Organizing Master Data Management

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Master data management (MDM) is defined as an application-independent process which describes, owns and manages core business data entities. The establishment of the MDM process is a Business Engineering (BE) tasks which requires organizational design. This paper reports on the results of a questionnaire survey among large enterprises aiming at delivering insight into what tasks and master data classes MDM organizations cover (“scope”) and how many people they employ (“size”). The nature of the study is descriptive, i.e. it allows for the identification of patterns and trends in organizing the MDM process.

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Organizing Master Data Management

  1. 1. Organizing Master Data ManagementOrganizing Master Data ManagementFindings from an Expert SurveyDr. Boris Otto, Andreas ReichertSierre March 23rd 2010Institute of Information ManagementChair of Prof Dr Hubert ÖsterleSierre, March 23rd, 2010Chair of Prof. Dr. Hubert Österle
  2. 2. Agenda1 I t d ti1. Introduction2. Background and Research Approachg pp3. Result Presentation4. Discussion and Outlook5. Project Context© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 2
  3. 3. 1.1 Selected business requirements for master data qualityCompliance to regulationsReporting (“Single Source of Truth”)Business process integrationCustomer-centric business models (“360 Degree View”)Corporate purchasingCorporate purchasingIT consolidation© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 3
  4. 4. 1.2 Motivation and research questionMaster data management (MDM) is referred to as an application-independentprocess for the description, ownership and management of core businessdata entities1,2Establishing MDM is a Business Engineering3 task comprising designactivities on strategic organizational and system levelactivities on strategic, organizational and system levelIn doing so, companies are confronted with the following questions:What is the scope in terms of master data classes?Which tasks does the MDM organization cover?How much capacity in terms of human resources is required to carry out thetasks?Whom does the MDM organization report to?How should responsibilities be balanced between central and local control?How do firms organize MDM?1) DAMA. DAMA Data Management Body of Knowledge (DMBOK): Functional Framework, DAMA International, Lutz, FL, 2007.2) Smith, H.A. and McKeen, J.D. Developments in Practice XXX: Master Data Management: Salvation Or Snake Oil? Communications of the Association forInformation Systems, 23 (4). 63-72.3) Österle, H. Business Engineering: Transition to the Networked Enterprise. Electronic Markets, 6 (2). 14-16.© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 4
  5. 5. 2.1 Background: Master data and MDMTime reference Change frequency Volume volatility Existentiali d dindependenceMaster Data low low low highTransactional high medium high lowDatag gInventory Data high high low lowMDM aims at creating an unambiguous understanding of a company’s coreentities1.entities .1) Smith, H.A. and McKeen, J.D. Developments in Practice XXX: Master Data Management: Salvation Or Snake Oil? Communications of the Association forInformation Systems, 23 (4). 63-72.© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 5
  6. 6. 2.2 Background: Organizational theoryThe two major tasks of organizational design are the division of labor andcoordination1The organization of a company materializes in its organizational structure andin the process organization1Grochla divides the goals of an organization into functional goalsGrochla divides the goals of an organization into functional goals(“Sachziele”) and formal goals (“Formalziele”)2One can distinguish between primary and secondary organizations1) Galbraith, J.R. Designing organizations: an executive guide to strategy, structure, and process. Jossey-Bass, San Francisco, 2002.2) Grochla, E. Grundlagen der organisatorischen Gestaltung. Poeschel, Stuttgart, 1982.© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 6
  7. 7. 2.3 Research approachProject context is the Competence Center Corporate Data Quality (CC CDQ)Surveys objective is of descriptive naturey j pOnline questionnaire covering nine questions was usedClosed questions (except one for master data volumes) were usedSample consisted of 38 experts in the MDM domain from the e-maildistribution list of the CC CDQReturn rate was 50 percentReturn rate was 50 percentQuestions aimed at answer the following:Is MDM part of the primary organization and - if so - where is it located in theorganizational structure?What organizational from has been chosen (line function, shared service etc.)?What are the functional goals (in terms of tasks)?g ( )What is the scope (in terms of number master data classes such as customer data,material data etc. and of number of master data records)?How many employees work in the MDM organization (both central and local)?How many employees work in the MDM organization (both central and local)?© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 7
