Corporate Data Quality

Corporate Data Quality

Boris Otto • Hubert Österle

Corporate Data Quality

Prerequisite for Successful Business Models

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

http://www.epubli.de

Published under Creative Commons CC BY-NC 4.0

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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

1.1.2 Industry 4.0.. 5

1.1.3 Consumerization.. 7

1.1.4 Digital Business Models. 10

1.2 Data Quality Drivers. 11

1.2.1 A 360-degree View of the Customers. 12

1.2.2 Corporate Mergers and Acquisitions. 13

1.2.3 Compliance. 14

1.2.4 Reporting Systems. 15

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

1.4.2 Strategic Level 23

1.4.3 Organizational Level 25

1.4.4 Information System Level 27

1.5 Definition of Terms and Foundations. 28

1.5.1 Data and Information.. 29

1.5.2 Master Data. 31

1.5.3 Data Quality. 32

1.5.4 Data Quality Management (DQM). 34

1.5.5 Business Rules. 35

1.5.6 Data Governance. 37

1.6 The Competence Center Corporate Data Quality. 38

2 Case Studies of Data Quality Management. 42

2.1 Allianz: Data Governance and Data Quality Management in the Insurance Sector 44

2.1.1 Overview of the Company. 44

2.1.2 Initial Situation and Rationale for Action.. 45

2.1.3 The Solvency II Project. 46

2.1.4 Data Quality Management at AGCS. 46

2.1.5 Insights. 52

2.1.6 Additional Reference Material 52

2.2 Bayer CropScience: Controlling Data Quality in the Agro-chemical Industry 53

2.2.1 Overview of the Company. 53

2.2.2 Initial Situation and Rationale for Action.. 54

2.2.3 Development of the Company-wide Data Quality Management 57

2.2.4 Insights. 64

2.2.5 Additional Reference Material 65

2.3 Beiersdorf: Product Data Quality in the Consumer Goods Supply Chain 65

2.3.1 Overview of the Company. 65

2.3.2 Initial Situation of Data Management and Rationale for Action 67

2.3.3 The Data Quality Measurement Project. 71

2.3.4 Insights. 77

2.3.5 Additional Reference Material 78

2.4 Bosch: Management of Data Architecture in a Diversified Technology Company 79

2.4.1 Overview of the Company. 79

2.4.2 Initial Situation and Rationale for Action.. 80

2.4.3 Data Architecture Patterns at Bosch.. 82

2.4.4 Insights. 87

2.4.5 Additional Reference Material 87

2.5 Festo: Company-wide Product Data Management in the Automation Industry 88

2.5.1 Overview of the Company. 88

2.5.2 Initial Situation and Rationale for Action regarding the Management of Product Data 90

2.5.3 Product Data Management Projects between 1990 and 2009 96

2.5.4 Current Activities and Prospects. 101

2.5.5 Insights. 102

2.5.6 Additional Reference Material 103

2.6 Hilti: Universal Management of Customer Data in the Tool and Fastener Industry 104

2.6.1 Overview of the Company. 104

2.6.2 Initial Customer Data Management Situation and Rationale for Action 105

2.6.3 The Customer Data Quality Tool Project. 106

2.6.4 Insights. 113

2.6.5 Additional Reference Material 114

2.7 Johnson & Johnson: Institutionalization of Master Data Management in the Consumer Goods Industry. 114

2.7.1 Overview of the Company. 114

2.7.2 Initial Data Management Situation in the Consumer Products Division and Activities up to 2008. 115

2.7.3 Introduction of Data Governance. 116

2.7.4 Current Situation.. 118

2.7.5 Insights. 122

2.7.6 Additional Reference Material 124

2.8 Lanxess: Business Intelligence and Master Data Management at a Specialty Chemicals Manufacturer. 125

2.8.1 Overview of the Company. 125

2.8.2 Initial Data Management Situation and Business Intelligence 2004 – 2011 126

2.8.3 Master Data Management at Lanxess since 2011. 126

2.8.4 Structure of the Strategic Reporting System since 2012. 129

2.8.5 Insights. 133

2.8.6 Additional Reference Material 135

2.9 Shell: Data Quality in the Product Lifecycle in the Mineral Oil Industry 135

2.9.1 Overview of the Company. 135

2.9.2 Initial Situation and Rationale for Action.. 136

2.9.3 Universal Management of Data in Product Lifecycle. 137

2.9.4 Challenges during Implementation.. 137

2.9.5 Using the New Solution.. 138

2.9.6 Insights. 139

2.9.7 Additional Reference Material 139

2.10 Syngenta: Outsourcing Data Management Tasks in the Crop Protection Industry 140

2.10.1 Overview of the Company. 140

2.10.2 Initial Situation and Goals of the Master Data Management Initiative 141

2.10.3 The Transformation Project and the MDM Design Principles 143

2.10.4 Master Data Management Organizational Structure. 145

2.10.5 The Data Maintenance Process and Decision-making Criteria for the Outsourcing Initiative. 149

2.10.6 Insights. 153

2.10.7 Additional Reference Material 153

3 Methods and Tools for Data Quality Management. 155

3.1 Method for DQM Strategy Development and Implementation 155

3.1.1 Structure of the Method. 156

3.1.2 Examples of the Techniques used by the Method. 157

3.2 Maturity Assessment and Benchmarking Platform for Data Quality Management 163

3.2.1 Initial Situation.. 163

3.2.2 Maturity Model and Benchmarking as Control Instruments 164

3.2.3 The EFQM Model of Excellence for the Management of Master Data Quality 166

3.2.4 Corporate Data Excellence: Control Tools for Managers of Data Quality 167

3.3 The Corporate Data League: One Approach for Cooperative Data Maintenance of Business Partner Data. 170

3.3.1 Challenges in Maintaining Business Partner Data. 170

3.3.2 The Cooperative Data Management Approach.. 171

3.3.3 The Corporate Data League. 172

3.4 Additional Methods and Tools from CC CDQ.. 176

4 Factors for Success and Immediate Measures. 178

4.1 Factors for the Success of Data Quality Management. 178

