Corporate Data Quality E-Book

Boris Otto

• Herausgeber: epubli
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 286

Veröffentlichungsjahr: 2015

Beschreibung

Data is the foundation of the digital economy. Industry 4.0 and digital services are producing so far unknown quantities of data and make new business models possible. Under these circumstances, data quality has become the critical factor for success. This book presents a holistic approach for data quality management and presents ten case studies about this issue. It is intended for practitioners dealing with data quality management and data governance as well as for scientists. The book was written at the Competence Center Corporate Data Quality (CC CDQ) in close cooperation between researchers from the University of St. Gallen and Fraunhofer IML as well as many representatives from more than 20 major corporations. Chapter 1 introduces the role of data in the digitization of business and society and describes the most important business drivers for data quality. It presents the Framework for Corporate Data Quality Management and introduces essential terms and concepts. Chapter 2 presents practical, successful examples of the management of the quality of master data based on ten cases studies that were conducted by the CC CDQ. The case studies cover every aspect of the Framework for Corporate Data Quality Management. Chapter 3 describes selected tools for master data quality management. The three tools have been distinguished through their broad applicability (method for DQM strategy development and DQM maturity assessment) and their high level of innovation (Corporate Data League). Chapter 4 summarizes the essential factors for the successful management of the master data quality and provides a checklist of immediate measures that should be addressed immediately after the start of a data quality management project. This guarantees a quick start into the topic and provides initial recommendations for actions to be taken by project and line managers. Please also check out the book's homepage at cdq-book.org/

Leseprobe

Corporate DataQuality

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

ISBN978-3-7375-7592-8

ISBN978-3-7375-7593-5(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 athttp://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

1Data Quality – A Management Task..1

1.1Trends in Digitization..3

1.1.1Penetration into Every Area of Life and Economy.3

1.1.2Industry 4.0..5

1.1.3Consumerization..7

1.1.4Digital Business Models.10

1.2Data Quality Drivers.11

1.2.1A 360-degree View of the Customers.12

1.2.2Corporate Mergers and Acquisitions.13

1.2.3Compliance.14

1.2.4Reporting Systems.15

1.2.5Operational Excellence.16

1.2.6Data Protection and Privacy.17

1.3Challenges and Requirements of Data Quality Management.18

1.3.1Challenges in Handling Data.18

1.3.2Requirements on Data Quality Management.21

1.4The Framework for Corporate Data Quality Management.23

1.4.1An Overview of the Framework.23

1.4.2Strategic Level23

1.4.3Organizational Level25

1.4.4Information System Level27

1.5Definition of Terms and Foundations.28

1.5.1Data and Information..29

1.5.2Master Data.31

1.5.3Data Quality.32

1.5.4Data Quality Management (DQM).34

1.5.5Business Rules.35

1.5.6Data Governance.37

1.6The Competence Center Corporate Data Quality.38

2CaseStudies of Data Quality Management.42

2.1Allianz: Data Governance and Data Quality Management in the Insurance Sector44

2.1.1Overview of the Company.44

2.1.2Initial Situation and Rationale for Action..45

2.1.3The Solvency II Project.46

2.1.4Data Quality Management at AGCS.46

2.1.5Insights.52

2.1.6Additional Reference Material52

2.2Bayer CropScience: Controlling Data Quality in the Agro-chemical Industry53

2.2.1Overview of the Company.53

2.2.2Initial Situation and Rationale for Action..54

2.2.3Development of the Company-wide Data Quality Management57

2.2.4Insights.64

2.2.5Additional Reference Material65

2.3Beiersdorf: Product Data Quality in the Consumer Goods Supply Chain65

2.3.1Overview of the Company.65

2.3.2Initial Situation of Data Management and Rationale for Action67

2.3.3The Data Quality Measurement Project.71

2.3.4Insights.77

2.3.5Additional Reference Material78

2.4Bosch: Management of Data Architecture in a Diversified Technology Company79

2.4.1Overview of the Company.79

2.4.2Initial Situation and Rationale for Action..80

2.4.3Data Architecture Patterns at Bosch..82

2.4.4Insights.87

2.4.5Additional Reference Material87

2.5Festo: Company-wide Product Data Management in the Automation Industry88

2.5.1Overview of the Company.88

2.5.2Initial Situation and Rationale for Action regarding the Management of Product Data90

