Data Quality as a Business Success FactorProf. Dr. Boris Otto, Assistant ProfessorEnschede, April 5, 2012Chair of Prof. Dr...
Case A looks at one of the business drivers of data quality at leading automotivesupplier ZF Friedrichhafen AG«Starting in...
At ZF OEM1 Relationship Management requires consistent and accurate masterdata about vehicles, customers, products across ...
Data quality is necessary to respond to strategic business requirements  1    Customer-Centric Business Models  $    Value...
The typical evolution of data quality over time does not live up to its businessrelevance              Data Quality       ...
Case B analyzes root causes of poor data quality at Bayer CropScience                                  People             ...
Corporate Data Quality Management (CDQM) is a Business Engineering task andrelates to a company’s business strategy, organ...
The EFQM Excellence Model for CDQM1 was collaboratively developed by EFQM,the University of St. Gallen, and partners from ...
The Competence Center Corporate Data Quality (CC CDQ) is a consortiumresearch project involving 22 partner companies      ...
Material master data quality has continuously been improved at BayerCropScience (Case B)                      © CC CDQ – E...
Data quality leads to tangible business benefits                            Savings of 2 percent of average inventory valu...
CC CDQ Resources on the InternetInstitute of Information Management at the University of St. Gallenhttp://www.iwi.unisg.ch...
Please reach out to me in case of questions and commentsProf. Dr. Boris OttoAssistant Professor & Head of CC CDQUniversity...
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Data Quality as a Business Success Factor

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  1. 1. Data Quality as a Business Success FactorProf. Dr. Boris Otto, Assistant ProfessorEnschede, April 5, 2012Chair of Prof. Dr. Hubert Österle
  2. 2. Case A looks at one of the business drivers of data quality at leading automotivesupplier ZF Friedrichhafen AG«Starting in January 2010, the Services business unit will additionallypool the global customer service activities of the Group. In doing so,the Services departments at German division and business unitlocations will be organizationally merged with the worldwide Servicescompanies. With this new structure, ZF has established a systematicapproach in the after-sales market.»ZF Friedrichshafen AG: Annual Report 2009, p. 64. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 2
  3. 3. At ZF OEM1 Relationship Management requires consistent and accurate masterdata about vehicles, customers, products across the organization Real world view Sales, Business Engineering Projects Logistics, process view Controlling Application System View Axalant SAP cProjects SAP ERP VW Group Audi AUDI AG Data View B8 AU416 PL481) OEM - Original Equipment Manufacturer. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 3
  4. 4. Data quality is necessary to respond to strategic business requirements 1 Customer-Centric Business Models $ Value Chain Excellence § Contractual and Regulatory Compliance © CC CDQ – Enschede, April 5, 2012, Boris Otto / 4
  5. 5. The typical evolution of data quality over time does not live up to its businessrelevance Data Quality Legend: Data quality “Submarines” (e.g. migrations, process errors, irregularities in management reporting). Time Project 1 Project 2 Project 3  No risk management possible  No chance to plan and to control budgets and resources  No target values for corporate data quality  No sustainability  High recurring project costs (change requests, external consultants etc.) © CC CDQ – Enschede, April 5, 2012, Boris Otto / 5
