Transcription
One Size Does Not Fit All:Best Practices for Data GovernanceBoris OttoMinneapolis MNMinneapolis,MN, September 2626, 2011University of St. Gallen, Institute of Information ManagementTuck School of Business at Dartmouth College
Agenda1 Business1.B iRRationaletil ffor DDatat GGovernance2. Data Governance Designg Optionsp3. Best Practice Cases4. Competence Center Corporate Data QualityMinneapolis, MN, 09/26/11, B. Otto / 2
Agenda1 Business1.B iRationaleR til forf DataD t GGovernance2. Data Governance Designg Optionsp3. Best Practice Cases4. Competence Center Corporate Data QualityMinneapolis, MN, 09/26/11, B. Otto / 3
Data Governance is necessary in order to meet several strategic businessrequirementsCompliance with regulations and contractual obligationsIntegrated customer management (“360 degree view”)Company-wide reporting needs (“Single Source of the Truth”)Business integrationGlobal business process harmonizationMinneapolis, MN, 09/26/11, B. Otto / 4
The typical evolution of data quality over time in companies shows astrong need for actionData QualityLegend:Data quality pitfalls((e. g.g Migrations,g, ProcessTouch Points, PoorManagement Reporting Data.TimeProject 1 Project 2Project 3No risk management possibleImpedes planning and controlling of budgets and resourcesN ttargetsNot forf datad t qualitylitPurely reactive - when too lateNo sustainability, high repetitive project costs (change requests, external consulting etc.)Minneapolis, MN, 09/26/11, B. Otto / 5
Data Governance and Data Quality Management are closely interrelatedis sub-goal ofMaximizeData Valuesupportssupportsis led byDataDGovernanceis sub-functionDataManagementofare object ofare object ofare object ofData AssetsLegend:GoalFunctionMaximizeData QualityData.Minneapolis, MN, 09/26/11, B. Otto / 6Data QDQualityliManagement
Data Governance is also about cost trade-off’sCostsTotal CostsΔCC t off PCostsPoor DDatatQualityDQM CostsΔDQMinneapolis, MN, 09/26/11, B. Otto / 7Data Quality
Without Data Governance companies are missing direction with regard totheir data assetsSource: Strassmann, 1995.Minneapolis, MN, 09/26/11, B. Otto / 8
Agenda1 Business1.B iRRationaletil ffor DDatat GGovernance2. Data Governance Designg Optionsp3. Best Practice Cases4. Competence Center Corporate Data QualityMinneapolis, MN, 09/26/11, B. Otto / 9
As Data Governance is an organizational task, design decisions must bemade in five organizational areasDataDt GGovernanceOrganizationOrganizational GoalsFormal GoalsFunctionalGoalsOrganizational StructureLocus ofControlOrganizationalFormSource: Otto, 2011.Minneapolis, MN, 09/26/11, B. Otto / 10Roles &Committees
Six cases from global companies are used to illustrate the different rmanyUSAGermanySwitzerlandGermanyRevenue 2009 [million ]6,51038,1744,10064,6008,3549,400Staff 2009Sta009 [[1,000],000]18,7008, 0,000Head ofEnterpriseMDMProgramManagerMDMHead of DataGovernanceHead of DataGovernanceHead ofMDM SSCProjectManagerMDMRole of main contact personfor the case studyKey: 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).Minneapolis, MN, 09/26/11, B. Otto / 11
Data Governance design options can be broken down into 28 individualitemsData Governance OrganizationData Governance GoalsData Governance StructureFormal GoalsLocus of ControlB iBusinessGGoalslFFunctionaltilPPositioningiti i Ensure complianceEnable decision-makingImprove customer satisfactionIncrease operational efficiencySupport business integrationHierarchical Positioning IS/IT-related Goals qualityyIncrease data qSupport IS integration (e.g. migrations)Functional Goals Create data strategy and policiesE bli h dEstablishdata qualityli controllinglliEstablish data stewardshipImplement data standards andmetadata managementEstablish data life-cycle managementE bli h dEstablishdata architecturehimanagementBusiness departmentIS/IT departmentExecutive managementMiddle managementOrganizational Form CentralizedDecentralized/localProject organizationVirtual organizationSh d serviceSharediRoles and Committees Sponsorgovernance councilData gData ownerLead data stewardBusiness data stewardTechnical data stewardMinneapolis, MN, 09/26/11, B. Otto / 12
