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Data Governance from a Strategic Management Perspective
1.
Data Governance from
a Strategic Management Perspectiveg g p Prof. Dr. Boris Otto, Assistant Professor Berlin, February 16, 2012
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.
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.
Data Governance is
necessary to respond to a numberData Governance is necessary to respond to a number of external business requirements 1 Customer-Centric Business Models $$ Value Chain Excellence § Contractual and Regulatory Compliance © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 4
5.
Data Governance and
Data Quality Management areData Governance and Data Quality Management are closely interrelated Maximize Data Quality Maximize Data Value is sub-goal of D D Q li supports supports is led by is sub-function Data Governance Data Quality Management Data Management is led by is sub-function of Data Assets are object of are object of are object of Data Assets Legend: 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.
Data Governance effectiveness
still varies widely todayData Governance effectiveness still varies widely today 25 07.5 7.5 25.0 30 0 very good 30 0 30.0 y g good di30.0 mediocre adequate poor Source: 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.
What issues does
upper management see with regardWhat issues does upper management see with regard to 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 high discipline 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. Be educational 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 / 7 q g g
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.
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.
Data Governance as
a Dynamic CapabilityData Governance as a Dynamic Capability Dynamic capabilities describe an enterprise’s ability to address a changing market environment by integrating, reconfiguring, gaining, and releasing resources1 1) 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 / 10 1) Eisenhardt, K.M. and Martin, J.A. Dynamic Capabilities: What Are They? Strategic Management Journal, 21, 10-11 (2000), 1105-1121.
11.
Companies start from
different Data ManagementCompanies start from different Data Management positions 2 2 1 5 6 D t Q lit M t Data Strategy Management 2 2 1 6 5 Data Stewardship Data Quality Management 1 2 3 2Data Lifecycle Management 1 2 1 4 DataArchitecture Management 7 1 Database Operations Management Does not exist in comprehensive form Existed prior to Data Governance in similar form Absolute numbers. Existed prior to Data Governance in similar form Existed prior to Data Governance, but was significantly revised or extended Newly created NB: Based on data from eight cases (Bayer CropScience, Corning Cable Systems, DB Netz, Deutsche Telekom, Johnson & Johnson, Robert B h S t ZF) © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 11 Bosch, Syngenta, ZF)
12.
Note taken in
a meeting with Johnson & JohnsonNote taken in a meeting with Johnson & Johnson on November 29, 2011, in Skillman, NJ © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 12
13.
The ideal lifecycle
of Data Governance as a dynamicThe ideal lifecycle of Data Governance as a dynamic capability resembles an “S” curve E Founding Phase „First Time Right“ Cleansing E Legend: E − Effectiveness; A − Amount of Activity. A © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 13
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.
It is not
a perfect world, thoughIt is not a perfect world, though E E E 3. Pattern I Pattern II 4. 5. Pattern IIIE E E 2. 1. 2. 3. 1. 2. 3. 4. 2008 2009 2010 2011 1. 2008 2009 2010 2011 2007 2008 2009 2010 A A A 2008 2009 2010 2011 1. CDM unit launched 2. Data creation workflow 3. DQ metrics launched 2008 2009 2010 2011 1. DG project launched 2. Address to board 3. DQ metrics launched 4. „Community“ approach 2007 2008 2009 2010 1. CDM unit launched 2. Progress report to the board proposed 3. Inventory data quality4. „Community approach proposed 5. DG council launched 3. Inventory data quality assessment 4. CDM reorganized Legend: 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.
Double-loop learning is
a central success factor forDouble loop learning is a central success factor for Data Governance maturity “Problems cannot be solved by the same level of thinkingby the same level of thinking that created them.” © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 16
17.
“We really need
to change the way Data Governance and Data Management are perceived throughout the companyData Management are perceived throughout the company. Usually people do not welcome me with open arms when I enter their office Data management is somehow treated likeenter their office. Data management is somehow treated like a skunk—no-one wants to spend too much time with it. It would be very important that we change the image of thewould be very important that we change the image of the issue. What would have a more positive connotation than a skunk? 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.
Options for novel
approaches: The case of CorningOptions for novel approaches: The case of Corning Cable Systems 1 Align MDM with the company’s culture of quality management Proof of concept for customer master data creation in NAFTA customer master life cycle Transferring TQM principles to MDM 1 Master data as an asset Establish business-oriented data quality metrics Data life cycle / Retirement process Managing cost and value of master data 2 Data life cycle / Retirement process Buy-in for MDM organization from data owners still missing Continuous roll-out of roles and responsibilities in MDM Global Data Governance rollout 3 Implementation of a shared MDM Service Data Governance rollout Knowledge capitalization on an organization and system levelGlobal leveraging of 4 g p g y Foundation of a global center for excellence g g knowledge assets 5 Technical integration/substitution of ASI, Windchill with SAP Extend workflow from material master to other domains System integration and process automation © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 18
19.
Existing Data Management
expertise is a crucial factor for identifying aExisting Data Management expertise is a crucial factor for identifying a need for action during the establishment of Data Governance © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 19
20.
The maturity model
is an instrument for controllingThe maturity model is an instrument for controlling Data Governance effectiveness Strategy Controlling Organization Applications Data Processes & Methods Data Architecture Legend: Current value 2010 Target value 2011 (= one maturity level for all enablers) © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 20
21.
RWE benefits from
EUR 2.6 million costs avoidedRWE benefits from EUR 2.6 million costs avoided Average cost rate Number of Number of Value = per master data record* X ( duplicate records + new records avoided ) Backward Forward * 2,861 EUR (2006). Source: Holzapfel, T. Harmonisierung Von Materialstammdaten - Eine Herausforderung. Proceedings of the Stammdaten-Management Forum (Frankfurt 2007 09 27) IIR Deutschland 2007 © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 21 Forum (Frankfurt, 2007-09-27). IIR Deutschland, 2007.
22.
Some more tangible
benefit examplesSome more tangible benefit examples Savings of 2 percent of actual value in stockSavings of 2 percent of actual value in stock More 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.
Contact DetailsContact Details Prof.
Dr. Boris Otto Assistant Professor University of St. Gallen Boris.Otto@unisg.ch Tel.: +41 71 224 32 20 http://cdq.iwi.unisg.ch © BEI St. Gallen – Berlin, February 16, 2012, Dr. Boris Otto / 23
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