Managing Enterprise Data as an Asset

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This presentation shows best practices in establishing and sustaining enterprise-wide data quality management.

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Managing Enterprise Data as an Asset

  1. 1. Managing Enterprise Data as an AssetBest Practices in Establishing and Sustaining Enterprise-Wide DataQuality ManagementProf. Dr. Boris Otto, Assistant ProfessorMunich, May 23, 2012Chair of Prof. Dr. Hubert Österle
  2. 2. © CC CDQ – Munich, May 23, 2012, Boris Otto / 2Agenda1. The Enterprise Data Challenge2. Enterprise-Wide Data Quality Management3. «Best Practices»
  3. 3. © CC CDQ – Munich, May 23, 2012, Boris Otto / 3Agenda1. The Enterprise Data Challenge2. Enterprise-Wide Data Quality Management3. «Best Practices»
  4. 4. © CC CDQ – Munich, May 23, 2012, Boris Otto / 4In many large enterprises no unambiguous understanding of key businessobjects existsResearch &DevelopmentLogistics &Distribution9 x 4 x 2 cm310 gThe product in the real world…… and perceptions in …SalesEnvironment, Health& Safety
  5. 5. © CC CDQ – Munich, May 23, 2012, Boris Otto / 5The ambiguity causes synonyms and duplicate records on businessprocess and IT level, thus, poor data qualityReal-World ObjectBusiness View(Business Objects)InformationManagement View(Information Objects)IT View(Data Objects)000004711 SUP00800Becker AG B. BECKER GMBH ……
  6. 6. © CC CDQ – Munich, May 23, 2012, Boris Otto / 6Today, many companies manage data quality reactively, i.e. in a «firefighting» modeLegend: Data quality“Submarines” (e.g. migrations,process errors, irregularities inmanagement reporting).Data QualityTimeProject 1 Project 2 Project 3 No risk mitigation No chance to plan and to control budgets and resources No target values for corporate data quality No sustainable data quality High recurring project costs (change requests, external consultants etc.)
  7. 7. © CC CDQ – Munich, May 23, 2012, Boris Otto / 7Root causes for poor data quality are manifold as the case of BayerCropScience showsLow / Not sustainableData QualityPeople Data MaintenanceStandards OrganizationNo sufficienttraining and / oreducationData Quality KPIsare not part ofpersonal objectivesHeterogeneous setof data maintenancetoolsMaster Data notprotected in alloperational systemsToo many rules,even more exceptionsNo globally acceptedset of rules, standards,policies, guidelinesGaps in businessresponsibility forMaster Data objectsNo empoweredData GovernanceorganizationData Quality ProcessesOnly very fewData Quality KPIsdefinedNo continuousmonitoring ofData QualityMaintenance processesare not fully supportedby existing toolsetMaster Datamaintenance processesnot globally harmonizedand optimizedPeople Data MaintenanceData Quality Process Standards OrganizationPoor DataQualityLegend: KPI - Key Performance Indicator.Source: Brauer, B. (2009). Master Data Quality Cockpit at Bayer CropScience. Paper presented at the 4th Workshop of the Competence Center Corporate Data Quality 2,Lucerne.
  8. 8. © CC CDQ – Munich, May 23, 2012, Boris Otto / 8Agenda1. The Enterprise Data Challenge2. Enterprise-Wide Data Quality Management3. «Best Practices»
  9. 9. © CC CDQ – Munich, May 23, 2012, Boris Otto / 9Enterprise data quality is a prerequisite for strategic business goalsAnti-counterfeitingCompany-wide batch management harmonizationCompliance§Operational excellence through efficient “data supply chains”Increased inventory visibility and improved planningLean Supply ChainManagement€Enhanced decision-marking procedures“Single version of the Truth”Business IntelligenceEffectivenessiImproved spend analysesEffective supplier development and managementStrategic Purchasing€Central master data services allow for IT consolidationIT Cost Reduction€
