CDQ Book


Data Quality

Prerequisite for Successful Business Models

The book “Corporate Data Quality – Prerequisite for Successful Business Models” presents concepts, practical solutions and methods for corporate data quality management.


This homepage guides you through the highlights of the book’s contents, links to related material – and tells you how to get it!

Data is the foundation of the digital economy. Industry 4.0 and digital services are producing quantities of data that have been unknown until now, while also making new business models possible. Under these circumstances, data quality has become the critical factor for success. The book “Corporate Data Quality – Prerequisite for Success-ful Business Models” presents concepts, practical solutions

and methods for corporate data qualitymanagement. It presents the results of the Competence Center Corporate Data Quality (CC CDQ), 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.

The contents

Over four chapters, the book presents the essentials of practical data quality management (DQM). It firstly covers the business rationale for DQM and its core concepts. The heart of the book are ten in-depth case studies from real companies. Finally, the book presents useful tools and methods and a brief overview of success factors and immediate measures for DQM.


Chapter 01

introduces the role of data in the digitization of the economy and society as a whole and describes the most important business drivers for data quality.

This chapter presents the Framework for Corporate Data Quality Management and introduces essential terms and concepts. A section about the research efforts of the Competence Center Corporate Data Quality (CC CDQ) provides an overview of the foundations for the research methods employed by this research consortium.


Chapter 02

presents practical, successful examples of master data quality management based on ten case studies that were conducted at the CC CDQ.

The case studies cover every aspect of Framework for Corporate Data Quality Management from Chapter 1. This chapter consistently uses the central concepts for the management of the quality of master data and contains links to further material.

CDL storage

Chapter 03

describes three designated tools for master data quality management: A method for implementing DQM strategy, a maturity assessment and benchmarking portal, and a platform for collaborative business partner data management.

These tools are the results of the CC CDQ and have been distinguished through their broad applicability and their high level of innovation. The majority of the results have been applied to the cases in Chapter 2 and other real-life situations.


Chapter 04

summarizes the essential factors for successful management of the quality of master data and provides a checklist of measures that should be addressed immediately after the start of a DQM project.

Both the factors for success as well as the immediate measures are intended to guarantee a quick start into the topic and provide initial recommendations for actions to be taken by project and line managers.

What’s in it for me?

The book targets different groups of readers. It provides an overview of the most important issues about corporate data quality based on many practical examples and thorough research. The book refers repeatedly to more detailed material for all questions.


For data managers and all others with a practical interest in data quality management

Project and line managers find support for the construction and development of company-wide data quality management (DQM).


For scholars, students, teaching staff and all others with an interest in the theoretical background

Researchers and theory enthusiasts in the domain of data quality management find a systematic review of the significant concepts for DQM as a corporate function along with further research material.

Highlight sections

Ch. 1.1 – 1.3:

Drivers and challenges of data quality

Ch. 1.4:

The framework for DQM

Ch. 2:

10 real-world case studies about successful DQM and data governance projects

Ch. 3:

Methods and tools for DQM

Ch. 4:

Factors for DQM success and immediate measures

Highlight sections

Ch. 1.4:

The framework for DQM

Ch. 1.5:

Definition of terms and foundations

Ch. 2:

10 real-world case studies about successful DQM and data governance projects

Ch. 3:

Methods and tools for DQM












Business value

Data Quality Management delivers tangible business value to companies all over the world

Use the book’s insights to ...



Learn why data quality management is critical for business process and company success.



Learn which things to keep in mind before launching a new data quality management initiative or project.



Learn how to implement your DQM initiative and how to operationalize it within your companies‘ strategy, organization and systems.



Learn how to convince your stakeholders of the necessity of DQM and which success stories are already out there.

Get the book


Click here to get the full book as PDF in English or German

CDQ book

Printed version

The printed versions of the book (hardcover) can be ordered in English or German via stationary bookshops and online resellers for € 49,-


The PDF version of this book is available free of charge! The CC BY-NC license lets others copy, distribute, display, and make derivative works of it for non-commercial purposes as long as the authors are given credit for the original work.


