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A Reference Process Model for Master Data Management

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The management of master data (MDM) plays an important role for companies in responding to a number of business drivers such as regulatory compliance and efficient reporting. With the understanding of …

The management of master data (MDM) plays an important role for companies in responding to a number of business drivers such as regulatory compliance and efficient reporting. With the understanding of MDM’s impact on the business drivers companies are today in the process of organizing MDM on corporate level. While managing master data is an organizational task that cannot be encountered by simply implementing a software system, business processes are necessary to meet the challenges efficiently. This paper describes the design process of a reference process model for MDM. The model design process spanned several iterations comprising multiple design and evaluation cycles, including the model’s application in three participative case studies. Practitioners may use the reference model as an instrument for the analysis and design of MDM processes. From a scientific perspective, the reference model is a design artifact that represents an abstraction of processes in the field of MDM.

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  • 1. Andreas Reichert, PD Dr.-Ing. Boris Otto, Prof. Dr. Hubert ÖsterleLeipzigFebruary 28, 2013A Reference Process Model for Master Data Management
  • 2. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 2Agenda1. Introduction2. Related Work3. Research Methodology4. Results Presentation5. Conclusion and Outlook
  • 3. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 31.1 Business Requirements for Master Data Master data describes key business objects in an enterprise (e.g. Stahlknecht &Hasenkamp 1997; Mertens 1997) Examples are product, material, customer, supplier, employee master data Master data of high quality is important for meeting various business requirements (e.g.Knolmayer & Röthlin 2006; Kokemüller 2010; Pula et al. 2003) Compliance with legal provisions Integrated customer management Automated business processes Effective and efficient reporting
  • 4. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 4Legend: Data quality pitfalls (e. g. migrations, process touch points, poor corporate reporting.Master Data QualityTimeProject 1 Project 2 Project 31.2 Difficulties in practice when it comes to managing master data qualityCase of Bayer CropScience (cf. Brauer 2006)
  • 5. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 51.3 Master Data Management must be organized Master data management is an application-independent function (Smith & McKeen2008) The organizational structure of master data management has been research to someextent Empirical analysis regarding the positioning of master data management within an organization(Otto & Reichert 2009) Master data governance design (Otto 2011)How to design master data management processes?
  • 6. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 61.4 Enterprises are in need of support in this matter* Source: Workshop presentations at the CC CDQ Workshops by companiesCompany Main Challenges Establishing a central master data Shared Service Center forgovernance and operational tasks Support of high quality master data for online sales channels Central governance for new data processes Set up of a central master data organization for material, customer,and vendor master data due to changing business model, and hence,processes New organization of medical and safety division Design of data governance processes for material master data
  • 7. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 7Model Focus Assessment(Dyché & Levy 2006) Customer data integrationNo focus on activities(English 1999): Total Quality data Management (TQdM)(Loshin 2007) Data governance(Weber 2009) Data governance reference model2.1 Related Work in Research and PracticeProcess models related to master data managementRole models related to master data managementModel Focus AssessmentITIL IT service managementNo integrated process focus(Batini & Scannapieco2006)Data quality management activitiesOtto et al. (2012) Software functionality
  • 8. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 83.1 Research Methodology and Process2009 2010 2011 20121. Identify problem & motivate1.1 Identification of challenges within practitioners community2. Define objectives of a solution2.1 Focus group A (2009-12-01)2.2 Principles of orderly reference modelingA6. Communication6.1 Scientific paper at hand4.1 Three participative case studies3.1 Literature review3.2 Principles of orderly reference modelling3.3 Process map techniques3.4 Focus groups B (2010-11-26), C (2011-11-24)B C5.1 Focus group C (2011-11-24)5.2 Three participative case studies5.3 Multi-perspective evaluation of reference modelsC3. Design &development4. Demonstration5. Evaluation
  • 9. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 94.1 Overview of the Reference Process Model for Master Data ManagementData LifeCycleData SupportDataArchitectureData ModelData QualityAssuranceStandards &GuidelinesStrategicFunctions1.12.12.22.3GovernanceStrategy2.43.23.1OperationsDevelopand adaptvisionAlign w/business &IT strategyDefinestrategictargetsSet upresponsibi-litiesDefineroadmapDevelopcommunic.and changeAdaptnomencla-tureAdapt datalife cylceAdaptstandards &guidelinesAdaptauthori-zationconceptAdaptsupportprocessesAdaptmeasure-mentmetricsAdaptreportingstructuresDefinequalitytargetsMonitor &report dataqualityInitiatequalityimprove-mentsIdentifydatarequire-mentsModel dataAnalyzeimplicationsTest &implementchangesRoll outdata modelchangesIdentifybusinessissuesIdentifyrequire-mentsModel dataarchitectureModelworkflows /UIsAnalyzeimplicationson changeRoll outdataarchitectureTest &implementManagerequestsCreate dataUpdatedataReleasedataUse dataArchive /delete dataAdapt usertrainingsProvidetrainingsProvideusersupportProvideprojectsupportProcess Area Main Process Process1231.1.1 1.1.2 1.1.3 1.1.4 1.1.5 1.1.62.1.1 2.1.2 2.1.3 2.1.4 2.1.5 2.1.62.2.1 2.2.2 2.2.3 2.2.4 2.2.52.3.1 2.3.2 2.3.3 2.3.4 2.3.52.4.1 2.4.2 2.4.3 2.4.4 2.4.5 2.4.63.1.1 3.1.2 3.1.3 3.1.4 3.1.5 3.1.63.2.1 3.2.2 3.2.3 3.2.4