  10. 10. © CC CDQ – Munich, May 23, 2012, Boris Otto / 10Thus, enterprise data quality management must be organized according toa set of design principles Accountable Comprehensive Lean Measurable Preventive Sustainable
  11. 11. © CC CDQ – Munich, May 23, 2012, Boris Otto / 11Corporate Data Quality Management (CDQM) is a Business Engineering task on acompany’s business strategy, organization, and information systems levelStrategyOrganizationSystemCDQ ControllingApplications for CDQMCorporate Data ArchitectureOrganizationfor CDQMCDQM Processes andMethodsStrategy for CDQMlocal globalMandateStrategy documentValue managementAction planGoals and targetsData quality metricsData GovernanceRoles andresponsibilitiesChangemanagementStandards &GuidelinesData life cyclemanagementBusiness metadatamanagementData-driven businessprocess managementConceptualcorporate datamodelData distributionarchitectureAuthoritative datasourcesSoftware support (e.g.MDM applications)System landscapeanalysis and planning
  12. 12. © CC CDQ – Munich, May 23, 2012, Boris Otto / 12The EFQM Excellence Model for CDQM1 was collaboratively developed by EFQM,the University of St. Gallen, and partners from industryLegend: Current value 2010Target value 2011 (= one maturity level for all enablers)StrategyControllingOrganizationProcesses& MethodsDataArchitectureApplicationsCDQM Maturity Assessment1) EFQM: EFQM Framework for Corporate Data Quality Management: Assessing the Organization’s Data Quality Management Capabilities, EFQM Press, Brussels, 2012.EFQM Framework Corporate DataQuality Management
  13. 13. © CC CDQ – Munich, May 23, 2012, Boris Otto / 13Agenda1. The Enterprise Data Challenge2. Enterprise-Wide Data Quality Management3. «Best Practices»
  14. 14. © CC CDQ – Munich, May 23, 2012, Boris Otto / 14Material master data quality has continuously been improved at BayerCropScience
  15. 15. © CC CDQ – Munich, May 23, 2012, Boris Otto / 15Data quality leads to tangible business benefitsSavings of 2 percent of average inventory value p.a.1More than GBP 500 million saved through retrieval of«lost assets»2CHF 3,000 saved per each obsolete master data record31) Benefit assessment as a result from a series of expert interviews at one of the CC CDQ partner companies.2) Otto, B.; Weber, K.: From Health Checks to the Seven Sisters: The Data Quality Journey at BT, University of St. Gallen, Institute of Information Management, St. Gallen,2009.3) Lay, J. (2008). Produktdaten im ERP. Paper presented at the Stammdatenmanagement-Forum 2008, Rapperswil.
  16. 16. © CC CDQ – Munich, May 23, 2012, Boris Otto / 16The Competence Center Corporate Data Quality (CC CDQ) is a consortiumresearch project involving 22 partner companiesAO FOUNDATION ASTRAZENECA PLC BAYER AG BEIERSDORF AGCORNING CABLE SYSTEMS GMBH DAIMLER AG DB NETZ AG E.ON AGETA SA FESTO AG & CO. KG HEWLETT-PACKARD GMBH IBM DEUTSCHLAND GMBHKION INFORMATION MANAGEMENTSERVICE GMBHMIGROS-GENOSSENSCHAFTS-BUND NESTLÉ SA NOVARTIS PHARMA AGROBERT BOSCH GMBH SAP AGSIEMENS ENTERPRISECOMMUNICATIONS GMBH & CO. KGSYNGENTA CROP PROTECTION AGTELEKOM DEUTSCHLAND GMBH ZF FRIEDRICHSHAFEN AG NB: Overview comprises both current and past research partner companies.
  17. 17. © CC CDQ – Munich, May 23, 2012, Boris Otto / 17CC CDQ Resources on the InternetInstitute 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
  18. 18. © CC CDQ – Munich, May 23, 2012, Boris Otto / 18Prof. Dr. Boris OttoAssistant Professor & Head of CC CDQUniversity of St. GallenInstitute of Information ManagementSwitzerland+41 71 224 32 20boris.otto@unisg.chPlease reach out to me in case of questions and comments

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