“Data is an economic asset. Companies can no longer dispense with the management of this asset. Many of the practical examples have their foundation in science and contain specific recommenda-tions for the long-lasting assurance of data quality at companies.”

Werner Boeing
Chief Information Officer, Roche

“For the last two decades, companies have been oriented on processes. On that basis, we have determined that data will be the most important economic asset of the future. The authors have provided practical, well-founded instructions for the management of data as an asset.”

Klaus Straub
Chief Information Officer, BMW

“Data quality is not an issue of "hygiene", but rather will decide a company's success in the market. In an understandable manner, this book describes a holistic approach for the management of data at companies.“

Dr. Andreas Weber
Vice-President of Business
Development, Evonik

The authors

Prof. Dr. Boris Otto

Prof. Dr. Boris Otto

« Data is the most important strategic resource fordigital business models. This requires a new generation of data management capabilities. »

Prof. Dr. Boris Otto is chairman of the Audi-Endowed Chair of Supply Net Order Management and 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, enterprise systems, and electronic business. Together with Hubert Österle he developed the methodical basics of consortium research as a multilateral approach of collaboration between economy and science in design-oriented research questions. He gained comprehensive practical experiences at PricewaterhouseCoopers and at SAP.

Prof. em. Dr. Hubert Österle

Prof. em. Dr. Hubert Österle

« Data is the image of people, things, business, and society. Digital data is becoming the basis for more and more of our personal and professional decisions and all kinds of communication »

Prof. em. Dr. Dr. h.c. Hubert Österle was appointed chair of Information Management at the University of St. Gallen in 1980. In the scientific world he founded the Institute of Information Management (IWI-HSG), in the business world “The Information Management Group”, the Business Engineering Institute St. Gallen, the CDQ AG, and further start-ups. His main research areas are corporate data quality, business networking, business engineering, and independent living.



Prof. Dr. Boris Otto

Prof. em. Dr. Hubert Österle


Lukasstrasse 4
CH-9008 St. Gallen
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Johnson & Johnson (USA)

manages product data in the consumer goods industry in «Six Sigma» data quality.

In 2008, consumer goods company Johnson & Johnson faced serious DQ problems – less than 40% of their product data were within tolerance limits. This caused trucks to wait before delivery and wrong customer invoices to be issued. A central data management department with clear responsibilities, a workflow management system and business rules now ensure a “Six Sigma” DQ level.

Migros (Switzerland)

asks its customers for their product preferences.

On the online platform „Migipedia“, Switzerland‘s largest retailer Migros asks its customers for product feedback and new product ideas. Several new products like a new bottled ice tea were launched as a result. High-quality product data that are consistent across front-end and back-end systems as well as physical products are prerequisites for the success of this program.

Corning (USA)

accelerates product introduction with Data Governance.

Corning Inc. provides fiber optics and copper cables to telecommunication companies. The process for creating new products used to be very slow. Thanks to the introduction of Data Governance, „time to market" could be reduced by 80 percent to two days. Part of the solution were clear roles and responsibilities for product data creation and a partly automated workflow management system.

Geberit (Switzerland)

streamlines the product portfolio with «Lean Data Management».

Sanitary parts and systems company Geberit faced high costs from product data maintenance due to an ever increasing number of parts and products. After the establishment of a new master data organization and the implementation of „lean management“ principles for DQM, the number of actively maintained records could be reduced by two thirds and indirect costs could be significantly reduced.

Bosch (Germany)

improves the «first time right» – ratio with HANA.

Multinational engineering and technology company Bosch processes millions of data records for products, customers, suppliers, employees, and raw materials. A central data quality platform based on SAP HANA allows continuous high-performance data cleansing and duplicate removal. A „first time right“ functionality saves employees time for their main tasks and reduces the need for reactive DQM.