Оглавление
Bas van Gils. Data Management: a gentle introduction
Other publications by Van Haren Publishing
Colophon
■ 1.1 GOALS FOR THIS BOOK
■ 1.2 INTENDED AUDIENCE
■ 1.3 APPROACH
■ 2.1 DATA
■ 2.2 ASSET
■ 2.3 DATA AND PROCESS
■ 2.4 VISUAL SUMMARY
■ 3.1 A DEFINITION OF DATA MANAGEMENT
■ 3.2 VALUE OF DM
■ 3.3 KEY CHALLENGES FOR DM
■ 3.4 VISUAL SUMMARY
■ 4.1 THE CENTER OF THE UNIVERSE
■ 4.2 DM AND BUSINESS PROCESS MANAGEMENT
■ 4.3 DM AND IT MANAGEMENT
■ 4.4 INFORMATION/DATA ANALYSIS
■ 4.5 DATABASE MANAGEMENT
■ 4.6 DM AND ENTERPRISE ARCHITECTURE MANAGEMENT
■ 4.7 PHILOSOPHICAL CONSIDERATIONS
■ 4.8 VISUAL SUMMARY
■ 6.1 INTRODUCTION
■ 6.2 DATA CODIFIES WHAT WE KNOW ABOUT THE WORLD
■ 6.3 STORING DATA IN SYSTEMS
■ 6.4 DATA IN PROCESSES
■ 6.5 CONNECTING THE BUSINESS AND IT PERSPECTIVE
■ 6.6 OUTLOOK
■ 6.7 VISUAL SUMMARY
■ 7.1 INTRODUCTION
■ 7.2 MANAGING THE LIFECYCLE OF DATA
■ 7.3 DECONSTRUCTING DM
■ 7.4 VISUAL SUMMARY
■ 8.1 CLASSIFYING DATA
■ 8.2 FIVE FUNDAMENTALLY DIFFERENT TYPES OF DATA
■ 8.3 TRANSACTION DATA
■ 8.4 MASTER DATA
■ 8.5 BUSINESS INTELLIGENCE DATA
■ 8.6 REFERENCE DATA
■ 8.7 METADATA
■ 8.8 VISUAL SUMMARY
■ 9.1 INTRODUCTION
■ 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
■ 9.6 VISUAL SUMMARY
■ 10.1 TYPES OF METADATA
10.1.1 Business metadata
10.1.2 Technical metadata
10.1.3 Operational metadata
■ 10.2 METADATA IS THE FOUNDATION
■ 10.3 METADATA REPOSITORIES
■ 10.4 VISUAL SUMMARY
■ 11.1 SCOPE
■ 11.2 ABSTRACTION LEVELS
■ 11.3 MODELING LANGUAGES
11.3.1 Fact-based modeling
11.3.2 Entity relationship modeling
11.3.3 Architecture modeling with ArchiMate
■ 11.4 RELATIONSHIP TO OTHER DM CAPABILITIES
■ 11.5 VISUAL SUMMARY
■ 12.1 ARCHITECTURE
■ 12.2 DATA ARCHITECTURE
■ 12.3 RELATIONSHIP TO OTHER (DATA MANAGEMENT) CAPABILITIES
■ 12.4 VISUAL SUMMARY
■ 13.1 INTRODUCTION TO DATA INTEGRATION
■ 13.2 COMMON INTEGRATION PATTERNS
13.2.1 Batch integration
13.2.2 Accessing data through services
13.2.3 Change data capture
13.2.4 Streaming data integration
13.2.5 Data virtualization
■ 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
■ 13.4 VISUAL SUMMARY
■ 14.1 DEFINITION
■ 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
■ 14.5 VISUAL SUMMARY
■ 15.1 MULTIPLE VERSIONS OF THE TRUTH
■ 15.2 BASIC MDM CONCEPTS
■ 15.3 RELATIONSHIP TO OTHER DATA MANAGEMENT CAPABILITIES
■ 15.4 VISUAL SUMMARY
■ 16.1 INTRODUCTION
■ 16.2 THE NOTION OF QUALITY
■ 16.3 DATA QUALITY
■ 16.4 DATA QUALITY MANAGEMENT
■ 16.5 CRITICAL DATA ELEMENTS
■ 16.6 RELATIONSHIP TO OTHER CAPABILITIES
■ 16.7 VISUAL SUMMARY
■ 17.1 RISKS AND RISK MITIGATING MEASURES
■ 17.2 ISO STANDARDS
■ 17.3 DATA SECURITY MANAGEMENT
■ 17.4 TRAINING AND CERTIFICATION
■ 17.5 RELATIONSHIP TO OTHER CAPABILITIES
■ 17.6 VISUAL SUMMARY
■ 18.1 DEFINING BUSINESS INTELLIGENCE AND ANALYTICS
■ 18.2 COMMON SYSTEM TYPES
■ 18.3 STRUCTURING DATA
■ 18.4 SELF-SERVICE BI
