Evolution of data governance excellence
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Evolution of data governance excellence Evolution of data governance excellence Presentation Transcript

  • London, 04/17/13, A. Reichert / 1
  • University of St. Gallen, Institute of Information ManagementEvolution of Data Governance Excellence in LargeEnterprises: Lessons Learned and StrategicDirectionsAndreas ReichertLondon, April 17th, 2013
  • London, 04/17/13, A. Reichert / 31) Actual und former partner companies since November 20062) Institute of Information Management at the University of St. GallenApproach  Design of solutions (e.g. architecture designs, models, methods,prototypes) supporting a quality oriented management of corporate data Set up of community for exchange of best practices for master data anddata quality managementSupportingcompanies1Organization  Consortium consisting of IWI-HSG2 and partner companies Joint creation of solution within workshops (5x per year) and projects Organization an management by IWI-HSG, since 2012 jointly with BEISt. GallenThe research of the Competence Center Corporate Data Quality (CDQ)is based on interaction with companies listed below
  • London, 04/17/13, A. Reichert / 4Agenda1. Business Rationale for Data Governance2. Data Governance Design Options
  • London, 04/17/13, A. Reichert / 5Data Governance is necessary in order to meet several strategic businessrequirements Legal and regulatoryrequirements ContractualobligationsRisk Management “Single Point of Truth” Standardized reportsand KPIsCorporateReporting Business processharmonization “End-to-end” businessprocessesGlobal BusinessProcesses 360°view oncustomers Hybrid productsCustomer-centricbusiness models Integration of acquiredbusinesses Data due diligenceMergers &Acquisitions IT consolidation (“domore with less”) Flexible architecturesComplexitymanagement1 23 45 6
  • London, 04/17/13, A. Reichert / 6Business impact of data quality?A product data example, consumer goods industryGTIN: Global Trade Item Number, standardized by Global Standards One (GS1, www.gs1.org)123452 To add additional filling may be reasonable with transparent bottles But: Not maintaining changed gross weight my cause wrong packingCapacity2 Wrong shelf planning at customers (retail) due to inaccurate measures Repacking of pallets due to inaccurate gross weightsLogisticData1 Flawed products due to too high or too low temperature during transport Temperature tolerance depends on product formula (bill of material)Temperaturefor transportation3 Different formats in several countries No globally standardized but changing formats (e.g. date, duration)Format ofexpiry date4 Wrong GTINs may cause complaints and compensations Product changes may require a new GTIN GTIN allocation depends on global and local guidelinesGTIN5Data quality is a prerequisite for correctproduct information and supply chain efficiency
  • London, 04/17/13, A. Reichert / 7Complexity drivers indicate a strong need for Data GovernanceCDQData volumesRFID, customer loyalty programsetc.Global processesMultilingualism, “Follow the sun“-principle etc.“Taylorism”Segregation of data creation anddata useConstant ChangeM&A, “Divestments”, ChangeManagement“Hyper-connectivity”New, external data sources, Data-Supply-chains etc.SizeRevenue Nestlé 2008: 110 billion CHFFederal budget CH 2008: 57 billion CHF
  • London, 04/17/13, A. Reichert / 8Defining Data Governance Data governance aims at the identification of decision rights and roles tofacilitate a consistent, company-wide behavior in the use of corporate data Also, data governance allocates responsibilities to roles to ensure theexecution of assigned decision rights Data governance results in company-wide standards, guidelines andmethodologies for creation and use of corporate dataManagement of sustainableand reliable high quality master data
  • London, 04/17/13, A. Reichert / 9The typical evolution of data quality over time in companies shows astrong need for actionLegend: Data quality pitfalls(e. g. Migrations, ProcessTouch Points, PoorManagement Reporting Data.Data QualityTimeProject 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.)
  • London, 04/17/13, A. Reichert / 10The CDQ Framework – Success Factors for effective Data GovernanceStrategyOrganizationSystemCDQ ControllingApplications for CDQCorporate Data ArchitectureCDQOrganizationCDQ Processes andMethodsCDQ Strategylokal globalMandateStrategy documentValue managementRoadmapKPI systemMeasurement processDimensions of dataqualityData GovernanceRoles andresponsibilitiesChange managementStandards & GuidelinesData life cyclemanagementMetadata managementMethods andprocessesConceptual corporatedata modelDistribution architectureData storagearchitectureSoftware for corporatedata qualitymanagementAs-is and To-be-planning of applicationsystem support
  • London, 04/17/13, A. Reichert / 11Design options for implementing Data GovernanceKey: BU: Business Unit; SSC: Shared Service Center Line Organization (Sold Line)Dotted LineCoordination via SLALocal Function/Staff Organization per BU Central FunctionShared Service Center ExternalizationGroup LevelBU BU BU BUGroup LevelBU BU BUCentralFunctionGroup LevelBU BU BUExternalPartyGroup LevelBU BU BU SSC1 23 4
  • London, 04/17/13, A. Reichert / 12Agenda1. Business Rationale for Data Governance2. Data Governance Design Options
  • London, 04/17/13, A. Reichert / 13Example 1 - High Tech IndustryBusiness drivers for Data Governance Changing business model From product & system business to solution orientation Focus on indirect business models Trend to managed services Higher competition leads to higher cost pressure Need to simplify and harmonize processes and IT Need to simplify and strengthen the organization Changes in the market require high flexibility Reduce the complexity in products and services Enable rapid merger and acquisitionsAccurate and trustful master data are the basis for business processes andenable to react flexible on changes!
