Data Quality Management: Framework and Approach for Data Governance

Data Quality Management: Framework and Approach for Data pp Governance 5th German Information Quality Management Conference Dr. Boris Otto Agenda Introduction to University of St. Gallen and the Research Context Business Drivers for Data Quality Management Data Governance: Design Elements and Implementation Summary Data Governance Bad Soden, November 22nd, 2007 © IWI-HSG / CC CDQ 2 Introduction to University of St. Gallen (HSG) Founded in 1898 5,000 5 000 students on bachelor bachelor, master, and doctorate level Two thirds of the students in business administration Largest business administration faculty in S it l d f lt i Switzerland 80 professors, 35 permanent lecturers, 260 associate lecturers 30+ institutes 1,000 1 000 staff 50 per cent industry funding Data Governance Bad Soden, November 22nd, 2007 © IWI-HSG / CC CDQ 3 Project partners of the Competence Center Corporate Data Quality (CC CDQ) Bayer CropScience AG Daimler D i l AG Monheim, Germany Stuttgart, Germany DB Netz AG Deutsche Telekom AG Frankfurt am Main, Germany Bonn, Germany ETA SA Manufacture Horlogère Suisse ZF Friedrichshafen AG Grenchen, Switzerland Friedrichshafen, Germany IBM Deutschland GmbH Stuttgart, Germany Data Governance Bad Soden, November 22nd, 2007 © IWI-HSG / CC CDQ 4 CC CDQ research scope in the context of Business Engineering Strategy Data Quality Strategy y gy Processes Management Reporting Organisation and Data Management Standards Processes Maturity and Cross-company Transition Networks local global Data Architecture System Architecture Systems Data Governance Bad Soden, November 22nd, 2007 © IWI-HSG / CC CDQ 5 Agenda Introduction to University of St. Gallen and the Research Context Business Drivers for Data Quality Management Data Governance: Design Elements and Implementation Summary Data Governance Bad Soden, November 22nd, 2007 © IWI-HSG / CC CDQ 6 Business drivers for data quality management can be found literally across the entire enterprise Poor data quality causes “blurry” management decisions Corporate Management/ p g No single point of truth Business Intelligence Manual effort necessary during report creation Legal and regulatory risks through bad or incomplete corporate data Compliance Contractual breaches and liability cases likely Common material and partner data as a mandatory pre-requisite for pre requisite Process Integration efficient order-to-cash and procure-to-pay processes along the Value Chain Necessity to establish unique data integration methodologies One-face-to-the-customer requires consistent and sustainable customer Customer-centric and contract data management Business Models Data integration necessary on business unit and regional level Customers and business partners demand high-quality electronic product Electronic Product information Information Information lifecycle management from F&E to Sales & Distribution Data Governance Bad Soden, November 22nd, 2007 © IWI-HSG / CC CDQ 7 Consequences of poor data quality Inaccurate reporting 81 Arguments over which data is appropriate or trusted A t hi h d t i i t t t d 78 Bad decisions based on incorrect definitions 54 Data governance and stewardship limitations 53 Limited visibility  for data  lineage and linkage 52 No understanding of master  data homonyms,  synonyms 46 Poor customer service 35 Inefficient marketing 32 Inefficient purchasing/sourcing 18 Delay in new product introductions 17 Other Oth 8 0 10 20 30 40 50 60 70 80 90 100 Source: Russom, P.: Master Data Management - Consensus-Driven Data Definitions , g for Cross-Application Consistency. The Data Warehousing Institute, 2006 Data Governance Bad Soden, November 22nd, 2007 © IWI-HSG / CC CDQ 8 Agenda Introduction to University of St. Gallen and the Research Context Business Drivers for Data Quality Management Data Governance: Design Elements and Implementation Summary Data Governance Bad Soden, November 22nd, 2007 © IWI-HSG / CC CDQ 9 Data quality management in a corporate-wide context Companies have difficulties with institutionalizing data quality management g y g • Definition of responsibilities • Integration in organizational structure • Enforcement of mandates Different internal and external stakeholders with divergent interests • CxOs, IT, Sales, Procurement, Controlling, Business Units, Customers etc. • Company-wide requirements, laws and regulations vs. regional and local differences • Separation of data maintenance and data usage Data Governance Bad Soden, November 22nd, 2007 © IWI-HSG / CC CDQ 10 Data quality management without data governance Strassmann, P.