2.5.3Product Data Management Projects between 1990 and 200996

2.5.4Current Activities and Prospects.101

2.5.5Insights.102

2.5.6Additional Reference Material103

2.6Hilti: Universal Management of Customer Data in the Tool and Fastener Industry104

2.6.1Overview of the Company.104

2.6.2Initial Customer Data Management Situation and Rationale for Action105

2.6.3The Customer Data Quality Tool Project.106

2.6.4Insights.113

2.6.5Additional Reference Material114

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

2.7.1Overview of the Company.114

2.7.2Initial Data Management Situation in the Consumer Products Division and Activities up to 2008.115

2.7.3Introduction of Data Governance.116

2.7.4Current Situation..118

2.7.5Insights.122

2.7.6Additional Reference Material124

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

2.8.1Overview of the Company.125

2.8.2Initial Data Management Situation and Business Intelligence 2004 – 2011126

2.8.3Master Data Management at Lanxess since 2011.126

2.8.4Structure of the Strategic Reporting System since 2012.129

2.8.5Insights.133

2.8.6Additional Reference Material135

2.9Shell: Data Quality in the Product Lifecycle in the Mineral Oil Industry135

2.9.1Overview of the Company.135

2.9.2Initial Situation and Rationale for Action..136

2.9.3Universal Management of Data in Product Lifecycle.137

2.9.4Challenges during Implementation..137

2.9.5Using the New Solution..138

2.9.6Insights.139

2.9.7Additional Reference Material139

2.10Syngenta: Outsourcing Data Management Tasks in the Crop Protection Industry140

2.10.1Overview of the Company.140

2.10.2Initial Situation and Goals of the Master Data Management Initiative141

2.10.3The Transformation Project and the MDM Design Principles143

2.10.4Master Data Management Organizational Structure.145

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

2.10.6Insights.153

2.10.7Additional Reference Material153

3Methodsand Tools for Data Quality Management.155

3.1Method for DQM Strategy Development and Implementation155

3.1.1Structure of the Method.156

3.1.2Examples of the Techniques used by the Method.157

3.2Maturity Assessment and Benchmarking Platform for Data Quality Management163

3.2.1Initial Situation..163

3.2.2Maturity Model and Benchmarking as Control Instruments164

3.2.3The EFQM Model of Excellence for the Management of Master Data Quality166

3.2.4Corporate Data Excellence: Control Tools for Managers of Data Quality167

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

3.3.1Challenges in Maintaining Business Partner Data.170

3.3.2The Cooperative Data Management Approach..171

3.3.3The Corporate Data League.172

3.4Additional Methods and Tools from CC CDQ..176

4Factorsfor Success and Immediate Measures.178

4.1Factors for the Success of Data Quality Management.178

4.2Immediate Measures on the Path to Successful Data Quality Management179

5Bibliography..181

6Glossary..193

Table of Abbreviations

API

Application Programming Interface

BE

Business Engineering

CAD

Computer-aided Design

CC CDQ

Competence Center Corporate Data Quality

CDL

Corporate Data League

CDQM

Corporate Data Quality Management

CIQ

Customer Information Quality

COO

Chief Operating Officer

CRM

Customer Relationship Management

CRUD

Create, Read, Update, Delete (database operations)

DAMA

Data Management Association

DQM

Data Quality Management

DUNS

Data Universal Numbering System

EFQM

European Foundation for Quality Management

ERP

Enterprise Resource Planning

EU

European Union

GS1

Global Standards One

GTIN

Global Trade Item Number

IRR

Internal Rate of Return

IS

Information System

ISO

International Standards Organization

IT

Information Technology

LCC

Lifecycle Costing

MDM

Master Data Management

NPV

Net Present Value

OMG

Open Management Group

p.a.

per annum

PIM

Product Information Management

PLM

Product Lifecycle Management

ROI

Return on Investment

SBVR

Semantics of Business Vocabulary and Rules

SCM

Supply Chain Management

TCO

Total Cost of Ownership

TQM

Total Quality Management

XAL

Extensible Address Language

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 Ottoholds the Audi-Endowed Chair of Supply Net Order Management at theTechnical University of Dortmundand 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.