  • London, 04/17/13, A. Reichert / 14The need for high quality master data for the new business environmentto GRIDThe GRID (Global Responsibility for Integrated Data) initiativeaims at setting up a global Enterprise Data Management (EDM)consisting of governance (organizational structures, roles,responsibilities, tasks), processes (data management, businessprocesses) as well as the information technology(systems, interfaces, automation).GRID has the mission to secure the global consistency ofmaster data – product, product information, supplier, customer - inorder to smoothly operate the business.
  • London, 04/17/13, A. Reichert / 15Why do we need global master data Governance?BusinessprocessesCorporateEnterprise Data Management is the backbone of the business processes!Global planning capabilities & integration of 3rd party productsEfficient marketing and e-commerce enablement (e2e)Clean & full integration of service business into MDMSpend transparency and volume consolidationSCMMark / SalesServicePurchasingInformationComplianceProjectsHigh reporting quality and timely reportingTraceability of products and export complianceAcceleration of project delivery and reduction of efforts
  • London, 04/17/13, A. Reichert / 16Processes are defined on strategic, governance, and operational levelEDM Life CycleManagementEDM Life CycleManagementCustomerEDM Life CycleManagementEDM Life CycleManagementEDM Strategy1EDM Standards& GuidelinesDevelopvisionDefineEDMroadmapDevelopcom./changestrategySet uporganizationresponsib.Align withbusiness/ITstrategyEDM Quality-AssuranceDefinemeasure-ment metricsDefinequalitytargetsDefinereportingstructuresMonitor &report23Definenomen-clatureDefine lifec.processesDefineauthoriza-tion conceptDefine & rollout lifecycleproceduresEDMData Model4 Detectrequirementsfor modelAnalyzeimplication ofchangesModelmaster dataTest masterdata modelchangesGovernanceStrat.EDMArchitecture5 Detectrequirementsfor arch.Analyzeimplication ofchangesModel dataarchitectureRoll out EDMarchitectureImplementworkflows/UIsImplementmeasure-ment metricsRoll out datamodelchangesModelworkflows /UIsEDM Support7ProvidetrainingsProvidebusinesssupportProvideprojectsupportEDM Life CycleManagement6OperationsSource/approveinformationDeploymaster dataArchivemaster dataCreatemaster dataMaintainmaster dataExecuted by EDMorganizationGoverned by EDMorganizationMass datachangesBusiness object specifictasks and responsibilitiesCommon tasksTasks andresponsibilities ofdifferentbusiness objects(e.g. supplier,customer, etc.)may differ on theoperational level.SupplierSupplierCustomerCustomer……
  • London, 04/17/13, A. Reichert / 17Roles are defined on strategic, governance, and operational levelGovernance LevelOperational LevelStrategic LevelSet strategic direction ofEDM and ensure alignmentwith business and ITstrategy.Define and control standardsand guidelines for enterprisedata according to the businessrequirements.Request, create, maintainand approve enterprise datafollowing defined standardsand guidelines. Establishtechnical readiness of ITsystems.EDM CommunityEDM BoardHead of ITBusinessData StewardTechnicalData StewardExecutive SponsorHead of EDMCorporate Data OperatorBusiness process ownerEDM organizationOther SEN organizationGlobal rolesGlobal or regional roles
  • London, 04/17/13, A. Reichert / 18Solution – Data Governance as central functionInteractionHead of EDMStrategiclevelGovernance/OperationallevelBusiness processes EDMEDM-BoardOperativein SAPBusiness ProcessOwnerBusiness ProcessOwnerData OwnerCorporate DataOperatorCommunicate /improve standardsDefine standardsBusiness DataStewardBusiness DataStewardEnforce standardsduring data updateAlign process /data requirementsITHead of ITAlign IT strategyIT implementationIT DataSteward
  • London, 04/17/13, A. Reichert / 19Example 2 – Chemical IndustryBusiness drivers for Data Governance Process Efficiency Delayed delivery to customers due to wrong material master Invoicing to the wrong customer Wrong labels Cost Reduction High inventories due to lack of trust in master data Additional air freight costs to ensure on time arrival Management Decision Support Reporting inaccuracy due to inconsistent data
  • London, 04/17/13, A. Reichert / 20The MDM organization will sustain efficiency and quality of master data• Defining and monitoring of SLAs and KPIs in a global governance framework• Acting as a global stewardship organization, driving the global standardization andoptimization of processes• Providing one global lead steward for each data object to ensure accountability and ahigh level of support to business users3. The MDM organization act as a catalyst through…• Accountabilities for master data are defined and data quality monitored• Maintenance processes are globally standardized and automated• A small number of data specialists concentrate on continuous improvement instead offirefighting and data typing2. We have to come to a state where…• No clear accountability for master data on a global level• Lack of standardization and automation Inefficient and heterogeneous ways of managing master data Poor data quality troubles users of global systems (APO, EDWH, global productcosting1. The situation today shows…
