This presentation shows best practices in establishing and sustaining enterprise-wide data quality management.

1. Managing Enterprise Data as an Asset
Best Practices in Establishing and Sustaining Enterprise-Wide Data
Quality Management
Prof. Dr. Boris Otto, Assistant Professor
Munich, May 23, 2012
Chair of Prof. Dr. Hubert Österle

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Agenda
1. The Enterprise Data Challenge
2. Enterprise-Wide Data Quality Management
3. «Best Practices»

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Agenda
1. The Enterprise Data Challenge
2. Enterprise-Wide Data Quality Management
3. «Best Practices»

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In many large enterprises no unambiguous understanding of key business
objects exists
Research &
Development
Logistics &
Distribution
9 x 4 x 2 cm3
10 g
The product in the real world…
… and perceptions in …
Sales
Environment, Health
& Safety

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The ambiguity causes synonyms and duplicate records on business
process and IT level, thus, poor data quality
Real-World Object
Business View
(Business Objects)
Information
Management View
(Information Objects)
IT View
(Data Objects)
000004711 SUP00800
Becker AG B. BECKER GMBH …
…

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Today, many companies manage data quality reactively, i.e. in a «fire
fighting» mode
Legend: Data quality
“Submarines” (e.g. migrations,
process errors, irregularities in
management reporting).
Data Quality
Time
Project 1 Project 2 Project 3
 No risk mitigation
 No chance to plan and to control budgets and resources
 No target values for corporate data quality
 No sustainable data quality
 High recurring project costs (change requests, external consultants etc.)

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Root causes for poor data quality are manifold as the case of Bayer
CropScience shows
Low / Not sustainable
Data Quality
People Data Maintenance
Standards Organization
No sufficient
training and / or
education
Data Quality KPIs
are not part of
personal objectives
Heterogeneous set
of data maintenance
tools
Master Data not
protected in all
operational systems
Too many rules,
even more exceptions
No globally accepted
set of rules, standards,
policies, guidelines
Gaps in business
responsibility for
Master Data objects
No empowered
Data Governance
organization
Data Quality Processes
Only very few
Data Quality KPIs
defined
No continuous
monitoring of
Data Quality
Maintenance processes
are not fully supported
by existing toolset
Master Data
maintenance processes
not globally harmonized
and optimized
People Data Maintenance
Data Quality Process Standards Organization
Poor Data
Quality
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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Agenda
1. The Enterprise Data Challenge
2. Enterprise-Wide Data Quality Management
3. «Best Practices»

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Enterprise data quality is a prerequisite for strategic business goals
Anti-counterfeiting
Company-wide batch management harmonization
Compliance
§
Operational excellence through efficient “data supply chains”
Increased inventory visibility and improved planning
Lean Supply Chain
Management€
Enhanced decision-marking procedures
“Single version of the Truth”
Business Intelligence
Effectivenessi
Improved spend analyses
Effective supplier development and management
Strategic Purchasing€
Central master data services allow for IT consolidationIT Cost Reduction
€

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Thus, enterprise data quality management must be organized according to
a set of design principles
 Accountable
 Comprehensive
 Lean
 Measurable
 Preventive
 Sustainable

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Corporate Data Quality Management (CDQM) is a Business Engineering task on a
company’s business strategy, organization, and information systems level
Strategy
Organization
System
CDQ Controlling
Applications for CDQM
Corporate Data Architecture
Organization
for CDQM
CDQM Processes and
Methods
Strategy for CDQM
local global
Mandate
Strategy document
Value management
Action plan
Goals and targets
Data quality metrics
Data Governance
Roles and
responsibilities
Change
management
Standards &
Guidelines
Data life cycle
management
Business metadata
management
Data-driven business
process management
Conceptual
corporate data
model
Data distribution
architecture
Authoritative data
sources
Software support (e.g.
MDM applications)
System landscape
analysis and planning

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The EFQM Excellence Model for CDQM1 was collaboratively developed by EFQM,
the University of St. Gallen, and partners from industry
Legend: Current value 2010
Target value 2011 (= one maturity level for all enablers)
Strategy
Controlling
Organization
Processes
& Methods
Data
Architecture
Applications
CDQM Maturity Assessment
1) EFQM: EFQM Framework for Corporate Data Quality Management: Assessing the Organization’s Data Quality Management Capabilities, EFQM Press, Brussels, 2012.
EFQM Framework Corporate Data
Quality Management

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Agenda
1. The Enterprise Data Challenge
2. Enterprise-Wide Data Quality Management
3. «Best Practices»

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Material master data quality has continuously been improved at Bayer
CropScience

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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 each 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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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
SERVICE GMBH
MIGROS-GENOSSENSCHAFTS-BUND NESTLÉ SA NOVARTIS PHARMA AG
ROBERT BOSCH GMBH SAP AG
SIEMENS ENTERPRISE
COMMUNICATIONS GMBH & CO. KG
SYNGENTA CROP PROTECTION AG
TELEKOM DEUTSCHLAND GMBH ZF FRIEDRICHSHAFEN AG NB: Overview comprises both current and past research partner companies.

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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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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
Please reach out to me in case of questions and comments