The overall objective of this book is to show that data management is an exciting and valuable capability that is worth time and effort.
More specifically it aims to achieve the following goals:
1. To give a “gentle” introduction to the field of DM by explaining and illustrating its core concepts, based on a mix of theory, practical frameworks such as TOGAF, ArchiMate, and DMBOK, as well as results from real-world assignments.
2. To offer guidance on how to build an effective DM capability in an organization.This is illustrated by various use cases, linked to the previously mentioned theoretical exploration as well as the stories of practitioners in the field.
The primary target groups are: busy professionals who “are actively involved with managing data”. The book is also aimed at (Bachelor’s/ Master’s) students with an interest in data management. The book is industry-agnostic and should be applicable in different industries such as government, finance, telecommunications etc.
Typical roles for which this book is intended: data governance office/ council, data owners, data stewards, people involved with data governance (data governance board), enterprise architects, data architects, process managers, business analysts and IT analysts.
The book is divided into three main parts: theory, practice, and closing remarks. Furthermore, the chapters are as short and to the point as possible and also make a clear distinction between the main text and the examples. If the reader is already familiar with the topic of a chapter, he/she can easily skip it and move on to the next.
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2. Data as an asset
3. Data management: why bother?
4. Positioning data management
Part I: Theory
7. Data management: a definition
8. Types of data
9. Data governance
14. Reference data
15. Master data
17. Risk and security
18. Business intelligence & analytics
19. Big data
21. Data (handling) ethics & compliance
Part II: Practice
23. Building the business case for data management
24. Kick-starting data quality management
25. Finding data owners and data stewards
26. The role of training
27. Setting up a data management policy
28. Business concepts and the conceptual data model
29. Setting up a metadata repository
30. Leveraging enterprise architecture
31. Integration architecture
32. A pragmatic approach to data security
33. Roles in data management
34. Working with big data
35. Building a data management roadmap
Part III: Closing remarks
36. Synthesis of the recommendations
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