EFQM Excellence Model for Corporate Data Quality Management (CDQM)


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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.

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EFQM Excellence Model for Corporate Data Quality Management (CDQM)

  1. 1. EFQM Excellence Model for Corporate DataQuality Management (CDQM)Boris OttoAugust 5th, 2011Institute of Information ManagementChair of Prof. Dr. Hubert Österle
  2. 2. Table of Content Business Rationale and Background CDQM Excellence Model Overview Application and Examples © CC CDQ – August 5th, 2011, B. Otto / 2
  3. 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 onRegistration, Evaluation, Authorisation and Restriction of Chemicals. © CC CDQ – August 5th, 2011, B. Otto / 3
  4. 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 SystemsLegend: CDQ – Corporate Data Quality. © CC CDQ – August 5th, 2011, B. Otto / 4
  5. 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 © CC CDQ – August 5th, 2011, B. Otto / 5
  6. 6. The EFQM Excellence Model for CDQM was jointly developed byEFQM, the University of St. Gallen, and partners from industry & more. © CC CDQ – August 5th, 2011, B. Otto / 6
  7. 7. The case of an international communication systems manufacturerCompany’s Profile Manufacturer of fibre optic communications system solutions for voice, data and video network applications 10,000 employees worldwide Multi billion USD businessInitial 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 programsGoal Maturity assessment for Corporate Data Quality Management and development of an action plan © CC CDQ – August 5th, 2011, B. Otto / 7
  8. 8. The final results show the overall CDQM maturity of the case studycompany Strategy Controlling Applications Organization Data Architecture Processes & Methods Legend: Current value 2010 Target value 2011 (= one maturity level for all enablers) © CC CDQ – August 5th, 2011, B. Otto / 8
  9. 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 master1A 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 data1B management comply with your company’s business 0.40 4.44 0.53 0.13 strategy? Is there any strategic project planning or coordination of1C initiatives for master data management in your 0.33 4.13 0.55 0.14 organization? Does your organization provide the resources needed1D 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 data1E 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 – staff1F capable of naming current activities of master data 0.42 3.68 0.43 0.11 management? Do top executives in your organization clearly show1G 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 © CC CDQ – August 5th, 2011, B. Otto / 9
  10. 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 domainsLegend: TQM - Total Quality Management; CDQM – Corporate Data Quality Management. © CC CDQ – August 5th, 2011, B. Otto / 10
  11. 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/ © CC CDQ – August 5th, 2011, B. Otto / 11
  12. 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 © CC CDQ – August 5th, 2011, B. Otto / 12
  13. 13. The general EFQM Model for Excellence has been a proven instrument formany 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. © CC CDQ – August 5th, 2011, B. Otto / 13
  14. 14. The EFQM Excellence Model for CDQM combines an accepted standardwith 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. © CC CDQ – August 5th, 2011, B. Otto / 14
  15. 15. The EFQM Excellence Model for CDQM provides detailed guidance for allsix 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  … © CC CDQ – August 5th, 2011, B. Otto / 15
  16. 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 implementationMajor 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 fullprogress made potential is by far not fully exploited yet II.  Some indications of a positive development identifiableMinor progress  Casual, more accidental verifications that have led to some improvement made  Positive results in very specific areas I.  No initiative identifiableNot yet started  Some good ideas expressed, but still wishful thinking is predominant © CC CDQ – August 5th, 2011, B. Otto / 16