■ 18.5 RELATIONSHIP TO OTHER CAPABILITIES
■ 18.6 VISUAL SUMMARY
■ 19.1 DEFINITION OF BIG DATA
■ 19.2 DEALING WITH BIG DATA
■ 19.3 TECHNICAL CAPABILITIES AND ARCHITECTURE
■ 19.4 RELATIONSHIP TO OTHER CAPABILITIES
■ 19.5 VISUAL SUMMARY
■ 20.1 PEOPLE ARE KEY
■ 20.2 OBSERVATIONS ABOUT TECHNOLOGY
■ 20.3 TECHNOLOGY AND THE FUNCTIONAL AREAS OF DMBOK
20.3.1 Data governance and stewardship
20.3.2 Metadata
20.3.3 Modeling
20.3.4 Architecture
20.3.5 Integration
20.3.6 Reference and master data
20.3.7 Quality
20.3.8 Security
20.3.9 Business intelligence
20.3.10 Big data
■ 20.4 TECHNOLOGY ADOPTION
■ 20.5 VISUAL SUMMARY
■ 21.1 ETHICS IN DATA
■ 21.2 ETHICAL HANDLING OF DATA
21.2.1 Ethical principles behind data protection
21.2.2 The data lifecycle
21.2.3 Using ethical principles in the data lifecycle
■ 21.3 THE RELATIONSHIP BETWEEN ETHICS AND GOVERNANCE
■ 21.4 VISUAL SUMMARY
■ 23.1 THE NEED FOR A BUSINESS CASE
■ 23.2 QUALITATIVE AND QUANTITATIVE BUSINESS CASE
■ 23.3 INCREMENTAL APPROACH TO BUILDING A BUSINESS CASE
■ 24.1 TOP-DOWN APPROACH
■ 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
■ 25.1 TOP-DOWN AND BOTTOM-UP
■ 25.2 OWNERSHIP/STEWARDSHIP MODELS
■ 25.3 FINDING OWNERS AND STEWARDS
■ 26.1 PEOPLE FIRST, AND THE NEED FOR TRAINING
■ 26.2 TYPES OF TRAINING
■ 26.3 HOW TO DESIGN A TRAINING PROGRAM
■ 27.1 DATA MANAGEMENT POLICY
■ 27.2 TYPICAL STRUCTURE FOR A DATA MANAGEMENT POLICY
■ 27.3 SETTING UP A DATA MANAGEMENT POLICY
27.3.1 Top-down
27.3.2 Bottom-up
■ 27.4 RECOMMENDATIONS
■ 28.1 FREEZING LANGUAGE
■ 28.2 DEFINITIONS AND CONCEPTUAL DATA MODELS
■ 28.3 DEFINITIONS IN A CONTEXT
■ 28.4 RECOMMENDATIONS
■ 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
■ 29.4 RECOMMENDATIONS
■ 30.1 EA AS A SOURCE OF INFORMATION
■ 30.2 EA MODELS AND VISUALIZATIONS
■ 30.3 BUILDING EFFECTIVE SOLUTIONS
■ 30.4 RECOMMENDATIONS
■ 31.1 DATA IS EVERYWHERE
■ 31.2 START SIMPLE
■ 31.3 KEEP IT SIMPLE
■ 31.4 RECOMMENDATIONS
■ 32.1 MOTIVATION FOR A SECURITY FRAMEWORK
■ 32.2 SECURITY USE CASES
■ 32.3 SECURITY LEVELS IN BUSINESS TERMS
■ 32.4 THE LINK TO SECURITY MEASURES AND CONTROLS
■ 32.5 TYING IT TOGETHER
■ 33.1 CHANGE AND RUN
■ 33.2 ROLES IN THE DMBOK
■ 33.3 SKILLS IN THE SFIA FRAMEWORK
■ 33.4 DEFINITION OF ROLES
33.4.1 Architect
33.4.2 Business management
33.4.3 Data owner, data steward
33.4.4 Project management
33.4.5 Chief data officer
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
■ 34.4 THE FUTURE OF BIG DATA
■ 35.1 TO ROADMAP OR NOT TO ROADMAP
■ 35.2 THE STEPS TOWARDS AN EFFECTIVE ROADMAP
■ 35.3 TECHNIQUES
35.3.1 Vision phase
35.3.2 Analysis phase
35.3.3 Portfolio phase
35.3.4 Execution phase
■ 35.4 RECOMMENDATIONS
■ 36.1 DATA MANAGEMENT
■ 36.2 ANTIFRAGILITY AND COMPLEXITY
■ 36.3 EXPECTED BENEFITS
■ 37.1 REVIEW
■ 37.2 OUTLOOK
■ 37.3 CALL TO ACTION
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