1Data Quality – A Management Task

Chapter Summary

Chapter 1 will introduce the role of data in the digitization of business and society and describes the most important business drivers for data quality. For companies, data represents a strategic resource that must be cultivated with a view towards the issues of time, expense and, naturally, quality. Data quality management is the corporate function for improving and assuring the quality of the company’s data in an enduring manner. This chapter will present the Framework for Corporate Data Quality Management and introduce essential terms and concepts. A section about the Competence Center Corporate Data Quality (CC CDQ)’s research efforts will provide an overview of the foundations for the research methods employed by this consortium.

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 (Ericsson2012). 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 ofhackers (Dahlkamp and Schmitt2014) 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 inChapter2will 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.

Chapter3will 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.

Chapter4will summarize the primary insights of the approaches described for solutions and present a list of immediate measures for improved data quality management.

1.1Trends inDigitization

New forms of information technology are changing every area of the economy as well as of society overall, as researchers, such asKagermann (2014),have analyzed them from the view of the Federal Republic of Germany. We have summarized the development into four trends (Figure 1-1).

Figure 1-1: Mega-trends in Digitization (authors’ illustration)

1.1.1Penetration into every Area of Life and Economy

According to the International Telecommunications Union, 2.9 billion people usedthe Internet in 2014, meaning roughly 40% of the world population (ITU2015). Thetechnological innovations of the last 15 years are responsible for this penetration into both the private and business areas.

·Mobility: wireless networks and miniaturization of computers and other components, like sensors and cameras, are bringing digital services to the location of use, whether in private life, such as recording a hiking route, or in business, such as remote diagnosis of a machine.

·Usability: touch screens and many other improvements in details, like logging into digital services through a Facebook account or vocal input and output systems, have drastically reduced the threshold for usage. Other efforts to ease usage, like data glasses (such as Google Glass), control using gestures and detection of eye movements, have also been distinguishing themselves.

·Content and community: whether individually (such as through blogs and tweets) or in combination (such as Facebook), innumerable people have been producing a volume of content in the form of written words, pictures, audio and video files, which can only be reviewed by machines. YouTube recorded more than one billion requests for video clips per day in June 2014[2]. Facebook recorded roughly 1.3 billion active users in March 2014[3].

·Communication: this content is being exchanged synchronously, asynchronously, privately and for business. Accessing email, text messaging, and social networking are among the top four most-popular daily activities of smartphone owners in the United States in January 2014 (statista2015). Visual communication has been increasingly supplementing more common audio telephony and instant messaging services (such as WhatsApp) are frequently used in addition to email messages.

·Big data: unexpectedly high volumes of data are the result of the penetration of the economy and society overall by digital services, while at the same time they are the foundation for the personalization of services, especially those based on providing location information (Figure 1-2).

Figure 1-2: Online Activities for Private Purposes over the Last Three Months in Swiss Households (Froideveaux2012p. 25)

Almost one quarter of the world population used smartphones in 2014 and in both North America and Western Europe, about 50 percent of total population used smartphones in one way or another (statista2015). Digital networking has had an enormous impact on the formation of people’s opinions in their political, economic and private affairs. From the view of data management, the following aspects (among others) should be taken into consideration.

·Data security: until now, the Intranet was considered the perimeter, meaning the boundary where data had to be secured. This boundary has dissolved and companies must go beyond it to protect not only networks and application systems but also enable data objects, which themselves must know who should be permitted read access and who should not (O'Brien2014).

·Data production: classically, companies have acquired data centrally (such as customer data collected through a central, internal marketing service). Due to the spread of social media and social networks however, data consumers are increasingly becoming data producers (Strong et al.1997). Customer data can be acquired directly from the customer or from external agencies by smartphones or onsite tablets. Employees expect that the data will be accessible from everywhere.

