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.
Auflage
Sprache
Verlagsort
Zielgruppe
Produkt-Hinweis
Dateigröße
ISBN-13
978-94-018-0555-1 (9789401805551)
Schweitzer Klassifikation
1 Introduction2 Data as an asset3 Data management: why bother?4 Positioning data managementPart I Theory5 Introduction6 Terminology7 Data management: a definition 8 Types of data9 Data governance10 Metadata11 Modeling12 Architecture13 Integration14 Reference data15 Master data16 Quality17 Risk and security18 Business intelligence & analytics19 Big data20 Technology21 Data (handling) ethics & complianceII Practice22 Introduction23 Building the business case for data management24 Kick-starting data quality management25 Finding data owners and data stewards26 The role of training27 Setting up a data management policy28 Business concepts and the conceptual data model29 Setting up a metadata repository30 Leveraging enterprise architecture31 Integration architecture32 A pragmatic approach to data security33 Roles in data management34 Working with big data35 Building a data management roadmapIII Closing remarks36 Synthesis of the recommendations37 ConclusionBibliography