  8. 8. 2.4 Survey participantsCompany Unit of AnalysisSICCodeSIC DescriptionRevenue 2008[bn EUR]Staff 2008 CountryRobert Bosch GmbH Corporation 36 Electrical Equipment and Components 45.0 283000 GermanyALSTOM Power Division 36 Electrical Equipment and Components n/a n/a SwitzerlandKuehne + Nagel Inc. Corporation 44 Water Transportation 14.0 54000 SwitzerlandSyngenta AG Corporation 28 Chemicals and Allied Products 12.0 24000 SwitzerlandDeutsche TelekomAGCorporation 48 Communications 18.0 45000 GermanyI d t i l d C i l M hi dOerlikon Textile Division 35Industrial and Commercial Machinery andComputer Equip0.6 7500 SwitzerlandBayer CropScience Corporation 28 Chemicals and Allied Products 6.4 18000 GermanyMars Corporation 20 Food and Kindred Products n.a. n.a. GermanyCorning Inc Corporation 32 Stone Clay Glass and Concrete Products 4 1 27000 USACorning Inc Corporation 32 Stone, Clay, Glass and Concrete Products 4.1 27 000 USAB.Braun AG Corporation 80 Health Services 3.8 38000 GermanyABB Corporation 35Industrial and Commercial Machinery andComputer Equip24.2 119000 SwitzerlandGeberit Corporation 39 Misc. Manufacturing Industries 1.6 5700 Switzerlandp gNestle S.A. Corporation 20 Food and Kindred Products 73.0 283000 SwitzerlandPostFinance Corporation 60 Depository Institutions 1.5 2‘830 SwitzerlandDeutsche Bahn AG Division 40 Railroad Transportation 33.5 240000 GermanyBASF Corporation 28 Chemicals and Allied Products 62.3 106000 Germanyp yRWE AG Corporation 49 Electric, Gas & Sanitary Services 49.0 66000 GermanyRoyal PhilipsElectronicsCorporation 36 Electrical Equipment and Components 26.0 116000 NetherlandsTchibo GmbH Corporation 51 Non-Durable Goods 3.6 12000 Germany© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 8
  9. 9. 3.1 Reporting lines16%Linked to centralIT or InformationManagement32%5%ManagementLinked to anothercentraldepartments (e.g.Purchasing,Controlling)11%Controlling)Linked to a businessdepartmentofa businessunitLinked to IT or InformationLinked to IT or InformationManagementin a business unitOther37%n = 19.© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 9
  10. 10. 3.2 Organizational form5%42%21%Line FunctionProjectOrganization42% ProjectOrganizationShared ServiceStaff FunctionVirtualOrganization16%VirtualOrganizationOther5%11%n = 19.© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 10
  11. 11. 3.3 Tasks11%Other84%74%Project supportTraining of users79%8 %Measurement and reporting of master data qualityj pp84%58%D l t d i t f t d d d id liMaster data lifecycle activities (e.g. creation, maintenance, deactivation)90%84%Development and maintenance of the master data strategyDevelopment and maintenance of standards and guidelines74%Business user support47%Application management for a master data management software0% 20% 40% 60% 80% 100%n = 19.© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 11
  12. 12. 3.4 Scope5%Other63%Supplier/ vendor master data68%37%Material and product master dataOrganizational master data (e.g. cost center structures)21%68%Human resources master data (e.g. employees, e-mail accounts)p47%Financial accounting master data (e.g. chart of accounts)84%Customer master data26%Asset master data0% 20% 40% 60% 80% 100%n = 19.© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 12
  13. 13. 3.5 Team size26%26%32%Less than 5Less than 5Between 5 and 10Between 10 and 20More than 20More than 2026%16%n = 19.© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 13
  14. 14. 3.6 Results summaryMDM is seen as both an organizational and technical topic.Data quality management is considered to be an integral part of the MDMq y g g porganization.Companies do not specialize the MDM organization on certain master dataclasses Instead more than 63% are responsible for at least 3 differentclasses. Instead, more than 63% are responsible for at least 3 differentmaster data classes.MDM per se is not a new topic. 42 percent of the respondents state that MDMactivities have been carried out for more than 5 years.MDM organizations are relatively big in size. More than 45 percent of thecompanies employ more than 10 full time employeescompanies employ more than 10 full time employees.No clear statement can be made regarding the positioning of MDM in theorganizational structure. 37 percent report to either a central or a local ITfdepartment whereas 47 percent report to a business function such aspurchasing or financial accounting.© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 14
  15. 15. 4.1 Discussion and outlookDiscussionResults provide first insight into current status of organizing master datatmanagementResults are descriptive and form a starting point for future researchLimitations are due to the nature of expert interviews as a researchLimitations are due to the nature of expert interviews as a researchmethod1:Intentionally selected individuals rather than random selection oflsampleTypically small number of respondentsOutlook to future researchOutlook to future researchCase studies (e.g. on shared service center approaches)Analysis of “formal goals”Method support for the establishment of MDM1) Meuser, M., Nagel, U.:(2002) :ExpertInneninterviews - vielfach erprobt, wenig bedacht. Ein Beitrag zur qualitativen Methodendiskussion. In: Bogner, A.,Littig, B., Menz, W. (eds.): Das Experteninterview. Theorie, Methode, Anwendung. Leske und Budrich, Opladen, 71-93© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 15
  16. 16. 5.1 Project context Competence Center Corporate Data Quality (CC CDQ)Objective Development of strategies, concepts and solutions for corporate data qualityj p g , p p q ymanagement (CDQM)Consortium Bayer CropScience AG (since 2006)Beiersdorf AG (since 2008)Daimler AG (2006 - 2008)Daimler AG (2006 - 2008)DB Netz AG (since 2007)Deutsche Telekom AG (2006 - 2009)E.ON AG (2007 - 2008)ETA SA (2006 - 2008)ETA SA (2006 2008)Hewlett-Packard GmbH (since 2008)IBM Deutschland GmbH (since 2006)Migros-Genossenschafts-Bund (since 2009)Nestlé SA (since 2008)( )Novartis Pharma AG (since 2008)Siemens Enterprise Communications GmbH & Co. KG (since 2010)Syngenta AG (since 2009)ZF Friedrichshafen AG (2007 - 2008)© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 16
  17. 17. Contact PersonDr. Boris OttoUniversity of St. GallenInstitute of Information ManagementE-mail: boris.otto@unisg.ch@ gPhone: +41 71 224 32 20Andreas ReichertUniversity of St. GallenInstitute of Information ManagementInstitute of Information ManagementE-mail: andreas.reichert@unisg.chPhone: +41 71 224 38 80© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 17

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