·Streams instead of records: millions of users generate data flows in social networks and through social media. This represents new challenges to companies, because the traditional processing of data was oriented on transactions, meaning that individual records were written persistently to databases. However, the increasing usage of data streams from social networks, such as from the cyber-physical systems in Industry 4.0, can no longer be updated incrementally, but rather must be followed continuously (BITKOM2014).

1.1.2Industry 4.0

The term “Industry 4.0” stands for the Fourth Industrial Revolution, meaning the merger of the physical and virtual worlds through so-called “cyber-physical systems” (Bauernhanslet al.2014). The data will be acquired more precisely and in more detail than previously without time delays or the help of people. Machines are becoming capable of working with the Internet, assuming the tasks of production and data distributionindependently and the data, which has only been available in the factory for a long time, is becoming accessible to the entire company and its business partners (Figure 1-3).

Figure 1-3: Data Acquisition at the Interface between the Virtual and Physical Worlds (Fleisch2010; Wahlster2011p. 5)

Industry 4.0 scenarios are changing the basic handling of data both within and between companies. Three issues make this clear.

·Decentralization of data management: things are becoming “smart”, meaning that they produce, use and have an increasing amount of data and rely less on central control systems. As a consequence, things are assuming increasing importance in the distribution of data without requiring central computers.

·From the class to the instance: the focus of electronic data processing has been on traditional classes of things, meaning articles with a certain Global Trade Item Number (GTIN) or products with certain material numbers. Industry 4.0 now means that each instance of a class of products can be identified, meaning individual hydraulic cylinders or the individual bottles of hydraulic fluid (Österle and Otto 2014).

·Continuous combination of the flow of information and goods: traditionally, industrial data processing has targeted the flow of information and goods to certain control points. One example is the goods receipt record in the central warehouse for the delivery of goods. Industry 4.0 scenarios use RFID technologies, for example, and enable access to the status and location information for the individual products at any time (Österle and Otto 2014).

The inBin intelligent container developed by the company SICK[4]together with the Fraunhofer Institut für Materialfluss und Logistik (Fraunhofer IML, Institute for Material Flow and Logistics) is one example of an Industry 4.0 application. The inBin knows its location, records its environmental temperature and arranges for its own pickup (Figure 1-4).

Figure 1-4: The inBin intelligent container (Fraunhofer IML2015)

A powerful data management system that fulfills the following requirements is the prerequisite for the success of Industry 4.0 in individual companies as well as across supply chains.

·Mastery of the volume of data: the data management system in the company must be capable of processing and reasonably evaluating the amounts of data (Wrobel et al. 2014).

·Decentralized data processing: when machines, containers, freight and so on become intelligent, this means that they will have to assume the tasks of processing their own data. Data analysis, aggregation and provision therefore no longer occur centrally in the Enterprise Resource Planning (ERP) and data warehousing systems, but rather locally onsite. Central corporate data processing will be supplemented by a network of decentralized intelligent devices (Aggarwal et al. 2013).

·Determination of data standards: advantages in terms of time, expenses and quality through the use of cyber-physical systems and automated data interchange can only be realized when standards for the description and exchange of data have been established. These standards must apply internally to the company, at least, and their applicability across entire supply chains would be better (Otto et al. 2014). The MobiVoc initiative developed, for example, a data vocabulary for new mobile solutions[5].

1.1.3Consumerization

Every one of us today uses a number of different consumer services that support various situations of ourlives (Österle2014).Figure 1-5depictsten areas of life in which people use digital services, from support for navigation to listening to music, from comparing prices to controlling the illumination of homes remotely. As an example, the area of communication has been expanded with two additional layers in order to provide an impression of the multitude of services. A more detailed, but still incompletemind map of digital consumer services can be found atil.iwi.unisg.ch/appmap(Amiona2014).

Figure 1-5: Ten Areas of Life and Examples of Digital Services supporting Them (Amiona2014)

At the same time, consumers increasingly expect digital services to be customized to their individual needs. Companies are reacting to the consumerization of information technology by orienting their business processes on the consumers’ needs, thus the consumer process. This process consists of all activities that an individual accomplishes for the fulfillment of various needs (such as purchasing, athletics and traveling) in a certain situation of the consumer’s life.