For example, the design area “Roles & Committees” comprises sixindividual rolesSponsorData OwnerDataGovernanceCouncilLead DataStewardBusiness DataStewardLegend:Disciplinary reporting line (“solid”);Functional reporting line (“dotted”);BusinessITData Team.Single roleComposite role.Technical DataStewardis part of.Minneapolis, MN, 09/26/11, B. Otto / 13
The cases show a variety of different Data Governance designsData Governance GoalsCaseFormal goalsData Governance StructureFunctional goalsLocus of controlOrg. formRoles, committeesANo formal quantifiedgoals;l DQ iindexd andddata lifecycle timemeasuredDQ, data lifecycle, data arch.,fttools,t l trainingt i isoftwareBusiness (IM andSCM) 3rd levelSCM),llCentral MDM dept.,virtuali t l globall b lorganisationMDM council, dataowners, leadl d steward,tdtechnical stewardBNo formal quantifiedggoalsBusiness: Data definitions,ownership,p, data lifecycle,y , dataarch.; IS/IT: Data models, ITarch., projects, DQBusiness (corporateaccounting),g), 3rd levelCentral projectorganisation,g, virtualorganisationSteering committee,master data owner,,master data officerCNo formal quantifiedgoals data lifecyclegoals,time measured, SLAswith internal customersplannedData ownership, data lifecycle,DQ service levelDQ,management, project supportBusiness (sharedservice centre)centre), 4thlevelCentral datamanagement orgorg.;;virtual globalorganisationDG manager, DQmanager data ownermanager,owner,data stewardshipmanager, data steward;no committeeDAlignment withbusiness strategicgoals, no quantificationDQ standards and rules, dataquality measuring, ownership,data models and arch., auditsHybrid (both central ITand business),) 3rdd and4th levelCentral organization,supported by projects“Data responsible”, dataarchitect, data manager,DQ manager, nocommitteeEAlignment withbusiness drivers,formalisation throughSLAsData strategy, rules andstandards, ownership, DQassurance, data & systemarch.Business (sharedservice centre), 4thlevelShared serviceHead of MDM, dataowners, lead stewards(per domain), regionalMDM heads, dataarchitect; no committeeFNo formal quantifiedgoalslMDM strategy, monitoring,organisation,i tiprocesses, andddata arch., system arch.,application dev.IS/IT, 3rd levelCentral organisation,t d byb projectsj tsupportedHead of MDM, dataowners, DG council,ildata architectKey: DG - Data governance; Org. - Organisational; DQ - Data quality; arch. - architecture; IM - Information Management; SCM - Supply Chain Management; MDM - Master DataManagement, dept. - department; IS - Information Systems; IT - Information Technology; SLA - Service Level Agreement.Minneapolis, MN, 09/26/11, B. Otto / 14
Agenda1 Business1.B iRRationaletil ffor DDatat GGovernance2. Data Governance Designg Optionsp3. Best Practice Cases4. Competence Center Corporate Data QualityMinneapolis, MN, 09/26/11, B. Otto / 15
In Case A data quality is measured on a continuous basisOverall data quality indices perregiongand pper countryy arepublished on the corporateintranet.Regions and countries canmonitor their own progress (aswell as the progress of best-inclass countries)Measurementt andMd datad t qualitylitindices are made transparent toeverybody.Calculation of indices can betrack down to the individualrecord level.Chemical IndustryMinneapolis, MN, 09/26/11, B. Otto / 16
