9783319689937