4.2 Immediate Measures on the Path to Successful Data Quality Management 179

5 Bibliography.. 181

6 Glossary.. 193

Table of Abbreviations

About the Authors

Prof. em. Dr. Dr. h.c. Hubert Österle was professor for Business Engineering and director of the Institute of Information Management at the University of St. Gallen (IWI-HSG) from 1980 to 2014. In 1989, he founded the Information Management Group and served in the company’s management and supervisory boards. In 2006, he founded the Business Engineering Institute St. Gallen AG and is presiding over its supervisory board. He is also member of the supervisory board of the CDQ AG. His main research areas are life engineering, corporate data quality, business networking, business engineering, and independent living.

Prof. Dr. Boris Otto holds the Audi-Endowed Chair of Supply Net Order Management at the Technical University of Dortmund and is director for Information Management and Engineering at the Fraunhofer Institute for Material Flow and Logistics. The focal points of his research and teaching fields are business and logistic networks, corporate data management as well as enterprise systems and electronic business. Boris Otto studied Industrial Engineering in Hamburg and received his doctor’s degree under the supervision of Prof. Hans-Jörg Bullinger at the University of Stuttgart. He habilitated at the University of St. Gallen under the supervision of Prof. Hubert Österle. Further research appointments were at the Fraunhofer Institute for Industrial Engineering in Stuttgart and at the Tuck School of Business at Dartmouth College in New Hampshire in the United States. He gained comprehensive practical experiences at PricewaterhouseCoopers and at SAP. Boris Otto is a member of the scientific advisory board of eCl@ss e.V., a leading standard-setting organization for the classification of articles and products. He also heads the Data Innovation Lab at the Fraunhofer Innovation Center for Logistics and IT and is president of the supervisory board of the CDQ AG.

1 Data Quality – A Management Task

Data is the foundation of the digital economy. The penetration of all areas of life and business with “digital services” supplies data as the fuel for new services, new access to customers, new pricing models, new economic systems and finally for a major percentage of the innovation decisive for business. All IT applications generate electronic data, which in turn creates a flood of data that has not been seen until now and which needs to be understood and used.

Ericsson, for example, is a leading provider of telecommunications products and services. With its headquarters in Stockholm, Sweden, this company provides solutions for the broadband mobile Internet, among other services. The use of these solutions creates new data. At the same time, Ericsson is re-positioning their services away from the field of network technologies into the field of digital services. Together with the Maersk container shipping company, Ericsson provides information transparency across the global supply chain (Ericsson 2012). Thus, for example, the maturity of bananas on trans-oceanic ships from South America to Europe can be continuously monitored and shipping speeds and losses at the destination port can be adjusted as needed. This leads to improved flow of goods at the port, optimization of fuel consumption by ships and, ultimately, to customer satisfaction at fruit stands in supermarkets.

An increasingly higher level of data quality is being demanded by corporate innovations as well as by the classic data quality drivers like business process harmonization. Because of the digital connectivity of entire value networks, data errors and misuse are having more significant effects than they did in the age of isolated IT applications. For example, organized groups of hackers (Dahlkamp and Schmitt 2014) are hacking into email traffic between companies, presenting themselves as creditors and redirecting payments for deliveries and services to fraudulent accounts. Often, this does not become obvious until the right creditor sends payment reminders for late payments. At that point, the transaction can no longer be reversed.

Data quality is not an “issue of hygiene”, but requires management. In the digital economy, companies must cultivate data like any other economic assets, especially with regard to cost, time and, of course, quality.

Structure of this Book

The first chapter of this book will review current data quality management drivers and introduce the Framework for Corporate Data Quality Management. In addition, this will be combined with the state of the science and practices regarding data quality management and will lead into the core concepts.

The case studies in Chapter 2 will show how important companies have made data quality a duty for all levels of management. The quality of the master data[1] cannot be guaranteed in one, central IT department, but rather must be ensured at the location of data acquisition and usage, meaning in the business divisions. The case studies document how ten companies from different sectors have anchored data quality management in everyday business routines.

Chapter 3 will present methods and tools that will provide support to companies constructing a successful master data quality management system. All of the methods have been tested several times in practice.

Chapter 4 will summarize the primary insights of the approaches described for solutions and present a list of immediate measures for improved data quality management.