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Competence Center Corporate Data Quality
 

Competence Center Corporate Data Quality

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Competence Center Corporate Data Quality Competence Center Corporate Data Quality Presentation Transcript

  • Competence Center Corporate Data Quality (CC CDQ): Overview Presentation Dr. Boris Otto St. Gallen, February 2008 cdq.iwi.unisg.ch
  • Table of Content
    • Background
    • Objectives and Organisation
    • Research Scope and Preliminary Results
    • Modus Operandi
    • Contact
  • Business drivers for corporate data quality are spread across the entire enterprise Corporate Management/ Business Intelligence Compliance Process Integration along the Value Chain Customer-centric Business Models
    • Poor data quality causes “blurry” management decisions
    • No single point of truth
    • Manual effort necessary during report creation
    • Legal and regulatory risks through bad or incomplete corporate data
    • Contractual breaches and liability cases likely
    • One-face-to-the-customer requires consistent and sustainable customer and contract data management
    • Data integration necessary on business unit and regional level
    • Common material and partner data as a mandatory pre-requisite for efficient order-to-cash and procure-to-pay processes
    • Necessity to establish unique data integration methodologies
    Strategic Purchasing
    • Cross-divisional spend analysis and supplier evaluation requires a consistent and integrated material groups and supplier hierarchies
    • Data transparency is a prerequisite for supplier development processes
  • Corporate Data Quality Management (CDQM) is about responding to the following questions
    • How does CDQM contribute to the company‘s strategy ?
    • How to measure performance of CDQM ? How do I know whether we do a good job?
    • How much does it cost to manage global master data?
    • How to align common standards and rules for CDQM across the organization?
    • How do we achieve a common understanding on the global corporate data objects? How can we keep it up-to-date?
    • Which tools are available to manage our metadata ?
    • Our application landscape has been grown historically; what are the leading systems for the most important data?
    • Which objects must be defined unambiguously across the company? Which ones are local? How to find the right degree of harmonization ?
  • A framework for Corporate Data Quality Management (CDQM) addresses strategic, organizational, and system aspect Strategy Organization Systems CDQ Controlling Applications for CDQ Information Architecture for CDQ CDQ Organization CDQ Operations Strategy for CDQ local global Business Networking Maturity & Transition
  • The project consortium consists of companies working on the same topics being confronted with comparable challenges CH D Berlin Köln Hamburg München Frankfurt Zürich ETA S.A Grenchen IWI-HSG St. Gallen Daimler AG Stuttgart IBM Deutschland GmbH Stuttgart Bayer CropScience AG Monheim/Rh. ZF Friedrichshafen AG Friedrichshafen DB Netz AG Frankfurt a. M. Deutsche Telekom AG Darmstadt E.ON AG München
  • The array of project results addresses a variety of questions
    • Guidelines for Data Governance
    • Maturity Model for CDQM
    • Market Survey on CDQ Tools
    • Case Studies
    • Methodology to Increase Transparency on Information Objects
    • Proof of Concepts for Business Data Dictionaries based on Web 2.0 and Semantic Web
  • Result example: Baseline Assessment for CDQM in the consortium according to CMM-I Implementation of a CDQ strategy Measurement and control of CDQ CDQ organisation and standards setting Execution of CDQM processes Provision of systems support for CDQ Partner A Partner B Partner C Partner D 1 – Initial 2 – Managed 3 – Defined 4 – Quantitatively Managed 5 – Optimizing Partner E 1 2 3 4 5
  • Result example: Data Governance at a national infrastructure provider in Europe Key: Z - Zustimmung, E - Entscheidung, F - Federführung, M - Mitwirkung, D - Durchführung. VR - Vorstandsressort, GF - Geschäftsfeld, DM - Datenmanagement, GE - Geschäftseinheit, FDM - Fachlicher Datenmanager. NB: Illustration in German in accordance with bilaterla project language. Rolle / Beteiligter VR Vorstand GF DM Board GF Daten-manager DM Fach-daten-steward Oper. Daten-manager GE-FDM GE-FDM GE-FDM Aufgabe   Bereich 1 Bereich 2 Bereich n Entwickelung DM-Strategie Z Z E F M M M M M Aufbau DM-Führungssystem Z Z E F F M M M M Entwicklung Data-Governance-Modell Z Z E F F M M M M Entwurf Datenproduktions- und Datenbereitstellungs-prozesse Z   E F D D D D D Aufbau DM-Datenkatalog     E F F M D D D Entwickeln DM-Datenmodell Z   E F F M M M M Fachliche Vorgaben für die Anwendungsentwicklung Z   Z E M F M M M