  • London, 04/17/13, A. Reichert / 21Process landscape for MDM services Each process delivers services to the business organizations The implementation of the services will follow of structured roadmap for the defined masterdata types (Material, Vendor, Customer, Finance, Employee) The services are measured by Service Level Agreements (SLAs) in order to assure thequality of the servicesProcess landscapeMaster Data Maintenance2Master DataStandardsTraining &SupportQualityAssurance3 4 5Master Data Infrastructure6Master Data Strategy1Scope of servicesMaterialVendorCustomerFinanceEmployee
  • London, 04/17/13, A. Reichert / 22Solution - Shared Service Center for governance and operationalresponsibilityData & System ArchitectureDataLifecycleManagementData QualityAssuranceMDMOrganisationData GovernanceEnables a single view oneach master data classCreates, changesand retires a dataobjectEnsures that the quality ofdata objects supports thedependent businessprocessesEnsures that theMDM agenda canbe driven acrossthe enterprise
  • London, 04/17/13, A. Reichert / 23Organizational integration of MDMCEOFunctionalGroupingServiceFunctionsBS (HR, IS,FI, LT etc)etcStrategicFunctionsHRFIMarketingetcDivisionalGroupingGeographicstructureProductstructureMarketstructureHead ofBusinessServicesHead of MDMRegional MDMHeadsHead of NAFTAMDMHead of LATAMMDMHead ofEAME/APACMDMLead DataStewardsMaterial HRCustomer VendorFinanceData ArchitectCompany structure MDM structure
  • London, 04/17/13, A. Reichert / 24Main benefit of the global MDM organization is the overall improved dataquality enabling the business to focus on value add activities• Change of functional reporting from business to a business neutral MDM unit• Change of regional reporting lines to global reporting lineImpacts• Harmonized processes and policies and governance across regions & business units• Higher scalability: faster integration of new companies or processes, systems etc.• Bigger pool of trained people• Reduced headcount• Reduced number of codes in system (big issue in material today as well as vendor andcustomer)• Improved data quality & reporting also since global team has higher authority to advise regionalteams to not “manipulate data in ERP system)• Attraction for higher skilled employees based on career opportunitiesBenefits• Strong and visible SLAs in place including tracking of KPIs• Strong governance model between business and MDM• Quick wins for Business in order to Business to accept organization• Outsourcing only when internal processes work wellCritical success factors
  • London, 04/17/13, A. Reichert / 25Governance design principlesGlobal Global responsibility Regional and local presenceShared Center of excellence for the business Efficiency and speedGoverning Binding standards and guidelines for the use of master data Defined methodologies and toolsService-oriented Aiming at internal customer satisfaction Service level agreements for measurable performanceManaged Preventive measures instead of “firefighting” Clear objectives and standard operating proceduresEmpowered Sponsored by executive management Appropriate resource assignment
  • London, 04/17/13, A. Reichert / 26The way forward – From shared service to outsourced data managementprocessesIS OutsourcingPartnerCompanyDomain MDMTeamsMDM LeadsMDM DataStewardsCompanyService Delivery &Operations TeamsService DeliveryManagersMaster DataRequestorsBusinessProcessOutsourcingPartnerMaster DataProcessorsClientsMaster Data Request Originators
  • London, 04/17/13, A. Reichert / 27Key success factors for implementing Data GovernanceDemonstrate 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 getbuy in from local business units.Define clear objectives and standard operation procedures toprevent “firefighting”.
  • London, 04/17/13, A. Reichert / 28Contacthttp://www.bei-sg.chhttp://cdq.iwi.unisg.chAndreas ReichertUniversity of St. GallenCC Corporate Data Qualityandreas.reichert@unisg.chTel.: +41 71 224 3880
  • London, 04/17/13, A. Reichert / 29Further informationInstitute of Information Management at the University of St. Gallenhttp://www.iwi.unisg.chBusiness Engineering Institute St. Gallenhttp://www.bei-sg.chCompetence Center Corporate Data Qualityhttp://cdq.iwi.unisg.chCC CDQ Benchmarking Platformhttps://benchmarking.iwi.unisg.ch/CC CDQ Community at XINGhttp://www.xing.com/net/cdqm