1. Data Quality as a Business Success Factor
Prof. Dr. Boris Otto, Assistant Professor
Enschede, April 5, 2012
Chair of Prof. Dr. Hubert Österle

2. Case A looks at one of the business drivers of data quality at leading automotive
supplier ZF Friedrichhafen AG
«Starting in January 2010, the Services business unit will additionally
pool the global customer service activities of the Group. In doing so,
the Services departments at German division and business unit
locations will be organizationally merged with the worldwide Services
companies. With this new structure, ZF has established a systematic
approach in the after-sales market.»
ZF Friedrichshafen AG: Annual Report 2009, p. 64.
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3. At ZF OEM1 Relationship Management requires consistent and accurate master
data about vehicles, customers, products across the organization
Real world
view
Sales,
Business
Engineering Projects Logistics,
process view
Controlling
Application
System View
Axalant SAP cProjects SAP ERP
VW Group Audi AUDI AG
Data View
B8 AU416 PL48
1) OEM - Original Equipment Manufacturer.
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4. Data quality is necessary to respond to strategic business requirements
1 Customer-Centric Business Models
$ Value Chain Excellence
§ Contractual and Regulatory Compliance
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5. The typical evolution of data quality over time does not live up to its business
relevance
Data Quality
Legend: Data quality
“Submarines” (e.g. migrations,
process errors, irregularities in
management reporting).
Time
Project 1 Project 2 Project 3
 No risk management possible
 No chance to plan and to control budgets and resources
 No target values for corporate data quality
 No sustainability
 High recurring project costs (change requests, external consultants etc.)
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6. Case B analyzes root causes of poor data quality at Bayer CropScience
People
People Data Maintenance
Data Maintenance
Maintenance processes
are not fully supported
No sufficient by existing toolset
training and / or Heterogeneous set
education of data maintenance
tools Master Data
maintenance processes
Data Quality KPIs not globally harmonized
Master Data not
are not part of and optimized
protected in all
personal objectives operational systems
Low / Not sustainable
Poor Data
Data Quality
Quality
Only very few No globally accepted No empowered
Data Quality KPIs set of rules, standards, Data Governance
defined policies, guidelines organization
No continuous Gaps in business
Too many rules,
monitoring of responsibility for
even more exceptions
Data Quality Master Data objects
Data Quality Processes
Data Quality Process Standards
Standards Organization
Organization
Legend: KPI - Key Performance Indicator.
Source: Brauer, B. (2009). Master Data Quality Cockpit at Bayer CropScience. Paper presented at the 4th Workshop of the Competence Center Corporate Data Quality 2,
Lucerne.
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7. Corporate Data Quality Management (CDQM) is a Business Engineering task and
relates to a company’s business strategy, organization, and information systems
Mandate Strategy
Strategy document Goals and targets
Strategy for CDQM
Value management Data quality metrics
Action plan
Organization
CDQ Controlling
Data Governance Data life cycle
management
Roles and
responsibilities Business metadata
management
Change Organization CDQM Processes and
management Data-driven business
for CDQM Methods process management
Standards &
Guidelines
local global
Conceptual
corporate data Software support (e.g.
model MDM applications)
Data distribution Corporate Data Architecture System landscape
architecture analysis and planning
Authoritative data
sources
Applications for CDQM
System
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8. The EFQM Excellence Model for CDQM1 was collaboratively developed by EFQM,
the University of St. Gallen, and partners from industry
CDQM Maturity Assessment
Strategy
Controlling
Applications
Organization
Data
Architecture Processes
& Methods
Legend: Current value 2010
Target value 2011 (= one maturity level for all enablers)
1) EFQM: EFQM Framework for Corporate Data Quality Management: Assessing the Organization’s Data Quality Management Capabilities, EFQM Press, Brussels, 2011
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9. The Competence Center Corporate Data Quality (CC CDQ) is a consortium
research project involving 22 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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10. Material master data quality has continuously been improved at Bayer
CropScience (Case B)
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11. Data quality leads to tangible business benefits
Savings of 2 percent of average inventory value p.a.1
More than GBP 500 million saved through retrieval of
«lost assets»2
CHF 3,000 saved per obsolete master data record3
1) Benefit assessment as a result from a series of expert interviews at one of the CC CDQ partner companies.
2) Otto, B.; Weber, K.: From Health Checks to the Seven Sisters: The Data Quality Journey at BT, University of St. Gallen, Institute of Information Management, St.
Gallen, 2009.
3) Lay, J. (2008). Produktdaten im ERP. Paper presented at the Stammdatenmanagement-Forum 2008, Rapperswil.
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12. 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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13. Please reach out to me in case of questions and comments
Prof. Dr. Boris Otto
Assistant Professor & Head of CC CDQ
University of St. Gallen
Institute of Information Management
Switzerland
+41 71 224 32 20
boris.otto@unisg.ch
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