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Data Governance – From Local Optimization to Outsourcing

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Master Data Management Summit Europe 2015 & Data Governance Conference Europe 2015
(May 18-21, 2015 London)

Publié dans : Direction et management
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Data Governance – From Local Optimization to Outsourcing

  1. 1. © BEI St. Gallen – 2015, A. Reichert / 1
  2. 2. Data Governance From Local Optimisation to Outsourcing Dr. Andreas Reichert London, May 2015
  3. 3. © BEI St. Gallen – 2015, A. Reichert / 3 IT Innovations Transformation of the Enterprise Business Engineering Institute – From research to consulting services http://de.wikipedia.org/wiki/Business_Engineering Competence Center Corporate Data Quality Business Engineering Institute St. Gallen AG
  4. 4. © BEI St. Gallen – 2015, A. Reichert / 4 The Competence Center Corporate Data Quality (CC CDQ) comprises more than 30 partner companies
  5. 5. © BEI St. Gallen – 2015, A. Reichert / 5 Agenda  Business Rationale for Data Governance  Data Governance Design Options
  6. 6. © BEI St. Gallen – 2015, A. Reichert / 6 Data Governance is necessary in order to meet several strategic business requirements  Legal and regulatory requirements  Contractual obligations Risk Management  “Single Point of Truth”  Standardized reports and KPIs Corporate Reporting  Business process harmonization  “End-to-end” business processes Global Business Processes  360°view on customers  Hybrid products Customer-centric business models  Integration of acquired businesses  Data due diligence Mergers & Acquisitions  IT consolidation (“do more with less”)  Flexible architectures Complexity management 1 2 3 4 5 6
  7. 7. © BEI St. Gallen – 2015, A. Reichert / 7 Business impact of data quality? A product data example, consumer goods industry GTIN: Global Trade Item Number, standardized by Global Standards One (GS1, www.gs1.org) 1 2 3 4 5 2  To add additional filling may be reasonable with transparent bottles  But: Not maintaining changed gross weight my cause wrong packing Capacity2  Wrong shelf planning at customers (retail) due to inaccurate measures  Repacking of pallets due to inaccurate gross weights Logistic Data 1  Flawed products due to too high or too low temperature during transport  Temperature tolerance depends on product formula (bill of material) Temperature for transportation 3  Different formats in several countries  No globally standardized but changing formats (e.g. date, duration) Format of expiry date 4  Wrong GTINs may cause complaints and compensations  Product changes may require a new GTIN  GTIN allocation depends on global and local guidelines GTIN5 Data quality is a prerequisite for correct product information and supply chain efficiency
  8. 8. © BEI St. Gallen – 2015, A. Reichert / 8  Data governance aims at the identification of decision rights and roles to facilitate a consistent, company-wide behavior in the use of corporate data  Also, data governance allocates responsibilities to roles to ensure the execution of assigned decision rights  Data governance results in company-wide standards, guidelines and methodologies for creation and use of corporate data Management of sustainable and reliable high quality master data Defining Data Governance
  9. 9. © BEI St. Gallen – 2015, A. Reichert / 9 Legend: Data quality pitfalls (e. g. Migrations, Process Touch Points, Poor Management Reporting Data. Data Quality Time Project 1 Project 2 Project 3  No risk management possible  Impedes planning and controlling of budgets and resources  No targets for data quality  Purely reactive - when too late  No sustainability, high repetitive project costs (change requests, external consulting etc.) The typical evolution of data quality over time in companies shows a strong need for action
  10. 10. © BEI St. Gallen – 2015, A. Reichert / 10 The CDQ Framework – Success Factors for effective Data Governance Strategy Strategy System Applications Data Architecture local global Organization Controlling Processes and Methods Organization and People Mandate Strategy Document Value Management Roadmap Data Governance Roles and Responsibilities Change Management Standards & Guidelines Conceptual Corporate Data Model Distribution Architecture Data Storage Architecture KPI System Measurement Process Dimensions of Data Quality Data Lifecycle Management Metadata Management Methods and Processes Software for Corporate Data Quality Management As-is and To-be- planning of application system support
  11. 11. © BEI St. Gallen – 2015, A. Reichert / 11 Design options for implementing Data Governance Key: BU: Business Unit; SSC: Shared Service Center Line Organization (Sold Line) Dotted Line Coordination via SLA Local Function/Staff Organization per BU Central Function Shared Service Center Externalization Group Level BU BU BU BU Group Level BU BU BU Central Function Group Level BU BU BU External Party Group Level BU BU BU SSC 1 2 3 4
  12. 12. © BEI St. Gallen – 2015, A. Reichert / 12 Agenda  Business Rationale for Data Governance  Data Governance Design Options
  13. 13. © BEI St. Gallen – 2015, A. Reichert / 13 Example 1 - High Tech Industry Business drivers for Data Governance  Changing business model  From product & system business to solution orientation  Focus on indirect business models  Trend to managed services  Higher competition leads to higher cost pressure  Need to simplify and harmonize processes and IT  Need to simplify and strengthen the organization  Changes in the market require high flexibility  Reduce the complexity in products and services  Enable rapid merger and acquisitions Accurate and trustful master data are the basis for business processes and enable to react flexible on changes!
