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Modern Data Strategy
von: Mike Fleckenstein, Lorraine Fellows
Springer-Verlag, 2018
ISBN: 9783319689937 , 269 Seiten
Format: PDF, Online Lesen
Kopierschutz: Wasserzeichen
Preis: 96,29 EUR
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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