Data Governance from aStrategic Management Perspectiveg g pProf. Dr. Boris Otto, Assistant ProfessorBerlin, February 16, 2...
AgendaAgenda A strategic view of Data Governance A strategic view of Data Governance Data Governance as dynamic capabil...
AgendaAgenda A strategic view of Data Governance A strategic view of Data Governance Data Governance as dynamic capabil...
Data Governance is necessary to respond to a numberData Governance is necessary to respond to a numberof external business...
Data Governance and Data Quality Management areData Governance and Data Quality Management areclosely interrelatedMaximize...
Data Governance effectiveness still varies widely todayData Governance effectiveness still varies widely today25 07.57.525...
What issues does upper management see with regardWhat issues does upper management see with regardto Data Governance? The ...
AgendaAgenda A strategic view of Data Governance A strategic view of Data Governance Data Governance as dynamic capabil...
Data Governance, what is it really?Data Governance, what is it really?… a business process?an organizational unit?… an org...
Data Governance as a Dynamic CapabilityData Governance as a Dynamic CapabilityDynamic capabilities describe an enterprise’...
Companies start from different Data ManagementCompanies start from different Data Managementpositions221 56D t Q lit M tDa...
Note taken in a meeting with Johnson & JohnsonNote taken in a meeting with Johnson & Johnsonon November 29, 2011, in Skill...
The ideal lifecycle of Data Governance as a dynamicThe ideal lifecycle of Data Governance as a dynamiccapability resembles...
AgendaAgenda A strategic view of Data Governance A strategic view of Data Governance Data Governance as dynamic capabil...
It is not a perfect world, thoughIt is not a perfect world, thoughE E E3.Pattern I Pattern II4.5. Pattern IIIE E E2.1.2.3....
Double-loop learning is a central success factor forDouble loop learning is a central success factor forData Governance ma...
“We really need to change the way Data Governance andData Management are perceived throughout the companyData Management a...
Options for novel approaches: The case of CorningOptions for novel approaches: The case of CorningCable Systems1 Align MD...
Existing Data Management expertise is a crucial factor for identifying aExisting Data Management expertise is a crucial fa...
The maturity model is an instrument for controllingThe maturity model is an instrument for controllingData Governance effe...
RWE benefits from EUR 2.6 million costs avoidedRWE benefits from EUR 2.6 million costs avoidedAverage cost rate Number of ...
Some more tangible benefit examplesSome more tangible benefit examplesSavings of 2 percent of actual value in stockSavings...
Contact DetailsContact DetailsProf. Dr. Boris OttoAssistant ProfessorUniversity of St. GallenBoris.Otto@unisg.chTel.: +41 ...
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Data Governance from a Strategic Management Perspective

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Data Governance from a Strategic Management Perspective

  1. 1. Data Governance from aStrategic Management Perspectiveg g pProf. Dr. Boris Otto, Assistant ProfessorBerlin, February 16, 2012
  2. 2. AgendaAgenda A strategic view of Data Governance A strategic view of Data Governance Data Governance as dynamic capability Examples from the consortium© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 2
  3. 3. AgendaAgenda A strategic view of Data Governance A strategic view of Data Governance Data Governance as dynamic capability Examples from the consortium© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 3
  4. 4. Data Governance is necessary to respond to a numberData Governance is necessary to respond to a numberof external business requirements1 Customer-Centric Business Models$$ Value Chain Excellence§ Contractual and Regulatory Compliance© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 4
  5. 5. Data Governance and Data Quality Management areData Governance and Data Quality Management areclosely interrelatedMaximizeData QualityMaximizeData Valueis sub-goal ofD D Q lisupports supportsis led by is sub-functionDataGovernanceData QualityManagementDataManagementis led by is sub-functionofData Assetsare object of are object ofare object ofData AssetsLegend: Goal Function Data.Source: Otto, B.: Data Governance, in: WIRTSCHAFTSINFORMATIK, 53, 4, 2011, S. 235-238.© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 5, , , , , ,
