This presentation gives an overview of the EFQM Execellence Model for Corporate Data Quality. The model supports the assessment of the maturity of enterprise-wide data quality management capabilities in multinational corporations. It was developed by the Competence Center Corporate Data Quality, a consortium research project at the University of St. Gallen, Switzerland. The presentation was given at the Business Academic Exchange workshop at the 17th Americas Conference on Information Systems (AMCIS 2011) in Detroit, MI.

1. EFQM Excellence Model for Corporate Data
Quality Management (CDQM)
Boris Otto
August 5th, 2011
Institute of Information Management
Chair of Prof. Dr. Hubert Österle

2. Table of Content
Business Rationale and Background
CDQM Excellence Model Overview
Application and Examples
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3. The quality of corporate data is necessary for various business drivers
Global Business  Implementation of a global ERP system
Process  „Single Point of Truth“
Harmonization  Standardization of processes, reports and KPIs
 Merger of several business units
Internal
 Creation of new business units
Reorganization
 „End-to-end“-Processes
 Economies of scale and scope, increased revenue or
Joint Ventures,
market share
Mergers, and
 Cross-selling and other synergies
Acquisition
 Taxation
 Online marketing strategy
Customer-centric
 360°-view on customers
Business Models
 Hybrid products
Regulatory  Import and export control
Compliance  SOX, REACH etc.
Legend: ERP – Enterprise Resource Planning; KPI – Key Performance Indicator; SOX – Sarbanes-Oxley Act, REACH – EU Regulation on
Registration, Evaluation, Authorisation and Restriction of Chemicals.
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4. Preventive Corporate Data Quality Management (CDQM) comprises six
design areas
Strategy 1
CDQ Strategy
Organization
2 CDQ Controlling
3 4
Processes and Methods
CDQ Organization for CDQ
5
lokal global
Corporate Data Architecture
6
Application Systems for CDQ
Systems
Legend: CDQ – Corporate Data Quality.
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5. Companies are confronted with a number of typical challenges
 What is the scope of CDQM in our company? How to approach the
establishment of CDQM?
 How can we measure progress and success?
 What can we learn from others?
Necessary is an instrument for assessing and improving the CDQM initiative
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6. The EFQM Excellence Model for CDQM was jointly developed by
EFQM, the University of St. Gallen, and partners from industry
& more.
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7. The case of an international communication systems manufacturer
Company’s Profile
 Manufacturer of fibre optic communications system solutions for voice, data
and video network applications
 10,000 employees worldwide
 Multi billion USD business
Initial situation
 Virtual data management organization established as a response to strategic
business requirements
 Challenges:
 Ownership of and responsibilities for data objects unclear
 Standards and common procedures for data quality missing
 Continuous organizational restructuring programs
Goal
 Maturity assessment for Corporate Data Quality Management and
development of an action plan
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8. The final results show the overall CDQM maturity of the case study
company
Strategy
Controlling
Applications
Organization
Data
Architecture Processes
& Methods
Legend: Current value 2010
Target value 2011 (= one maturity level for all enablers)
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9. All 31 goals were assessed in 25+ interviews using a standard, tool-
supported questionnaire
“CDQ Strategy” Results
Intended
Maturity Need for
Question Priority Im prove-
Evaluation action
m ent 2011
Are there any strategic objectives and values of master
1A data management in your organization (in a well- 0.32 4.50 0.62 0.15
documented and well-communicated form)?
Do the strategic objectives and values of master data
1B management comply with your company’s business 0.40 4.44 0.53 0.13
strategy?
Is there any strategic project planning or coordination of
1C initiatives for master data management in your 0.33 4.13 0.55 0.14
organization?
Does your organization provide the resources needed
1D for conducting master data management according to 0.36 4.46 0.56 0.14
given objectives and plans?
Are overall objectives and plans of master data
1E management broken down to objectives and plans 0.32 4.00 0.54 0.14
applicable on specific organizational levels?
