The document provides an overview of corporate data quality research and services from the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen. It discusses how data quality is a success factor for business and introduces the CC CDQ's research focus areas, consortium of partner companies, and services which include assessments, strategy development, and knowledge sharing. The CC CDQ team leverages expertise in research and consulting to help organizations improve data quality management.

1. Corporate Data Quality
Research and Services Overview
Prof. Dr. Boris Otto, Assistant Professor
St. Gallen, March 2012
Chair of Prof. Dr. Hubert Österle

2. Competence Area Corporate Data Quality
Competence Center Business Engineering
Corporate Data Quality Institute St. Gallen AG
Applied Consortium Research Business Value Transformation
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3. Table of Content
 Data Quality as a Success Factor for Business
 Competence Center Corporate Data Quality
 BEI Project References
 Team Overview
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4. Data quality is necessary to respond to a number of
strategic business requirements
1 Customer-Centric Business Models
$ Value Chain Excellence
Contractual and Regulatory Compliance
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5. Complexity drivers pose challenges on data quality
management
Size “Big Data”
Revenue Nestlé 2010: 110 billion CHF RFID, customer loyalty programs
Federal budget CH 2008: 57 billion CHF etc.
“Hyper-Connectivity” Corporate Globalized Operations
Social media, data supply chains Data Multilingualism, “Follow the sun“-
etc. Quality principle etc.
Constant Change “Taylorism”
M&A, “Divestments”, Change Segregation of data creation and
Management data use
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6. Today, companies manage data quality purely in a
reactive mode
Data quality
: “Submarines” of data quality, e.g. data
migration, incorrect reports, process errors).
Project 1 Project 2 Project 3 Time
 No risk management possible
 No chance to plan and to control budgets and resources
 No target values for corporate data quality
 No sustainability of increased data quality
 High recurring project costs (change requests, external consultants etc.)
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7. Costing for data quality must find a trade-off between
preventive and reactive measures
Costs (C)
C
Total costs of data quality
Costs related to DQM
Follow-up costs in business as a result
of data defects
DQ DQM: Data quality management
Cost-optimal Data quality
data quality level (DQ)
Otto, B., Hüner, K., Österle, H.: A Cybernetic View on Data Quality Management, AMCIS 2010 Proceedings, Peru, 14.08.2010, 2010, http://aisel.aisnet.org/amcis2010/423
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8. Table of Content
 Data Quality as a Success Factor for Business
 Competence Center Corporate Data Quality
 BEI Project References
 Team Overview
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9. The Competence Center Corporate Data Quality (CC
CDQ) responds to urgent issues
 How does Corporate Data Quality contribute to the strategic business objectives?
 How does our company compare to others in our peer group?
 How can we measure our performance in Corporate Data Quality Management?
 What are the costs and benefits of Corporate Data Quality?
 How can we establish Data Governance in the company?
 What is the appropriate degree of standards and regulation for our company?
 How do we achieve consistent understanding of corporate data? What is the
baseline of Corporate Data Quality?
 Which data architecture is the right one and how do we implement it?
 How do we benefit from innovative technologies (e.g. Social Media, Linked Data)?
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10. The consortium comprises more than 20 research
partner companies
AO FOUNDATION ASTRAZENECA PLC BAYER AG BEIERSDORF AG
CORNING CABLE SYSTEMS GMBH DAIMLER AG DB NETZ AG E.ON AG
ETA SA FESTO AG & CO. KG HEWLETT-PACKARD GMBH IBM DEUTSCHLAND GMBH
KION INFORMATION MANAGEMENT
MIGROS-GENOSSENSCHAFTS-BUND NESTLÉ SA NOVARTIS PHARMA AG
SERVICE GMBH
SIEMENS ENTERPRISE
ROBERT BOSCH GMBH SAP AG SYNGENTA CROP PROTECTION AG
COMMUNICATIONS GMBH & CO. KG
TELEKOM DEUTSCHLAND GMBH ZF FRIEDRICHSHAFEN AG NB: Overview comprises both current and past research partner companies.