  6. 6. Data Governance effectiveness still varies widely todayData Governance effectiveness still varies widely today25 07.57.525.030 0very good30 030.0y ggooddi30.0 mediocreadequatepoorSource: Messerschmidt, M.; Stüben, J.: Verborgene Schätze: Eine internationale Studie zum Master-Data-Management,PricewaterhouseCooopers AG, 2011, pp. 25 et seq.NB: Figures are percentages.© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 6
  7. 7. What issues does upper management see with regardWhat issues does upper management see with regardto Data Governance? The case of Syngenta Business benefitsus ess be e ts– “Keep in mind to balance costs for double-handling on one hand and of highdiscipline on the other.”“Emphasize usability of MDM its value ”– Emphasize usability of MDM, its value. Organizational readinessg– “Data owners and data stewards are terms people don‘t understand. Beeducational and promotive.”– “Organizational maturity differs in the divisions ”Organizational maturity differs in the divisions. Data Governance implementation and execution– “What’s the migration path? Are there intermediate staging gates?”– “Is it a journey or can one make a choice? Or both?”– “How to integrate this strategy into the program of next year?”How to integrate this strategy into the program of next year?– “How to integrate the 35,000 ft view with daily operations?”NB: Selected quotes from a series of eight interviews with line managers conducted in October and November 2011.© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 7q g g
  8. 8. AgendaAgenda A strategic view of Data Governance A strategic view of Data Governance Data Governance as dynamic capability Examples from the consortium© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 8
  9. 9. Data Governance, what is it really?Data Governance, what is it really?… a business process?an organizational unit?… an organizational unit?… just a persistent management fad?© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 9
  10. 10. Data Governance as a Dynamic CapabilityData Governance as a Dynamic CapabilityDynamic capabilities describe an enterprise’s ability to address a changing market environment byintegrating, reconfiguring, gaining, and releasing resources11) Eisenhardt K M and Martin J A Dynamic Capabilities: What Are They? Strategic Management Journal 21 10-11 (2000) 1105-1121© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 101) Eisenhardt, K.M. and Martin, J.A. Dynamic Capabilities: What Are They? Strategic Management Journal, 21, 10-11 (2000), 1105-1121.
  11. 11. Companies start from different Data ManagementCompanies start from different Data Managementpositions221 56D t Q lit M tData Strategy Management22165Data StewardshipData Quality Management1 2 3 2Data Lifecycle Management1 2 1 4DataArchitectureManagement7 1Database OperationsManagementDoes not exist in comprehensive formExisted prior to Data Governance in similar formAbsolute numbers.Existed prior to Data Governance in similar formExisted prior to Data Governance, but was significantly revised or extendedNewly createdNB: Based on data from eight cases (Bayer CropScience, Corning Cable Systems, DB Netz, Deutsche Telekom, Johnson & Johnson, RobertB h S t ZF)© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 11Bosch, Syngenta, ZF)
  12. 12. Note taken in a meeting with Johnson & JohnsonNote taken in a meeting with Johnson & Johnsonon November 29, 2011, in Skillman, NJ© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 12
  13. 13. The ideal lifecycle of Data Governance as a dynamicThe ideal lifecycle of Data Governance as a dynamiccapability resembles an “S” curveEFounding Phase „First Time Right“ CleansingELegend: E − Effectiveness; A − Amount of Activity. A© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 13
  14. 14. AgendaAgenda A strategic view of Data Governance A strategic view of Data Governance Data Governance as dynamic capability Examples from the consortium© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 14
