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Corporate Data Quality
Research and Services Overview



Prof. Dr. Boris Otto, Assistant Professor
St. Gallen, March 2012

Chair of Prof. Dr. Hubert Österle
Competence Area Corporate Data Quality




       Competence Center                                        Business Engineering
       Corporate Data Quality                                   Institute St. Gallen AG




       Applied Consortium Research                                   Business Value Transformation




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 2
Table of Content

     Data Quality as a Success Factor for Business
     Competence Center Corporate Data Quality
     BEI Project References
     Team Overview




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 3
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




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 4
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




   © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 5
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.)


© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 6
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


© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 7
Table of Content

     Data Quality as a Success Factor for Business
     Competence Center Corporate Data Quality
     BEI Project References
     Team Overview




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 8
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)?




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 9
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.


© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 10
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


© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 11
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




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 12
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.)




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 13
Table of Content

     Data Quality as a Success Factor for Business
     Competence Center Corporate Data Quality
     BEI Project References
     Team Overview




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 14
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

© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 15
Table of Content

     Data Quality as a Success Factor for Business
     Competence Center Corporate Data Quality
     BEI Project References
     Team Overview




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 16
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




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 17
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


© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 18
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




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 19
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




© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 20

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Corporate Data Quality: Research and Services Overview

  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 2
  • 3. Table of Content  Data Quality as a Success Factor for Business  Competence Center Corporate Data Quality  BEI Project References  Team Overview © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 3
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 4
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 5
  • 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.) © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 6
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 7
  • 8. Table of Content  Data Quality as a Success Factor for Business  Competence Center Corporate Data Quality  BEI Project References  Team Overview © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 8
  • 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)? © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 9
  • 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. © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 10
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 11
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 12
  • 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.) © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 13
  • 14. Table of Content  Data Quality as a Success Factor for Business  Competence Center Corporate Data Quality  BEI Project References  Team Overview © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 14
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 15
  • 16. Table of Content  Data Quality as a Success Factor for Business  Competence Center Corporate Data Quality  BEI Project References  Team Overview © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 16
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 17
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 18
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 19
  • 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 © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 20