  14. 14. © BEI St. Gallen – 2015, A. Reichert / 14 The need for high quality master data for the new business environment to GRID The GRID (Global Responsibility for Integrated Data) initiative aims at setting up a global Enterprise Data Management (EDM) consisting of governance (organizational structures, roles, responsibilities, tasks), processes (data management, business processes) as well as the information technology (systems, interfaces, automation). GRID has the mission to secure the global consistency of master data – product, product information, supplier, customer - in order to smoothly operate the business.
  15. 15. © BEI St. Gallen – 2015, A. Reichert / 15 Why do we need global master data Governance? BusinessprocessesCorporate Enterprise Data Management is the backbone of the business processes! Global planning capabilities & integration of 3rd party products Efficient marketing and e-commerce enablement (e2e) Clean & full integration of service business into MDM Spend transparency and volume consolidation SCM Mark / Sales Service Purchasing Information Compliance Projects High reporting quality and timely reporting Traceability of products and export compliance Acceleration of project delivery and reduction of efforts
  16. 16. © BEI St. Gallen – 2015, A. Reichert / 16 Processes are defined on strategic, governance, and operational level EDM Life Cycle Management EDM Life Cycle Management Customer EDM Life Cycle Management EDM Life Cycle Management EDM Strategy 1 EDM Standards & Guidelines Develop vision Define EDM roadmap Develop com./change strategy Set up organization responsib. Align with business/IT strategy EDM Quality- Assurance Define measure- ment metrics Define quality targets Define reporting structures Monitor & report 2 3 Define nomen- clature Define lifec. processes Define authoriza- tion concept Define & roll out lifecycle procedures EDM Data Model 4 Detect requirements for model Analyze implication of changes Model master data Test master data model changes GovernanceStrat. EDM Architecture 5 Detect requirements for arch. Analyze implication of changes Model data architecture Roll out EDM architecture Implement workflows/ UIs Implement measure- ment metrics Roll out data model changes Model workflows / UIs EDM Support 7 Provide trainings Provide business support Provide project support EDM Life Cycle Management 6 Operations Source /approve information Deploy master data Archive master data Create master data Maintain master data Executed by EDM organization Governed by EDM organization Mass data changes Business object specific tasks and responsibilities Common tasks Tasks and responsibilities of different business objects (e.g. supplier, customer, etc.) may differ on the operational level. SupplierSupplier CustomerCustomer ……
  17. 17. © BEI St. Gallen – 2015, A. Reichert / 17 Roles are defined on strategic, governance, and operational level Governance Level Operational Level Strategic Level Set strategic direction of EDM and ensure alignment with business and IT strategy. Define and control standards and guidelines for enterprise data according to the business requirements. Request, create, maintain and approve enterprise data following defined standards and guidelines. Establish technical readiness of IT systems. EDM Community EDM Board Head of IT Business Data Steward Technical Data Steward Executive Sponsor Head of EDM Corporate Data Operator Business process owner EDM organization Other organization Global roles Global or regional roles
  18. 18. © BEI St. Gallen – 2015, A. Reichert / 18 Solution – Data Governance as central function Interaction Head of EDM Strategiclevel Governance/ Operationallevel Business processes EDM EDM-Board Operative in SAP Business Process Owner Business Process Owner Data OwnerCorporate Data Operator Communicate / improve standards Define standards Business Data Steward Business Data Steward Enforce standards during data update Align process / data requirements IT Head of IT Align IT strategy IT implementation Technical Data Steward
  19. 19. © BEI St. Gallen – 2015, A. Reichert / 19 Example 2 – Chemical Industry Business drivers for Data Governance  Process Efficiency  Delayed delivery to customers due to wrong material master  Invoicing to the wrong customer  Wrong labels  Cost Reduction  High inventories due to lack of trust in master data  Additional air freight costs to ensure on time arrival  Management Decision Support  Reporting inaccuracy due to inconsistent data
