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Data Governance Best Practices

This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.

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One Size Does Not Fit All:
Best Practices for Data Governance

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

University of St. Gallen, Institute of Information Management
Chair of Prof. Dr. Hubert Österle
Agenda




1. Business Rationale for Data Governance

2. Data Governance Design Options

3. Best Practice Cases

4. Competence Center Corporate Data Quality




                           St. Gallen, Switzerland, March 2012, B. Otto / 2
Agenda




1. Business Rationale for Data Governance

2. Data Governance Design Options

3. Best Practice Cases

4. Competence Center Corporate Data Quality




                           St. Gallen, Switzerland, March 2012, B. Otto / 3
Data Governance is necessary in order to meet several strategic business
requirements


    Compliance with regulations and contractual obligations

    Integrated customer management (“360 degree view”)

    Company-wide reporting needs (“Single Source of the Truth”)

    Business integration

    Global business process harmonization




                            St. Gallen, Switzerland, March 2012, B. Otto / 4
The typical evolution of data quality over time in companies shows a
strong need for action

     Data Quality




                                                                                                      Legend:        Data quality pitfalls
                                                                                                      (e. g. Migrations, Process
                                                                                                      Touch Points, Poor
                                                                                                      Management Reporting Data.

                                                                                                  Time
                     Project 1             Project 2                 Project 3



      No risk management possible
      Impedes planning and controlling of budgets and resources
      No targets for data quality
      Purely reactive - when too late
      No sustainability, high repetitive project costs (change requests, external consulting etc.)




                                         St. Gallen, Switzerland, March 2012, B. Otto / 5
Data Governance and Data Quality Management are closely interrelated




          Maximize                                                                        is sub-goal of      Maximize
          Data Value                                                                                         Data Quality


                   supports                                                                                         supports

                                         is led by                                      is sub-function
             Data                                            Data                                            Data Quality
          Governance                                      Management                                    of   Management


                                                                     are object of


                                  are object of                                    are object of
                                                          Data Assets




Legend:     Goal      Function   Data.




                                                     St. Gallen, Switzerland, March 2012, B. Otto / 6

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Data Governance Best Practices