  6. 6. Case B analyzes root causes of poor data quality at Bayer CropScience People People Data Maintenance Data Maintenance Maintenance processes are not fully supported No sufficient by existing toolset training and / or Heterogeneous set education of data maintenance tools Master Data maintenance processes Data Quality KPIs not globally harmonized Master Data not are not part of and optimized protected in all personal objectives operational systems Low / Not sustainable Poor Data Data Quality Quality Only very few No globally accepted No empowered Data Quality KPIs set of rules, standards, Data Governance defined policies, guidelines organizationNo continuous Gaps in business Too many rules,monitoring of responsibility for even more exceptions Data Quality Master Data objects Data Quality Processes Data Quality Process Standards Standards Organization OrganizationLegend: KPI - Key Performance Indicator.Source: Brauer, B. (2009). Master Data Quality Cockpit at Bayer CropScience. Paper presented at the 4th Workshop of the Competence Center Corporate Data Quality 2,Lucerne. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 6
  7. 7. Corporate Data Quality Management (CDQM) is a Business Engineering task andrelates to a company’s business strategy, organization, and information systems Mandate StrategyStrategy document Goals and targets Strategy for CDQMValue management Data quality metrics Action plan Organization CDQ Controlling Data Governance Data life cycle management Roles and responsibilities Business metadata management Change Organization CDQM Processes and management Data-driven business for CDQM Methods process management Standards & Guidelines local global Conceptual corporate data Software support (e.g. model MDM applications) Data distribution Corporate Data Architecture System landscape architecture analysis and planning Authoritative data sources Applications for CDQM System © CC CDQ – Enschede, April 5, 2012, Boris Otto / 7
  8. 8. The EFQM Excellence Model for CDQM1 was collaboratively developed by EFQM,the University of St. Gallen, and partners from industry CDQM Maturity Assessment Strategy Controlling Applications Organization Data Architecture Processes & Methods Legend: Current value 2010 Target value 2011 (= one maturity level for all enablers)1) EFQM: EFQM Framework for Corporate Data Quality Management: Assessing the Organization’s Data Quality Management Capabilities, EFQM Press, Brussels, 2011 © CC CDQ – Enschede, April 5, 2012, Boris Otto / 8
  9. 9. The Competence Center Corporate Data Quality (CC CDQ) is a consortiumresearch project involving 22 partner companies AO FOUNDATION ASTRAZENECA PLC BAYER AG BEIERSDORF AGCORNING CABLE SYSTEMS GMBH DAIMLER AG DB NETZ AG E.ON AG ETA SA FESTO AG & CO. KG HEWLETT-PACKARD GMBH IBM DEUTSCHLAND GMBHKION INFORMATION MANAGEMENT MIGROS-GENOSSENSCHAFTS-BUND NESTLÉ SA NOVARTIS PHARMA AG SERVICE GMBH SIEMENS ENTERPRISE ROBERT BOSCH GMBH SAP AG SYNGENTA CROP PROTECTION AG COMMUNICATIONS GMBH & CO. KG TELEKOM DEUTSCHLAND GMBH ZF FRIEDRICHSHAFEN AG NB: Overview comprises both current and past research partner companies. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 9
  10. 10. Material master data quality has continuously been improved at BayerCropScience (Case B) © CC CDQ – Enschede, April 5, 2012, Boris Otto / 10
  11. 11. Data quality leads to tangible business benefits Savings of 2 percent of average inventory value p.a.1 More than GBP 500 million saved through retrieval of «lost assets»2 CHF 3,000 saved per obsolete master data record31) Benefit assessment as a result from a series of expert interviews at one of the CC CDQ partner companies.2) Otto, B.; Weber, K.: From Health Checks to the Seven Sisters: The Data Quality Journey at BT, University of St. Gallen, Institute of Information Management, St. Gallen, 2009.3) Lay, J. (2008). Produktdaten im ERP. Paper presented at the Stammdatenmanagement-Forum 2008, Rapperswil. © CC CDQ – Enschede, April 5, 2012, Boris Otto / 11
  12. 12. CC CDQ Resources on the InternetInstitute of Information Management at the University of St. Gallenhttp://www.iwi.unisg.chBusiness Engineering Institute St. Gallenhttp://www.bei-sg.chCompetence Center Corporate Data Qualityhttp://cdq.iwi.unisg.chCC CDQ Benchmarking Platformhttps://benchmarking.iwi.unisg.ch/CC CDQ Community at XINGhttp://www.xing.com/net/cdqm © CC CDQ – Enschede, April 5, 2012, Boris Otto / 12
  13. 13. Please reach out to me in case of questions and commentsProf. Dr. Boris OttoAssistant Professor & Head of CC CDQUniversity of St. GallenInstitute of Information ManagementSwitzerland+41 71 224 32 20boris.otto@unisg.ch © CC CDQ – Enschede, April 5, 2012, Boris Otto / 13
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