Consumerization leads to a new role for consumers in economic life (“consumer centricity”). They are no longer the terminal or transitional points in the unidirectional flow of goods and information, but rather directly affect public opinions of products and companies through platforms like FoodWatch.org and are now acting both as consumer and producer of goods and services. Examples include the floods of indignation that descended on Nestlé because of the use of palm oil in KitKat chocolate bars and the crowd sourcing of programming services.

Figure 1-6: Network Analysis of the Flow of Product Information at Beiersdorf (Schierning2012p. 9)

Figure 1-6depicts an example of how the flow of product information has changed over a period of five years at Beiersdorf, a manufacturer of consumer goods. On the one hand, the number of participants in the company network increased from 2007 to 2012, because companies like Apple and Google, as well as online retailers like Zalando use and distribute Nivea, for example. Borrowing a term from ecology, the expanded corporate network can also be viewed as an ecosystem. On the other hand, consumers have moved from the periphery to center with regard to control of thedata, since nearly every company in the network interacts with consumers (Schierning2012).

Nestlé not only maintain classic corporate data systems, but also consumer data. Nestlé had 94 million fans on Facebook and 16 million clicks to view their Contrex video on YouTube. Data from online shops, where Nespresso sold more than 50% of their coffee packets for example, should be added to these figures.

Consumer centricity means a rejection of the traditional corporate-centered view of the end customers for companies. The design and improvement of interaction with consumers is no longer the only focus from the view of the companies (Inside-out approach), but also the integral consumer process across the boundaries of individual companies (Outside-in approach).

Consumerization places new requirements on the management of data.

·Data ownership: who does the data belong to? This multi-facetted discussion about data protection and statements like those of Mark Zuckerberg of Facebook, that data security is no longer a social standard (Johnson2010),indicate that the trend in consumerization has surpassed the traditional understanding of ownership and possession of immaterial goods. So-called “data brokers” collect personal Internet data in legal gray areas (Anthes 2015). For companies, this means that they must formulate a uniform legal position in regards to data protection. Legislatures are being asked to crate uniform frameworks.

·Data integration: people no longer use a single channel for communication in order to connect to a company, but rather use multiple channels. The Swiss retailer, Migros, identified nine different channels (offline and online) through which they communicate with consumers. This diversity includes traditional letters, online shops and email and text messages. Because consumers expect to be uniquely identified through all channels and to get the same prices and rebates on Migros products, the company had to provide consistent, current and complete data about their customers and products across all channels (Schemm 2012).

·Combinations of “structured” and “unstructured” data: as a consequence of consumerization, companies are no longer only providing information in traditional alphanumeric formats, such as descriptions, weights and prices about the products, but are more often providing product video clips, marketing information and lists of active ingredients. The differences between product data (which is generally stored in central ERP or Product Lifecycle Management (PLM) systems) and multimedia product information (which is frequently distributed using a number of internal application systems and external service providers, such as advertising agencies) can no longer be maintained (Österle and Otto 2014).

1.1.4Digital Business Models

The penetration of digital services into the economy and society overall and in particular, of the industrial and consumer sectors, will lead to new types of business models outside of classic companies[6]. Examples from the area of consumer services include Google as well as Airbnb, idealo and many other companies that bring a large number of consumers and business customers together with a large number of providers. These companies have been assuming a role as brokers between the supply and demand for services from a variety of participants. From a more technical point of view, one frequently speaks of the “Internet of services”. Four developments characterize these business models.

·Focus on data: new business models for the Internet-based service economy use data as a strategic resource (seeFigure 1-7). For example, Deutsche Post provides high-resolution geographic information for retailers, insurers, real estate agents and public administration and other customers (data as the product)[7] through the GEOVISTA service.

·Industrial convergence: traditional sector boundaries are losing their significance. Google is one of the innovation drivers for autonomous cars; classic vehicle manufacturers are potential licensees for this technology. Amazon has transformed itself from a book retailer into a fulfillment expert, who offers special capabilities like scalable IT infrastructure services or provides logistics service to companies from many sectors and even consumers.

