Читать книгу Data Management: a gentle introduction - Bas van Gils - Страница 9

1 INTRODUCTION

1.1 Goals for this book

1.2 Intended audience

1.3 Approach

2 DATA AS AN ASSET

2.1 Data

2.2 Asset

2.3 Data and process

2.4 Visual summary

3 DATA MANAGEMENT: WHY BOTHER?

3.1 A definition of data management

3.2 Value of DM

3.3 Key challenges for DM

3.4 Visual summary

4 POSITIONING DATA MANAGEMENT

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

PART I: THEORY

5 INTRODUCTION

6 TERMINOLOGY

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 DATA MANAGEMENT: A DEFINITION

7.1 Introduction

7.2 Managing the lifecycle of data

7.3 Deconstructing DM

7.4 Visual summary

8 TYPES OF DATA

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 DATA GOVERNANCE

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 METADATA

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 MODELING

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 ARCHITECTURE

12.1 Architecture

12.2 Data architecture

12.3 Relationship to other (data management) capabilities

12.4 Visual summary

13 INTEGRATION

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 REFERENCE DATA

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 MASTER DATA

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 QUALITY

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 RISK AND SECURITY

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 BUSINESS INTELLIGENCE & ANALYTICS

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 BIG DATA

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 TECHNOLOGY

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 DATA (HANDLING) ETHICS & COMPLIANCE

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

PART II: PRACTICE

22 INTRODUCTION

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

24.1 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 FINDING DATA OWNERS AND DATA STEWARDS

25.1 Top-down and bottom-up

25.2 Ownership/stewardship models

25.3 Finding owners and stewards

26 THE ROLE OF TRAINING

26.1 People first, and the need for training

26.2 Types of training

26.3 How to design a training program

27 SETTING UP A DATA MANAGEMENT POLICY

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 BUSINESS CONCEPTS AND THE CONCEPTUAL DATA MODEL

28.1 Freezing language

28.2 Definitions and conceptual data models

28.3 Definitions in a context

28.4 Recommendations

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

29.4 Recommendations

30 LEVERAGING ENTERPRISE ARCHITECTURE

30.1 EA as a source of information

30.2 EA models and visualizations

30.3 Building effective solutions

30.4 Recommendations

31 INTEGRATION ARCHITECTURE

31.1 Data is everywhere

31.2 Start simple

31.3 Keep it simple

31.4 Recommendations

32 A PRAGMATIC APPROACH TO DATA SECURITY

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 ROLES IN DATA MANAGEMENT

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 WORKING WITH BIG DATA

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 BUILDING A DATA MANAGEMENT ROADMAP

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

PART III: CLOSING REMARKS

36 SYNTHESIS OF THE RECOMMENDATIONS

36.1 Data management

36.2 Antifragility and complexity

36.3 Expected benefits

37 CONCLUSION

37.1 Review

37.2 Outlook

37.3 Call to action

BIBLIOGRAPHY

INDEX

ABOUT THE AUTHOR