Читать книгу Data Management: a gentle introduction - Bas van Gils - Страница 9
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3 DATA MANAGEMENT: WHY BOTHER?
3.1 A definition of data management
4.1 The center of the universe
4.2 DM and business process management
4.6 DM and enterprise architecture management
4.7 Philosophical considerations
6.2 Data codifies what we know about the world
6.5 Connecting the business and IT perspective
7 DATA MANAGEMENT: A DEFINITION
7.2 Managing the lifecycle of data
8.2 Five fundamentally different types of data
8.5 Business intelligence data
9.2 Data governance and data management
9.3 Data governance activities in DMBOK
9.4 A modern approach to data governance
9.5 Position of data governance
10.2 Metadata is the foundation
11.3.2 Entity relationship modeling
11.3.3 Architecture modeling with ArchiMate
11.4 Relationship to other DM capabilities
12.3 Relationship to other (data management) capabilities
13.1 Introduction to data integration
13.2 Common integration patterns
13.2.2 Accessing data through services
13.2.4 Streaming data integration
13.3 Integration from an architecture perspective
13.3.1 Dealing with the number of potential connections
13.3.2 Dealing with different names and structures
13.3.3 Dealing with different patterns
14.2 Using reference data to harmonize the meaning of data
14.3 Historic versions of reference data sets
14.4 Reference data and governance
15.1 Multiple versions of the truth
15.3 Relationship to other data management capabilities
16.6 Relationship to other capabilities
17.1 Risks and risk mitigating measures
17.4 Training and certification
17.5 Relationship to other capabilities
18 BUSINESS INTELLIGENCE & ANALYTICS
18.1 Defining business intelligence and analytics
18.5 Relationship to other capabilities
19.3 Technical capabilities and architecture
19.4 Relationship to other capabilities
20.2 Observations about technology
20.3 Technology and the functional areas of DMBOK
20.3.1 Data governance and stewardship
20.3.6 Reference and master data
21 DATA (HANDLING) ETHICS & COMPLIANCE
21.2.1 Ethical principles behind data protection
21.2.3 Using ethical principles in the data lifecycle
21.3 The relationship between ethics and governance
23 BUILDING THE BUSINESS CASE FOR DATA MANAGEMENT
23.1 The need for a business case
23.2 Qualitative and quantitative business case
23.3 Incremental approach to building a business case
24 KICK-STARTING DATA QUALITY MANAGEMENT
24.2 A motivation for starting small
24.3 Setting up your first experiments with data quality management
24.4 Scaling up after successful experimentation
25 FINDING DATA OWNERS AND DATA STEWARDS
25.2 Ownership/stewardship models
25.3 Finding owners and stewards
26.1 People first, and the need for training
26.3 How to design a training program
27 SETTING UP A DATA MANAGEMENT POLICY
27.2 Typical structure for a data management policy
27.3 Setting up a data management policy
28 BUSINESS CONCEPTS AND THE CONCEPTUAL DATA MODEL
28.2 Definitions and conceptual data models
29 SETTING UP A METADATA REPOSITORY
29.1 The importance of metadata
29.2 Metadata repository architectures
29.3 Implementation strategies
29.3.1 Top-down metadata strategy
29.3.2 Bottom-up metadata strategy
29.3.3 Matching the strategy to the situation
30 LEVERAGING ENTERPRISE ARCHITECTURE
30.1 EA as a source of information
30.2 EA models and visualizations
30.3 Building effective solutions
32 A PRAGMATIC APPROACH TO DATA SECURITY
32.1 Motivation for a security framework
32.3 Security levels in business terms
32.4 The link to security measures and controls
33.3 Skills in the SFIA framework
33.4.3 Data owner, data steward
33.4.6 Business analyst, process analyst, and system analyst
33.5 Reflection and recommendation
34.1 Observations about big data adoption
34.2 Building a culture of innovation
34.3 Linking to data management defense
35 BUILDING A DATA MANAGEMENT ROADMAP
35.1 To roadmap or not to roadmap
35.2 The steps towards an effective roadmap
36 SYNTHESIS OF THE RECOMMENDATIONS
