Data Management

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.

Contenu
1Introduction2Data as an asset3Data management: why bother?4Positioning data managementPart ITheory5Introduction6Terminology7Data management: a definition8Types of data9Data governance10Metadata11Modeling12Architecture13Integration14Reference data15Master data16Quality17Risk and security18Business intelligence & analytics19Big data20Technology21Data (handling) ethics & complianceIIPractice22Introduction23Building the business case for data management24Kick-starting data quality management25Finding data owners and data stewards26The role of training27Setting up a data management policy28Business concepts and the conceptual data model29Setting up a metadata repository30Leveraging enterprise architecture31Integration architecture32A pragmatic approach to data security33Roles in data management34Working with big data35Building a data management roadmapIIIClosing remarks36Synthesis of the recommendations37ConclusionBibliography