·Hybrid services: often, digital business models combine digital services with classic offline services. One example involves the car-sharing models that combine digital rental and provision of cars including payment (generally supported by smartphone apps) with the classic services of mobility.

·Consumer process: the Internet of services is oriented on the individual, meaning the individual consumer, the patient, the service technician or the shopper. The goal is “end-to-end” support for life situations, such as purchasing, jobs, mobility, therapy and health care (Österle and Senger 2011).

Figure 1-7: Digital Business Models (Brenner and Herrmann2012p. 20)

1.2Data Quality Drivers

Digital business models and the Internet of services are based on the resource of data. Data quality is therefore no longer a question of “hygiene” or even of internal use by line departments,but rather has become critical for operational excellence. Data quality is defined as a measure of the applicability of data forfulfilling certain requirements in business processes, where it is used (Otto et al.2011). Thefollowing material will constantly treat “data management” with special consideration of data quality management.

The most important drivers for quality-oriented data management include:

·360-degree view of the customers

·Corporate mergers and acquisitions

·Compliance

·Reporting systems

·Operational excellence

·Data protection and privacy

1.2.1A 360-degree View of the Customers

Knowledge about the customers is the starting point for marketing and sales, as well as for the development of products and services. For this reason, companies must be capable of gaining access to all information about the customers’ needs. For consumers, such information includes Internet surfing behavior, purchases and peer groups in social networks; for business customers, their addresses, subsidiaries, contact information and the name of their contact people as well as data about purchased products and existing contracts.

Bühler is a globally active manufacturer of production systems specializing in the industry of food. They make digital customer profiles available to their employees in the Customer Service and Marketing departments. These profiles answer questions such as:

·How much revenue has been made with the customer (and all of its subsidiaries) in the current fiscal year?

·Which of our systems and services are used by the customer and at which locations?

·When will maintenance contracts expire?

·Which employees made contact with which customer employees in the last three months? What were the results of such contacts?

·How profitable is the customer relationship?

The 360-degree view of the customers places many requirements on quality-oriented data management.

·Data quality: customer data must be consistently, currently and completely available for all functional departments (Marketing, Services, etc.).

·Data lifecycle: how customer data is acquired by the company, where it was acquired and stored, who will modify and change the data and which business processes and systems use the data must be clearly defined.

·Data security: with consumer data, provisions must be made to ensure that data protection provisions will be maintained, including that customer data will be deleted upon request.

·Data governance: companies must clearly determine who will be responsible for which customer data in the company. Is the field staff responsible for customer addresses or the internal Marketing department? Can service employees change the customer status to active? Who will collect email messages with this customer or their Facebook pictures?

1.2.2Corporate Mergers and Acquisitions

Corporate mergers and acquisitions are important tools for corporate strategies. In the chemical industry for example, BASF has taken over the electro-chemical division of Merck, the fine chemistry company, Orgamol, the catalyzer manufacturer, Engelhard, the building chemistry division of Degussa and the special chemical group, Ciba, since 2005. These acquisition have been integrated in the uniform application systems and business processes.

Nestlé represents another example of corporate integration. The company operates more than 2000 different brands, which are produced in almost 90 countries and sold in more than 190 countries[8]. Of their total revenue amounting to more than 92 billion Swiss franks in 2013, 93% are processed through the GLOBE central Enterprise Resource Planning system.Figure 1-8depicts several pieces of important information about GLOBE.

Figure 1-8: Important Information about the GLOBE Central System at Nestlé (according to Muthreich2013p. 18)

The GLOBE program has pursued three goals since it began operations in 2001, specifically: the company-wide use of best practices based on shared business processes, the introduction of standardized application systems and the use of data as an asset. The prerequisite for this is a powerful data management system, which has integrated many corporate acquisitions over the last few years in particular.

·Data standards: binding specifications for the acquisition, maintenance and use of master data, such as customer, supplier and material and product information, must be applied.

·Data acquisition at the source: due to the size and complexity of the company, data cannot be acquired centrally, but rather as close as possible to its source.

·Data quality