  • 1. One Size Does Not Fit All: Best Practices for Data Governance Prof. Dr. Boris Otto, Assistant Professor St. Gallen, Switzerland, March 2012 University of St. Gallen, Institute of Information Management Chair of Prof. Dr. Hubert Österle
  • 2. Agenda 1. Business Rationale for Data Governance 2. Data Governance Design Options 3. Best Practice Cases 4. Competence Center Corporate Data Quality St. Gallen, Switzerland, March 2012, B. Otto / 2
  • 3. Agenda 1. Business Rationale for Data Governance 2. Data Governance Design Options 3. Best Practice Cases 4. Competence Center Corporate Data Quality St. Gallen, Switzerland, March 2012, B. Otto / 3
  • 4. Data Governance is necessary in order to meet several strategic business requirements Compliance with regulations and contractual obligations Integrated customer management (“360 degree view”) Company-wide reporting needs (“Single Source of the Truth”) Business integration Global business process harmonization St. Gallen, Switzerland, March 2012, B. Otto / 4
  • 5. The typical evolution of data quality over time in companies shows a strong need for action Data Quality Legend: Data quality pitfalls (e. g. Migrations, Process Touch Points, Poor Management Reporting Data. Time Project 1 Project 2 Project 3  No risk management possible  Impedes planning and controlling of budgets and resources  No targets for data quality  Purely reactive - when too late  No sustainability, high repetitive project costs (change requests, external consulting etc.) St. Gallen, Switzerland, March 2012, B. Otto / 5
  • 6. Data Governance and Data Quality Management are closely interrelated Maximize is sub-goal of Maximize Data Value Data Quality supports supports is led by is sub-function Data Data Data Quality Governance Management of Management are object of are object of are object of Data Assets Legend: Goal Function Data. St. Gallen, Switzerland, March 2012, B. Otto / 6
  • 7. Data Governance is also about cost trade-off’s Costs Total Costs ΔC Costs of Poor Data Quality DQM Costs ΔDQ Data Quality St. Gallen, Switzerland, March 2012, B. Otto / 7
  • 8. Without Data Governance companies are missing direction with regard to their data assets Source: Strassmann, P.: The Politics of Information Management, The Information Economics Press, New Canaan, CT, 1995. St. Gallen, Switzerland, March 2012, B. Otto / 8
  • 9. Agenda 1. Business Rationale for Data Governance 2. Data Governance Design Options 3. Best Practice Cases 4. Competence Center Corporate Data Quality St. Gallen, Switzerland, March 2012, B. Otto / 9
  • 10. As Data Governance is an organizational task, design decisions must be made in five organizational areas Data Governance Organization Organizational Goals Organizational Structure Functional Locus of Organizational Roles & Formal Goals Goals Control Form Committees Source: Otto, B.: A Morphology of the Organisation of Data Governance, Proceedings of the 19th European Conference on Information Systems, Helsinki, Finland, 11.06.2011, 2011. St. Gallen, Switzerland, March 2012, B. Otto / 10
  • 11. Six cases from global companies are used to illustrate the different design options Case A B C D E F Industry Chemicals Automotive Mfg. Telecom Chemicals Automotive Headquarter Germany Germany USA Germany Switzerland Germany Revenue 2009 [million €] 6,510 38,174 4,100 64,600 8,354 9,400 Staff 2009 [1,000] 18,700 275,000 23,500 260,000 25,000 60,000 Role of main contact person Head of Program Head of Data Head of Data Head of Project for the case study Enterprise Manager Governance Governance MDM SSC Manager MDM MDM MDM Key: MDM - Master Data Management, Mfg. - Manufacturing; SSC - Shared Service Center. NB: All case study companies are research partner companies in the Competence Center Corporate Data Quality (CC CDQ). St. Gallen, Switzerland, March 2012, B. Otto / 11
  • 12. Data Governance design options can be broken down into 28 individual items Data Governance Organization Data Governance Goals Data Governance Structure Formal Goals Locus of Control Business Goals Functional Positioning • Ensure compliance • Business department • Enable decision-making • IS/IT department • Improve customer satisfaction • Increase operational efficiency Hierarchical Positioning • Support business integration • Executive management IS/IT-related Goals • Middle management • Increase data quality Organizational Form • Support IS integration (e.g. migrations) • Centralized Functional Goals • Decentralized/local • Project organization • Create data strategy and policies • Virtual organization • Establish data quality controlling • Shared service • Establish data stewardship • Implement data standards and Roles and Committees metadata management • Sponsor • Establish data life-cycle management • Data governance council • Establish data architecture • Data owner management • Lead data steward • Business data steward • Technical data steward St. Gallen, Switzerland, March 2012, B. Otto / 12
  • 13. For example, the design area “Roles & Committees” comprises six individual roles Sponsor Data Data Owner Governance Council Lead Data Steward Business Data Technical Data Steward Steward Legend: Disciplinary reporting line (“solid”); Functional reporting line (“dotted”); is part of. Business IT Data Team. Single role Composite role. St. Gallen, Switzerland, March 2012, B. Otto / 13