Data Governance in Case B is well-balanced between IT and businessfunctions as well as between corporate and business unitsExecutive Managementgreportcorporate sector/corporate departmentfor a master data yin relevant units (datamaintenance/ application)Master DataOwner AMaster DataOwner XMasterMt DataD tOfficer Master DataOfficer tsMaster Data ManagementSteering Committeeworking group /competence teamInnterdisciplinaryy(MMD Owner, ITT, .)Overall responsibilityIT ProjectsMaster dataclass 1 e. g. Supplier master dataIT platforms, IT target systemsMaster dataclass NChart of accountsAutomotive IndustryMinneapolis, MN, 09/26/11, B. Otto / 17
Case D is an example of a formalized Data Governance organization withhybrid location of responsibilitiesDeutscheTelekom AGT-HomeMQMMarketing andQuality Mngmt.T-MobileT-SystemsLine ofBusiness CIO MQM2QualityManagementIT1IT Strategy andQualityMQM27Data QualityManagement ZIT721DataGovernanceIT2Enterprise ITArchitecture ZIT7InformationProcessing ZIT72MDMIT73/74DataManagement ZIT722DQ Measurementand AssuranceMinneapolis, MN, 09/26/11, B. Otto / 18Telecom Industry
In Case E Master Data Management is organized as a shared service andoperated as a “data factory”Ensures that the quality ofdata objects supports thedependent businesspprocessesData GovernanceCreates, changesand retires a dataobjectDataLifecycleManagementData QualityAssuranceMDMOrganisationEnsures that theMDM agenda canbe driven acrossthe enterpriseData & System ArchitectureEnables a single view oneachh mastert datad t classlChemical IndustryMinneapolis, MN, 09/26/11, B. Otto / 19
Case F is an example for locating the Data Management Organizationwithin the IS/IT functionManagement BoardKey:ProcessHarmonization gementCommitteeBusinessProcess Architecture Mgmt.DivisionITIT Architecture&Org. gmt.IT CompetenceCenterMaster DataManagementInformationand cedDevelopmentpCorporate ITAutomotive IndustryRecently established.Minneapolis, MN, 09/26/11, B. Otto / 20
Some key success factors become apparent when analyzing the casesDemonstrate staying power! Data Governance is a changeissue 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 getb iin ffrom llocall bbuybusinessiunits.iMinneapolis, MN, 09/26/11, B. Otto / 21
Agenda1 Business1.B iRRationaletil ffor DDatat GGovernance2. Data Governance Designg Optionsp3. Best Practice Cases4. Competence Center Corporate Data QualityMinneapolis, MN, 09/26/11, B. Otto / 22
The Competence Center Corporate Data Quality comprises 20 partnercompanies1AO FOUNDATIONASTRAZENECA PLCBAYER AGBEIERSDORF AGCORNING CABLE SYSTEMS GMBHDAIMLER AGDB NETZ AGE.ON AGETA SAFESTO AG & CO. KGHEWLETT-PACKARD GMBHIBM DEUTSCHLAND GMBHMIGROS-GENOSSENSCHAFTS-BUNDNESTLÉ SANOVARTIS PHARMA AGROBERT BOSCH GMBHSIEMENS ENTERPRISECOMMUNICATIONS GMBH & CO. KGSYNGENTA AGTELEKOM DEUTSCHLAND GMBHZF FRIEDRICHSHAFEN AG1) Current and former partners as of March 2011.Minneapolis, MN, 09/26/11, B. Otto / 23
The Competence Center Corporate Data Quality channels the knowledgeand experience of a large network of practitioners and researchersNB: as of August 2011.Data covers 2006-2010.650 Contacts in the overall CC CDQ community155 Members in the XING Community150 150Bilateral Project Workshops55Best Practice Presentations25Consortium Workshops20Partner Companies12Scientific Researchers/PhD Students1Competence CenterMinneapolis, MN, 09/26/11, B. Otto / 24
Life is good with Data Governance Source: Strassmann, 1995.Minneapolis, MN, 09/26/11, B. Otto / 25
ContactProf. Dr. Boris OttoUniversity of St. Gallen, Institute of Information ManagementTuck School of Business at Dartmouth h.edu 1 603 646 8991Minneapolis, MN, 09/26/11, B. Otto / 26
Head of Data Governance Head of Data Governance Head of MDM SSC Project Manager 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). Minneapolis, MN, 09/26/11, B. Otto / 11
