Corporate Data Quality: Research and Services Overview

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This presentation gives an overview of the research in the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen in Switzerland and the service portfolio in the field of corporate data quality of the Business Engineering Institute (BEI) St. Gallen.

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Corporate Data Quality: Research and Services Overview

  1. 1. Corporate Data QualityResearch and Services OverviewProf. Dr. Boris Otto, Assistant ProfessorSt. Gallen, March 2012Chair of Prof. Dr. Hubert Österle
  2. 2. Competence Area Corporate Data Quality Competence Center Business Engineering Corporate Data Quality Institute St. Gallen AG Applied Consortium Research Business Value Transformation© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 2
  3. 3. Table of Content Data Quality as a Success Factor for Business Competence Center Corporate Data Quality BEI Project References Team Overview© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 3
  4. 4. Data quality is necessary to respond to a number ofstrategic business requirements 1 Customer-Centric Business Models $ Value Chain Excellence Contractual and Regulatory Compliance© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 4
  5. 5. Complexity drivers pose challenges on data quality management Size “Big Data” Revenue Nestlé 2010: 110 billion CHF RFID, customer loyalty programs Federal budget CH 2008: 57 billion CHF etc. “Hyper-Connectivity” Corporate Globalized OperationsSocial media, data supply chains Data Multilingualism, “Follow the sun“- etc. Quality principle etc. Constant Change “Taylorism” M&A, “Divestments”, Change Segregation of data creation and Management data use © BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 5
  6. 6. Today, companies manage data quality purely in areactive mode Data quality : “Submarines” of data quality, e.g. data migration, incorrect reports, process errors). Project 1 Project 2 Project 3 Time  No risk management possible  No chance to plan and to control budgets and resources  No target values for corporate data quality  No sustainability of increased data quality  High recurring project costs (change requests, external consultants etc.)© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 6
  7. 7. Costing for data quality must find a trade-off betweenpreventive and reactive measures Costs (C) C Total costs of data quality Costs related to DQM Follow-up costs in business as a result of data defects DQ DQM: Data quality management Cost-optimal Data quality data quality level (DQ) Otto, B., Hüner, K., Österle, H.: A Cybernetic View on Data Quality Management, AMCIS 2010 Proceedings, Peru, 14.08.2010, 2010, http://aisel.aisnet.org/amcis2010/423© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 7
  8. 8. Table of Content Data Quality as a Success Factor for Business Competence Center Corporate Data Quality BEI Project References Team Overview© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 8
  9. 9. The Competence Center Corporate Data Quality (CCCDQ) responds to urgent issues How does Corporate Data Quality contribute to the strategic business objectives? How does our company compare to others in our peer group? How can we measure our performance in Corporate Data Quality Management? What are the costs and benefits of Corporate Data Quality? How can we establish Data Governance in the company? What is the appropriate degree of standards and regulation for our company? How do we achieve consistent understanding of corporate data? What is the baseline of Corporate Data Quality? Which data architecture is the right one and how do we implement it? How do we benefit from innovative technologies (e.g. Social Media, Linked Data)?© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 9
  10. 10. The consortium comprises more than 20 researchpartner companies AO FOUNDATION ASTRAZENECA PLC BAYER AG BEIERSDORF AG CORNING CABLE SYSTEMS GMBH DAIMLER AG DB NETZ AG E.ON AG ETA SA FESTO AG & CO. KG HEWLETT-PACKARD GMBH IBM DEUTSCHLAND GMBH KION 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.© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 10
  11. 11. The CC CDQ Framework in the context of BusinessEngineering Mandate Strategy Strategy document Goals and targets Strategy for CDQ Value management Data quality metrics Roadmap Organization CDQ Controlling Data life cycle Data Governance management Roles and Business metadata responsibilities management Change Organization CDQ Processes and Data-driven management for CDQ Methods business process Standards & management Guidelines local global Conceptual Software support corporate data (e.g. MDM model applications) Data distribution Corporate Data Architecture System landscape architecture analysis and Authoritative data planning sources Applications for CDQ System© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 11
  12. 12. Achieved results provide a “tool box” for establishingCorporate Data Quality Management EFQM Excellence Model for Corporate Data Quality Management Method for specifying business-relevant data quality metrics Reference model for Data Governance Method for establishing Data Governance Analysis and modeling method for integrating data quality in business process management Method for master data integration Design patterns for data architecture Reference model for Master Data Quality Management software© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 12
  13. 13. The CC CDQ research service portfolio rests on threepillars I II III Research on Network & Bilateral Project Demand Benchmarking  Full access to the CC  5 two-day consortium  Individual CDQ maturity CDQ knowledge pool workshops p.a. assessment  Customized research  In-depth benchmarking  Individual project results studies groups (e.g. data governance  Case studies within the  Moderation and co- design, metric design, peer group ordination of peer group data architecture  “Best practice” analysis)  Analysis of the state of presentations  Moderation of internal the art in research and  Access to a network of workshops practice CDQ professionals  Training and knowledge  Active participation in  Access to highly-qualified transfer (in-house leading edge research PhD students and seminars etc.)  Leveraging a global graduate students  Individual support of CDQ research network  Use of professional programs platform (seminars, lectures etc.)© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 13
  14. 14. Table of Content Data Quality as a Success Factor for Business Competence Center Corporate Data Quality BEI Project References Team Overview© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 14
  15. 15. BEI is a trusted partner for designing and implementingCorporate Data Quality strategies  Master data processes Bühler AG  Software evaluation  Master data strategy Drägerwerke AG & Co. KGaA  Data governance  Implementation roadmap Elektrizitätswerke des Kantons  Maturity assessment Zürich  Data quality metrics  Master data strategy LIDL Stiftung & Co. KG  Data governance  Implementation roadmap OTTO Group  Master data strategy  Conceptual data model RWE IT GmbH  Data architecture Stadtwerke München  Maturity assessment SWM Services GmbH  Maturity assessment Swisscom IT Services AG  Master data strategy© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 15
  16. 16. Table of Content Data Quality as a Success Factor for Business Competence Center Corporate Data Quality BEI Project References Team Overview© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 16
  17. 17. The combined team at IWI-HSG and BEI leveragessound research and consulting expertise IWI-HSG Prof. Dr. Dr. Boris Otto Verena Ebner Clarissa Falge Ehsan Baghi Hubert Österle BEI Dr. Dimitrios Dr. Kai Hüner Martin Ofner Andreas Max Wolfgang Peter Mayer* Gizanis Reichert Zurkinden Dietrich© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 17
  18. 18. Customers and partners benefit from an unmatchedpool of knowledge and expertise 850+ Contacts in the overall CC CDQ community 150+ Members in the XING Community 140+ Bilateral Project Workshops 70+ Best Practice Presentations 28 Consortium Workshops 22 Partner Companies 13 Scientific Researchers/PhD Students 1 Competence Center© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 18
  19. 19. CC CDQ Resources on the Internet Institute of Information Management at the University of St. Gallen http://www.iwi.unisg.ch Business Engineering Institute St. Gallen http://www.bei-sg.ch Competence Center Corporate Data Quality http://cdq.iwi.unisg.ch CC CDQ Benchmarking Platform https://benchmarking.iwi.unisg.ch/ CC CDQ Community at XING http://www.xing.com/net/cdqm© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 19
  20. 20. Contact Details Dr.-Ing. Boris Otto University of St. Gallen Institute of Information Management Boris.Otto@unisg.ch Tel.: +41 71 224 32 20 http://cdq.iwi.unisg.ch© BEI St. Gallen – St. Gallen, March 2012, Dr. Boris Otto / 20

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