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