  • 14. The cases show a variety of different Data Governance designs Data Governance Goals Data Governance Structure Case Formal goals Functional goals Locus of control Org. form Roles, committees A No formal quantified DQ, data lifecycle, data arch., Business (IM and Central MDM dept., MDM council, data goals; DQ index and software tools, training SCM), 3rd level virtual global owners, lead steward, data lifecycle time organisation technical steward measured B No formal quantified Business: Data definitions, Business (corporate Central project Steering committee, goals ownership, data lifecycle, data accounting), 3rd level organisation, virtual master data owner, arch.; IS/IT: Data models, IT organisation master data officer arch., projects, DQ C No formal quantified Data ownership, data lifecycle, Business (shared Central data DG manager, DQ goals, data lifecycle DQ, service level service centre), 4th management org.; manager, data owner, time measured, SLAs management, project support level virtual global data stewardship with internal customers organisation manager, data steward; planned no committee D Alignment with DQ standards and rules, data Hybrid (both central IT Central organization, “Data responsible”, data business strategic quality measuring, ownership, and business), 3rd and supported by projects architect, data manager, goals, no quantification data models and arch., audits 4th level DQ manager, no committee E Alignment with Data strategy, rules and Business (shared Shared service Head of MDM, data business drivers, standards, ownership, DQ service centre), 4th owners, lead stewards formalisation through assurance, data & system level (per domain), regional SLAs arch. MDM heads, data architect; no committee F No formal quantified MDM strategy, monitoring, IS/IT, 3rd level Central organisation, Head of MDM, data goals organisation, processes, and supported by projects owners, DG council, data arch., system arch., data architect application dev. Key: DG - Data governance; Org. - Organisational; DQ - Data quality; arch. - architecture; IM - Information Management; SCM - Supply Chain Management; MDM - Master Data Management, dept. - department; IS - Information Systems; IT - Information Technology; SLA - Service Level Agreement. St. Gallen, Switzerland, March 2012, B. Otto / 14
  • 15. Agenda 1. Business Rationale for Data Governance 2. Data Governance Design Options 3. Best Practice Cases 4. Competence Center Corporate Data Quality St. Gallen, Switzerland, March 2012, B. Otto / 15
  • 16. In Case A data quality is measured on a continuous basis Overall data quality indices per region and per country are published on the corporate intranet. Regions and countries can monitor their own progress (as well as the progress of best-in- class countries) Measurement and data quality indices are made transparent to everybody. Calculation of indices can be track down to the individual record level. Chemical Industry St. Gallen, Switzerland, March 2012, B. Otto / 16
  • 17. Data Governance in Case B is well-balanced between IT and business functions as well as between corporate and business units Executive Management report corporate sector/ corporate department Overall responsibility (MD Owner, IT, ..) Interdisciplinary for a master data class Master Data Master Data Master Data Management (specialist/organizational Owner A Owner X Steering Committee level) working group / Responsibility Master Data Master Data competence team in relevant units (data Officer Officer maintenance/ application) … … Governance Governance Function Function IT Projects Concepts Concepts IT platforms, IT target systems Master data Master data class 1 … class N e. g. Supplier master data Chart of accounts Automotive Industry St. Gallen, Switzerland, March 2012, B. Otto / 17
  • 18. Case D is an example of a formalized Data Governance organization with hybrid location of responsibilities Deutsche Telekom AG T-Home T-Mobile T-Systems MQM Line of Marketing and … Business CIO Quality Mngmt. MQM2 IT1 IT2 Quality IT Strategy and Enterprise IT … Management Quality Architecture MQM27 ZIT7 Data Quality … Information … Management Processing IT73/74 ZIT72 … Data … MDM Management ZIT721 ZIT722 Data DQ Measurement Governance and Assurance Telecom Industry St. Gallen, Switzerland, March 2012, B. Otto / 18
  • 19. In Case E Master Data Management is organized as a shared service and operated as a “data factory” Ensures that the quality of data objects supports the dependent business processes Data Governance Creates, changes and retires a data object Data Ensures that the Data Quality MDM Lifecycle MDM agenda can Assurance Organisation Management be driven across the enterprise Data & System Architecture Enables a single view on each master data class Chemical Industry St. Gallen, Switzerland, March 2012, B. Otto / 19
  • 20. Case F is an example for locating the Data Management Organization within the IS/IT function Process Management Board CFO Harmonization Board Corporate Divisions Process Corporate IT Mgmt. Business Business IT Architecture Division Management Process Archi- & SCO Committee tecture Mgmt. IT Org. Consulting Project Information Corporate IT Competence Master Data Portfolio and Application Departments Center Management Mgmt. Integration Corporate Advanced Applications Development Key: Recently established. Automotive Industry St. Gallen, Switzerland, March 2012, B. Otto / 20
  • 21. Some key success factors become apparent when analyzing the cases Demonstrate staying power! Data Governance is a change issue and requires involvement of all stakeholders. No bureaucracy! Use existing board structures and processes. No ivory tower, no silver bullet! Use “real-life” examples to get buy in from local business units. St. Gallen, Switzerland, March 2012, B. Otto / 21
  • 22. Agenda 1. Business Rationale for Data Governance 2. Data Governance Design Options 3. Best Practice Cases 4. Competence Center Corporate Data Quality St. Gallen, Switzerland, March 2012, B. Otto / 22
  • 23. The Competence Center Corporate Data Quality comprises 22 partner companies1 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. St. Gallen, Switzerland, March 2012, B. Otto / 23
  • 24. The Competence Center Corporate Data Quality channels the knowledge and experience of a large network of practitioners and researchers 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 St. Gallen, Switzerland, March 2012, B. Otto / 24
  • 25. Life is good with Data Governance… Source: Strassmann, P.: The Politics of Information Management, The Information Economics Press, New Canaan, CT, 1995. St. Gallen, Switzerland, March 2012, B. Otto / 25
  • 26. 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 St. Gallen, Switzerland, March 2012, B. Otto / 26
  • 27. Contact Prof. Dr. Boris Otto University of St. Gallen, Institute of Information Management Tuck School of Business at Dartmouth College Boris.Otto@unisg.ch Boris.Otto@tuck.dartmouth.edu +1 603 646 8991 St. Gallen, Switzerland, March 2012, B. Otto / 27