  • Case study: KPIs for CDQM in the retail industry Source: Schemm, J.; Otto, B.: Stammdatenmanagement bei der Karstadt Warenhaus GmbH, Institut für Wirtschaftsinformatik, Universität St. Gallen, St. Gallen, 2007. NB: Illustration in German in accordance with original paper language. Kennzahl Bezugsgrösse Berechnungsvorschrift Ebene Periodizität Formatwechsel Wert (Absolutwert der Formatwechsel) / Verbrauch * 100 Filiale, Abteilung Monatlich Pseudo-Bepo Wert (Wert Pseudo-Bepo) / Wert Bepo Gesamt * 100 Filiale, Abteilung Monatlich Minusbestand Anzahl (Anzahl Bepo ohne Bestand) / (Anzahl Bepo Gesamt) * 100 Filiale, Abteilung Monatlich Inventurbestand ohne Bestellpositionen Wert (Inventurbestand ohne Bepo) / Inventurbestand * 100 Filiale, Abteilung Jährlich EK-Differenzen Anzahl (Anzahl fehlerhafte Repo) / (Anzahl Repo Gesamt) * 100 Abteilung Monatlich Rechnungen ohne Auftrag Anzahl (Anzahl Rechnungen ohne Auftrag) / (Anzahl Rechnungen Gesamt) * 100 Filiale, Abteilung Monatlich Fehlerlisten Anzahl (Absolutwert der Menge mit Fehlern) / (Absolutwert der Gesamtmenge) * 100 Filiale, Abteilung Monatlich Stapf-Korrekturen Wert Wert der nachträglichen Ergebniskorrekturen Filiale, Abteilung Monatlich
  • Result example: Benefit tree for CDQM Human Resource Management Inbound Logistics Procurement Operations Outbound Logistics Marketing & Sales Service Technological Development Firm Infrastructure Profit Profit Support Activities Primary Activities
  • A common understanding on the company-wide information objects is key to successful CDQM Information Objects Information Object Information Object Information Object Information Object Information Object Business Objects Attribute Attribute Attribute Attribute Attribute Attribute Attribute Data Objects Process Layer (conceptual) System Layer (physical) Integration LAyer (logical) Process A Process B Plate Büromaterial Vertriebs GmbH Hilligenwarf 5 28865 Lilienthal
  • The first cycle of the CC CDQ ends in October 2008 - a second cycle is planned to run until 2010 23.11.06 (Frankfurt): Kick-off Workshop 01./02.02.07 (Darmstadt): Baseline Assessment and Data Governance 24./25.02.07 (Esslingen): Theoretical Foundations, Business Data Dictionary, Scorecarding 25./26.06.07 (Basel): Business Alignment and Business Case 20./21.09.07 (Leverkusen/Monheim): Data Governance 15./16.11.07 (Berlin): Business Value &Meta Data Management 16./17.01.08 (St. Gallen): Data Architecture & OCM 02./03.04.08 (nn): Data Management Processes 18./19.06.08 (nn): Change Management for CDQ 03./04.09.08 (nn): System Support and Architecture for CDQ 29./30.10.08 (nn): Final Workshop Key: completed still to come. OCM - Organizational Change Management. CC CDQ2 2006 2007 2008 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 09 09 10
  • The CDQ network keeps growing… CC CDQ Guest Speeches CDQ Market Survey Scientific Community Case Studies Practical Community Awareness
  • The standard CC CDQ participation packages covers research results, bilateral support and workshop participation 6 to 8 working reports Approx. 35 person days p.a. 5 to 6 workshops p.a.
  • The CC CDQ team at the chair of Prof. Dr. Hubert Österle Head of CC CDQ Scientic coordination and head of the institute Scientific researchers and PhD students Dr. Boris Otto Prof. Dr. Hubert Österle Kai Hüner Alexander Schmidt Tobias Vogel Kristin Wende
  • Selected feedback from participants of the 6 th CC CDQ workshop „ Wir möchten uns bei Ihnen und Ihrem Team für die Möglichkeit zur Präsentation bedanken! Es war ein sehr spannender Workshop mit vielen interessanten Teilnehmern und neuen Erkenntnissen! Ein schönes Rahmenprogramm und die tolle Organisation haben das Event perfekt abgerundet!“ --- Wiebke Hedlefs, Deutsche Börse AG „ Professional organization in all means of the 2 days“ „ Participant are all professional in DQ“ „ Good mixture of companies all working in the same area as we do“ “ Content is not technical but business oriented, very pragmatic” „ I strongly recommend o join the group.“ --- Karsten Muthreich, Nestle S.A. „ Gratulation zu einem gelungenen Workshop an das ganze Team“ --- Albert Hatz, Robert Bosch GmbH
  • The CC CDQ objectives combine research excellence with practical application
    • Strengthening management and business awareness for data quality and improving its business alignment
    • Developing proven concepts to improve corporate data quality
    • Evaluating innovative technologies and approaches - such as Semantic Web, Social Software, Service-oriented Architecture - on their benefits for corporate data quality
    • Facilitating exchange of experiences and knowledge between partners and developing best practices
    • Supporting data quality initiatives within the partner companies
  • Your contact for further information
    • University of St. Gallen
    • Institute of Information Management
    • [email_address]
    • http://cdq.iwi.unisg.ch
    • Dr. Boris Otto
    • ++41 71 224 32 20
    • [email_address]
    • Kai Hüner
    • Alexander Schmidt
    • Tobias Vogel
    • Kristin Wende