This book presents a set of models, methods, and techniques that allow the successful implementation of data governance (DG) in an organization and reports real experiences of data governance in different public and private sectors.
To this end, this book is composed of two parts. Part I on "Data Governance Fundamentals" begins with an introduction to the concept of data governance that stresses that DG is not primarily focused on databases, clouds, or other technologies, but that the DG framework must be understood by business users, systems personnel, and the systems themselves alike. Next, chapter 2 addresses crucial topics for DG, such as the evolution of data management in organizations, data strategy and policies, and defensive and offensive approaches to data strategy. Chapter 3 then details the central role that human resources play in DG, analysing the key responsibilities of the different DG-related roles and boards, while chapter 4 discusses the most common barriers to DG in practice. Chapter 5 summarizes the paradigm shifts in DG from control to value creation. Subsequently chapter 6 explores the needs, characteristics and key functionalities of DG tools, before this part ends with a chapter on maturity models for data governance. Part II on "Data Governance Applied" consists of five chapters which review the situation of DG in different sectors and industries. Details about DG in the banking sector, public administration, insurance companies, healthcare and telecommunications each are presented in one chapter.
The book is aimed at academics, researchers and practitioners (especially CIOs, Data Governors, or Data Stewards) involved in DG. It can also serve as a reference for courses on data governance in information systems.
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Zielgruppe
Illustrationen
8
29 farbige Abbildungen, 8 s/w Abbildungen
XXIII, 241 p. 37 illus., 29 illus. in color.
Maße
Höhe: 241 mm
Breite: 160 mm
Dicke: 21 mm
Gewicht
ISBN-13
978-3-031-43772-4 (9783031437724)
DOI
10.1007/978-3-031-43773-1
Schweitzer Klassifikation
Ismael Caballero is Professor with the Information Systems and Technologies Department at the University of Castilla-La Mancha (UCLM, Spain). His research interests are in the areas of Data Quality Management and Data Governance. He has published more than 75 papers in journals and conferences and has also participated in the organization of several scientific events related to data quality management and data governance. He is Head of the "Data Governance and Data Management Program" of UCLM in collaboration with DAMA Spain to prepare students for DAMA's Certified Data Management Professional. He also participated in the development of several ISO standards related to the area, like the ISO 8000-60 series on data quality management processes or in the Spanish National Standards (UNE) Specifications UNE 0077 (Data Governance), UNE 0078 (Data Management), UNE 0079 (Data Quality Management), UNE 0080 (Data Maturity Evaluation) and UNE 0081 (Data Quality Evaluation). He is one of the founders and partners of the Spinoff DQTeam, specialized in data quality and data governance.
Mario Piattini is Professor of Computer Languages and Systems at UCLM, and Director of the Alarcos Research Group, focusing on the quality of information systems. He is the founder of several spinoffs including AQCLab, the only international accredited laboratory for data quality assessment, and DQTeam, a consulting firm specializing in data governance and data quality. Mario is recognized among the World's ten best computer science researchers in 2021, according to the ranking drawn up by Research.com, and appears on the Stanford University list of the world's most widely cited scientists. He has participated in several in the development of several ISO standards, as well as in the development of the Spanish National Standards (UNE) Specifications UNE 0077 (Data Governance), UNE 0078 (Data Management), UNE 0079 (Data Quality Management), UNE 0080 (Data Maturity Evaluation)and UNE 0081 (Data Quality Evaluation).