Is your master data organization – i.e. DMO – staff
1F capable of naming current activities of master data 0.42 3.68 0.43 0.11
management?
Do top executives in your organization clearly show
1G their support for master data management by concrete 0.22 3.88 0.59 0.15
action or favorable statements?
Collected during interviews for Calculated for each question
each question
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10. In the case study, five strategic areas of action were identified as a result
of the maturity assessment
1
 Align CDQM with the company’s culture of quality management
Transferring TQM
principles to CDQM
 Proof of concept for customer master data creation in NAFTA
Customer master life cycle
2
 Corporate data as an asset: Business case calculation
Managing cost and
 Establish business-oriented data quality metrics
value of data quality
 Data life cycle: Retirement process
3
 Buy-in for CDQM from data owners still missing
Global data governance
 Continuous roll-out of roles and responsibilities
rollout
 Implementation of a shared corporate data management service
4
Global leveraging of  Knowledge capitalization on an organization and system level
knowledge assets  Foundation of a global center for excellence
5
System integration and
 Technical integration/substitution of application systems
supporting corporate data management
process automation
 Extend workflow from material master to other domains
Legend: TQM - Total Quality Management; CDQM – Corporate Data Quality Management.
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11. Contact Person
Prof. Dr. Boris Otto
University of St. Gallen
Institute of Information Management
E-mail: Boris.Otto@unisg.ch
Phone: +41 71 224 32 20
EFQM Excellence Model for CDQM
https://benchmarking.iwi.unisg.ch/
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12. Backup
General EFQM Model for Excellence
Overview of the EFQM Excellence Model for CDQM
Details of the EFQM Excellence Model for CDQM
Maturity levels
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13. The general EFQM Model for Excellence has been a proven instrument for
many years
Enabler criteria cover what an organization does. The Results criteria cover what an
organization achieves. Results are
caused by Enablers.
Enabler Results
People People Results
10% 10%
Processes, P Key
Customer
Leadership Strategy roducts, Servi Performance
Results
10% 10% ces Results
15%
10% 15%
Partnership &
Society Results
Resources
10%
10%
Innovation and Learning
Weightings are assigned to each Enablers are improved using
criteria and are used to determine feedback from Results and root-
the final score. cause analysis.
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14. The EFQM Excellence Model for CDQM combines an accepted standard
with the expertise from industry
Enabler criteria cover what an organization does The Results criteria cover what an
in terms of CDQM. organization achieves in terms of CDQM.
Results are caused by Enablers.
Enabler Results
Strategy
People Results
Controlling
Key
Customer
Organization Processes and Methods Performance
Results
Results
Data Architecture
Society Results
Applications
Innovation and Learning
Enablers are improved using
CDQM design areas. feedback from Results and root-
cause analysis.
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15. The EFQM Excellence Model for CDQM provides detailed guidance for all
six enablers
1A. Strategy for data quality management is
developed, reviewed and updated based on the
Goal organization’s business strategy
 Determining, analyzing, documenting and
communicating the impact of data quality on
business objectives and operational excellence
 Formalizing, reviewing and updating
strategy, objectives and processes for data
Guidance
quality management which meet stakeholders’
points
need and expectations and which are aligned
with the business strategy
 …
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16. Five maturity levels allow for detailed assessments
Level Description
V.
 Excellent results in all areas
Fully
 Outstanding solution found; no significant further improvement imaginable
completed
IV.  Clear proof of successful implementation
Major progress  Regular verifications and substantial improvement
made  But approach is still not fully applied in all areas
 Proof that initiative is seriously established
III.
 Successful implementation in a number of areas
Substantial
 A number of examples of verification and improvement identifiable, but the full
progress made
potential is by far not fully exploited yet
II.  Some indications of a positive development identifiable
Minor progress  Casual, more accidental verifications that have led to some improvement
made  Positive results in very specific areas
I.  No initiative identifiable
Not yet started  Some good ideas expressed, but still wishful thinking is predominant
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