  15. 15. It is not a perfect world, thoughIt is not a perfect world, thoughE E E3.Pattern I Pattern II4.5. Pattern IIIE E E2.1.2.3.1. 2.3. 4.2008 2009 2010 20111.2008 2009 2010 2011 2007 2008 2009 2010A A A2008 2009 2010 20111. CDM unit launched2. Data creation workflow3. DQ metrics launched2008 2009 2010 20111. DG project launched2. Address to board3. DQ metrics launched4. „Community“ approach2007 2008 2009 20101. CDM unit launched2. Progress report to the boardproposed3. Inventory data quality4. „Community approachproposed5. DG council launched3. Inventory data qualityassessment4. CDM reorganizedLegend: E − Effectiveness; A − Amount of Activity; CDM − Corporate Data Management; DQ − Data Quality; DG −Data Governance.© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 15
  16. 16. Double-loop learning is a central success factor forDouble loop learning is a central success factor forData Governance maturity“Problems cannot be solvedby the same level of thinkingby the same level of thinkingthat created them.”© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 16
  17. 17. “We really need to change the way Data Governance andData Management are perceived throughout the companyData Management are perceived throughout the company.Usually people do not welcome me with open arms when Ienter their office Data management is somehow treated likeenter their office. Data management is somehow treated likea skunk—no-one wants to spend too much time with it. Itwould be very important that we change the image of thewould be very important that we change the image of theissue. What would have a more positive connotation than askunk? Maybe a squirrel!” Karl-Heinz Weber, Bayer CropScience AGskunk? Maybe a squirrel! Karl Heinz Weber, Bayer CropScience AG© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 17
  18. 18. Options for novel approaches: The case of CorningOptions for novel approaches: The case of CorningCable Systems1 Align MDM with the company’s culture of quality management Proof of concept for customer master data creation in NAFTAcustomer master life cycleTransferringTQM principles to MDM1 Master data as an asset Establish business-oriented data quality metrics Data life cycle / Retirement processManaging cost andvalue of master data2 Data life cycle / Retirement process Buy-in for MDM organization from data owners still missing Continuous roll-out of roles and responsibilities in MDMGlobalData Governance rollout3 Implementation of a shared MDM ServiceData Governance rollout Knowledge capitalization on an organization and system levelGlobal leveraging of4g p g y Foundation of a global center for excellenceg gknowledge assets5 Technical integration/substitution of ASI, Windchill with SAP Extend workflow from material master to other domainsSystem integration andprocess automation© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 18
  19. 19. Existing Data Management expertise is a crucial factor for identifying aExisting Data Management expertise is a crucial factor for identifying aneed for action during the establishment of Data Governance© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 19
  20. 20. The maturity model is an instrument for controllingThe maturity model is an instrument for controllingData Governance effectivenessStrategyControllingOrganizationApplicationsDataProcesses& MethodsDataArchitectureLegend: Current value 2010Target value 2011 (= one maturity level for all enablers)© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 20
  21. 21. RWE benefits from EUR 2.6 million costs avoidedRWE benefits from EUR 2.6 million costs avoidedAverage cost rate Number of Number ofValue = per master datarecord*X ( duplicaterecords+ new recordsavoided)Backward Forward* 2,861 EUR (2006).Source: Holzapfel, T. Harmonisierung Von Materialstammdaten - Eine Herausforderung. Proceedings of the Stammdaten-ManagementForum (Frankfurt 2007 09 27) IIR Deutschland 2007© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 21Forum (Frankfurt, 2007-09-27). IIR Deutschland, 2007.
  22. 22. Some more tangible benefit examplesSome more tangible benefit examplesSavings of 2 percent of actual value in stockSavings of 2 percent of actual value in stockMore than GBP 500 million saved through retrieval of“lost assets”More than GBP 500 million saved through retrieval of“lost assets”CHF 3,000 saved per obsolete master data recordCHF 3,000 saved per obsolete master data record© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 22
  23. 23. Contact DetailsContact DetailsProf. Dr. Boris OttoAssistant ProfessorUniversity of St. GallenBoris.Otto@unisg.chTel.: +41 71 224 32 20http://cdq.iwi.unisg.ch© BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 23

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