Corporate Data Quality
Boris Otto • Hubert Österle
Corporate Data Quality
Prerequisite for Successful Business Models
| Boris Otto Fraunhofer Institute for Material Flow and Logistics Dortmund Germany | Hubert Österle CDQ AG St. Gallen Switzerland |
ISBN 978-3-7375-7592-8
ISBN 978-3-7375-7593-5 (eBook)
Published in 2015
Printed and published by epubli GmbH, Prinzessinenstraße 20, 10969 Berlin
Published under Creative Commons CC BY-NC 4.0
http://creativecommons.org/licenses/by-nc/4.0/legalcode
The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbiblio- graphie; detailed bibliographic data are available on the Internet at http://dnb.d-nb.de
Copyright: © 2015 The authors
Cover design: Andreas Karré
Cover image: Shutterstock Image ID 304478969, Copyright: Sergey Nivens
Translation: ZIS GmbH
Foreword
Digitization is causing upheaval for the economy as well as for society overall. Under these circumstances, even more than before, data is becoming a strategic resource for companies, for public organizations and for individuals. Only when high quality data about customers and products, and contextual information about their whereabouts, preferences and billing conditions exist will companies be able to provide digital services that will make life easier, open new business opportunities or make transactions between companies quicker and simpler.
Corporate data quality as a prerequisite for successful business models was and is the mission statement for the Competence Center Corporate Data Quality (CC CDQ). The CC CDQ is a consortium research project, in which more than one hundred employees from more than 30 major companies have been working with researchers from the University of St. Gallen and from Fraunhofer IML since the spring of 2006. We have been working on solutions and methods for corporate data quality in more than 40 two-day consortium workshops and with more than 200 project meetings. The content of this book has arisen almost exclusively from the CC CDQ research.
The book will address three groups of readers. Firstly, the book would like to provide support to the project and line managers for the construction and development of company-wide data quality management (DQM). Secondly, the book would like to inform students and teaching staff at colleges and universities about the foundations of data quality management as a corporate function and place a pool of cases studies in their hands. Thirdly, the book will address the significant concepts from research and practical experience for researchers interested in their application.
The contents of this book form the core of the results of the CC CDQ project. It will provide an overview of the most important issues about corporate data quality based on practical examples. The book will refer repeatedly to more detailed material provided for all questions.
This book would not have been possible without the combined capabilities and experiences of a number of people. We owe our thanks to the representatives of the companies that have participated in the CC CDQ for their active collaboration in the consortium research process. They openly discussed their companies’ problems, developed solutions together with the researchers, tested them in corporate practice and ensured that the research efforts were always enjoyable while doing all of this. Also, we would like to thank all of our scientific co-workers, who have contributed to the success of the CC CDQ with their passion and their efforts in their dissertational intents. Of these people, Rieke Bärenfänger, without whose care and determination this book would not exist, is owed special thanks.
Corporate data quality has been making many friends for us for more than eight years. We hope that the readers will also enjoy the results.
Boris Otto
Hubert Österle
Table of Contents
1 Data Quality – A Management Task.. 1
1.1 Trends in Digitization.. 3
1.1.1 Penetration into Every Area of Life and Economy. 3
1.1.4 Digital Business Models. 10
1.2.1 A 360-degree View of the Customers. 12
1.2.2 Corporate Mergers and Acquisitions. 13
1.2.5 Operational Excellence. 16
1.2.6 Data Protection and Privacy. 17
1.3 Challenges and Requirements of Data Quality Management. 18
1.3.1 Challenges in Handling Data. 18
1.3.2 Requirements on Data Quality Management. 21
1.4 The Framework for Corporate Data Quality Management. 23
1.4.1 An Overview of the Framework. 23
1.4.4 Information System Level 27
1.5 Definition of Terms and Foundations. 28