  • 10. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 104.2 Iterative Design and Evaluation in Three Case StudiesCase A B CIndustry High Tech Engineering RetailHeadquarter Germany Germany GermanyRevenue 2011 [bn €] 3.2 2.2 42.0Staff 2011 11,000 11,000 170,000Role of main contact person forthe case studyHead of EnterpriseMDMHead of MaterialMDMProject ManagerMDM StrategyInitial situation Specification of existingdata managementorganizationMerger of twointernal datamanagementorganizationsDesign of new datamanagementorganization withinproject
  • 11. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 114.3 Design DecisionsDesign Decision Justification A B CProcess “Define strategictargets” removed (1.1.3) Activities integrated in process “Align with business/IT strategy” No explicit MDM strategic targets required as they should beintegrated in existing target systemsXProcess “ModelWorkflows/UIs (UserInterfaces) moved frommain process “Architecture”to “Standards & Guidelines”(2.4.3) Focus for activity is set on conceptual design rather than technicalimplementation aspects Technical implementation needs to be covered by IT-processes.Case A only covers the conceptual part of the workflow design. Theimplementation process will be described outside of this processXProcess “Monitor & report”(in context of QualityAssurance) moved frommain process “Support” to“Quality Assurance” (3.2.4) Mix of governance and operational activities in main process“Governance” However, focus is set on end-to-end process including both aspectsXProcess “Test & Implement”(in context Architecture)removed (2.4.5) Testing activities defined within IT-processes and do not need to becovered by data management processes Removal will eliminate double definitions within companyX XProcesses of main process“Life Cycle” renamed (3.1) Naming of processes aligned with company specific namingconventions as processes were already definedX X XProcess “Mass datachanges” added to“Support” (new 3.2.5) New process added as activity is performed on continuous baseand should be covered by data management processesX X
  • 12. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 124.3 Design Decisions (continued)Design Decision Justification A B CProcess “Develop and adaptvision” removed (1.1.1) Company strategies not defined by visions but by strategic targets XProcesses “Adapt data lifecycle”, “Adapt standards andguidelines”, “User trainings”,and “Support Processes”merged to “Standards foroperational processes”(2.1.2 - 2.1.6) Activities of all processes remain existing Goal is simplification of process model Description of all activities, which have been merged to the newprocess, will be created on the work description level, which willunderlay the process model for execution of processes (includingprocess flows, responsibilities, etc)XProcesses “Test andimplement (data model)”and “Roll out data modelchanges” removed (2.3.4 -2.3.5) Activities defined within IT service portfolio outside of this processmodel As activities are already defined, they do not need to be coveredwithin this structureXMain process “DataArchitecture” removed (2.4) Activities defined within IT service portfolio Clear separation between business requirements and modeling ofdata and IT realization (integration architecture etc.)XProcess “Data analysis” inmain process “Support”added (new 3.2.6) Requests for one-time analysis of master data as service offeringdefined which are not covered by standard reportsX
  • 13. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 135.1 Conclusion and Outlook Results The reference model supports the design process of master data managements organizationsas well as the specification of existing structures The reference model was evaluated from an economic, deployment, engineering andepistemological perspective (cf. Frank 2006) by researchers and practitioners Contribution Innovative artifact in a relevant field of research Explication of the design process Engaged scholarship case Limitations Qualitative justification of design decisions Further design/test cycles necessary Applicable for large enterprises mainly
  • 14. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 14PD Dr.-Ing. Boris OttoUniversity of St. GallenInstitute of Information ManagementBoris.Otto@unisg.ch+41 71 224 3220Your SpeakerThis research was supported by the Competence Center Corporate Data Quality (CC CDQ) at theUniversity of St. Gallen.
  • 15. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 15ReferencesBRAUER, B. 2009. Master Data Quality Cockpit at Bayer CropScience. 4. Workshop des Kompetenzzentrums Corporate DataQuality 2 (CC CDQ2). Luzern: Universität St. Gallen.DYCHÉ, J. & LEVY, E. 2006. Customer Data Integration, Hoboken (USA), John Wiley.ENGLISH, L. P. 1999. Improving Data Warehouse and Business Information Quality, New York et al., Wiley.FRANK, U. 2006. Evaluation of Reference Models. In: FETTKE, P. & LOOS, P. (eds.) Reference Modeling for Business SystemsAnalysis. Hershey, PA: IGI Publishing.KNOLMAYER, G. F. & RÖTHLIN, M. 2006. Quality of Material Master Data and Its Effect on the Usefulness of Distributed ERPSystems. In: RODDICK, J. F. (ed.) Advances in Conceptual Modeling - Theory and Practice. Berlin: Springer.KOKEMÜLLER, J. 2010. Master Data Compliance: The Case of Sanction Lists. 16th Americas Conference on Information Systems.Lima, Peru: Universidad ESAN.MERTENS, P. 1997. Integrierte Informationsverarbeitung, Wiesbaden, Gabler.OTTO, B. 2011. A Morphology of the Organisation of Data Governance. 19th European Conference on Information Systems.Helsinki, Finland.OTTO, B., HÜNER, K. & ÖSTERLE, H. 2012. Toward a functional reference model for master data quality management. InformationSystems and e-Business Management, 10, 395-425.OTTO, B. & REICHERT, A. 2010. Organizing Master Data Management: Findings from an Expert Survey. In: BRYANT, B. R.,HADDAD, H. M. & WAINWRIGHT, R. L. (eds.) 25th ACM Symposium on Applied Computing. Sierre, Switzerland.PULA, E. N., STONE, M. & FOSS, B. 2003. Customer data management in practice: An insurance case study. J. of Database Mark.,10, 327-341.SMITH, H. A. & MCKEEN, J. D. 2008. Developments in Practice XXX: Master Data Management: Salvation Or Snake Oil?Communications of the AIS, 23, 63-72.STAHLKNECHT, P. & HASENKAMP, U. 1997. Einführung in die Wirtschaftsinformatik, Berlin, Springer.

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