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11. The CC CDQ Framework in the context of Business
Engineering
Mandate Strategy
Strategy document Goals and targets
Strategy for CDQ
Value management Data quality metrics
Roadmap
Organization
CDQ Controlling Data life cycle
Data Governance management
Roles and Business metadata
responsibilities management
Change Organization CDQ Processes and Data-driven
management for CDQ Methods business process
Standards & management
Guidelines
local global
Conceptual Software support
corporate data (e.g. MDM
model applications)
Data distribution Corporate Data Architecture System landscape
architecture analysis and
Authoritative data planning
sources
Applications for CDQ
System
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12. Achieved results provide a “tool box” for establishing
Corporate Data Quality Management
 EFQM Excellence Model for Corporate Data Quality Management
 Method for specifying business-relevant data quality metrics
 Reference model for Data Governance
 Method for establishing Data Governance
 Analysis and modeling method for integrating data quality in business process
management
 Method for master data integration
 Design patterns for data architecture
 Reference model for Master Data Quality Management software
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13. The CC CDQ research service portfolio rests on three
pillars
I II III
Research on Network &
Bilateral Project
Demand Benchmarking
 Full access to the CC  5 two-day consortium  Individual CDQ maturity
CDQ knowledge pool workshops p.a. assessment
 Customized research  In-depth benchmarking  Individual project results
studies groups (e.g. data governance
 Case studies within the  Moderation and co- design, metric design,
peer group ordination of peer group data architecture
 “Best practice” analysis)
 Analysis of the state of
presentations  Moderation of internal
the art in research and
 Access to a network of workshops
practice
CDQ professionals  Training and knowledge
 Active participation in  Access to highly-qualified transfer (in-house
leading edge research PhD students and seminars etc.)
 Leveraging a global graduate students  Individual support of CDQ
research network  Use of professional programs
platform (seminars,
lectures etc.)
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14. Table of Content
 Data Quality as a Success Factor for Business
 Competence Center Corporate Data Quality
 BEI Project References
 Team Overview
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15. BEI is a trusted partner for designing and implementing
Corporate Data Quality strategies
 Master data processes
Bühler AG
 Software evaluation
 Master data strategy
Drägerwerke AG & Co. KGaA  Data governance
 Implementation roadmap
Elektrizitätswerke des Kantons  Maturity assessment
Zürich  Data quality metrics
 Master data strategy
LIDL Stiftung & Co. KG  Data governance
 Implementation roadmap
OTTO Group  Master data strategy
 Conceptual data model
RWE IT GmbH
 Data architecture
Stadtwerke München
 Maturity assessment
SWM Services GmbH
 Maturity assessment
Swisscom IT Services AG
 Master data strategy
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16. Table of Content
 Data Quality as a Success Factor for Business
 Competence Center Corporate Data Quality
 BEI Project References
 Team Overview
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17. The combined team at IWI-HSG and BEI leverages
sound research and consulting expertise
IWI-HSG
Prof. Dr. Dr. Boris Otto Verena Ebner Clarissa Falge Ehsan Baghi
Hubert Österle
BEI
Dr. Dimitrios Dr. Kai Hüner Martin Ofner Andreas Max Wolfgang Peter Mayer*
Gizanis Reichert Zurkinden Dietrich
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18. Customers and partners benefit from an unmatched
pool of knowledge and expertise
850+ Contacts in the overall CC CDQ community
150+ Members in the XING Community
140+ Bilateral Project Workshops
70+ Best Practice Presentations
28 Consortium Workshops
22 Partner Companies
13 Scientific Researchers/PhD Students
1 Competence Center
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19. CC CDQ Resources on the Internet
Institute of Information Management at the University of St. Gallen
http://www.iwi.unisg.ch
Business Engineering Institute St. Gallen
http://www.bei-sg.ch
Competence Center Corporate Data Quality
http://cdq.iwi.unisg.ch
CC CDQ Benchmarking Platform
https://benchmarking.iwi.unisg.ch/
CC CDQ Community at XING
http://www.xing.com/net/cdqm
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20. Contact Details
Dr.-Ing. Boris Otto
University of St. Gallen
Institute of Information Management
Boris.Otto@unisg.ch
Tel.: +41 71 224 32 20
http://cdq.iwi.unisg.ch
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