  20. 20. © BEI St. Gallen – 2015, A. Reichert / 20 • Defining and monitoring of SLAs and KPIs in a global governance framework • Acting as a global stewardship organization, driving the global standardization and optimization of processes • Providing one global lead steward for each data object to ensure accountability and a high level of support to business users 3. The MDM organization act as a catalyst through… • Accountabilities for master data are defined and data quality monitored • Maintenance processes are globally standardized and automated • A small number of data specialists concentrate on continuous improvement instead of firefighting and data typing 2. We have to come to a state where… • No clear accountability for master data on a global level • Lack of standardization and automation  Inefficient and heterogeneous ways of managing master data  Poor data quality troubles users of global systems (APO, EDWH, global product costing 1. The situation today shows… The MDM organization will sustain efficiency and quality of master data
  21. 21. © BEI St. Gallen – 2015, A. Reichert / 21  Each process delivers services to the business organizations  The implementation of the services will follow of structured roadmap for the defined master data types (Material, Vendor, Customer, Finance, Employee)  The services are measured by Service Level Agreements (SLAs) in order to assure the quality of the services Process landscape Master Data Maintenance2 Master Data Standards Training & Support Quality Assurance 3 4 5 Master Data Infrastructure6 Master Data Strategy1 Scope of services Material Vendor Customer Finance Employee Process landscape for MDM services
  22. 22. © BEI St. Gallen – 2015, A. Reichert / 22 CEO Functional Grouping Service Functions BS (HR, IS, FI, LT etc) etc Strategic Functions HR FI Marketing etc Divisional Grouping Geographic structure Product structure Market structure Head of Business Services Head of MDM Regional MDM Heads Head of NAFTA MDM Head of LATAM MDM Head of EAME/APAC MDM Lead Data Stewards Material HR Customer Vendor Finance Data Architect Company structure MDM structure Organizational integration of MDM
  23. 23. © BEI St. Gallen – 2015, A. Reichert / 23 • Change of functional reporting from business to a business neutral MDM unit • Change of regional reporting lines to global reporting line Impacts • Harmonized processes and policies and governance across regions & business units • Higher scalability: faster integration of new companies or processes, systems etc. • Bigger pool of trained people • Reduced headcount • Reduced number of codes in system (big issue in material today as well as vendor and customer) • Improved data quality & reporting also since global team has higher authority to advise regional teams to not “manipulate data in ERP system) • Attraction for higher skilled employees based on career opportunities Benefits • Strong and visible SLAs in place including tracking of KPIs • Strong governance model between business and MDM • Quick wins for Business in order to Business to accept organization • Outsourcing only when internal processes work well Critical success factors Main benefit of the global MDM organization is the overall improved data quality enabling business to focus on core
  24. 24. © BEI St. Gallen – 2015, A. Reichert / 24 Global  Global responsibility  Regional and local presence Shared  Center of excellence for the business  Efficiency and speed Governing  Binding standards and guidelines for the use of master data  Defined methodologies and tools Service- oriented  Aiming at internal customer satisfaction  Service level agreements for measurable performance Managed  Preventive measures instead of “firefighting”  Clear objectives and standard operating procedures Empowered  Sponsored by executive management  Appropriate resource assignment Governance design principles
  25. 25. © BEI St. Gallen – 2015, A. Reichert / 25 The way forward – From shared service to outsourced data management processes IS Outsourcing Partner Company Domain MDM Teams MDM Leads MDM Data Stewards Company Service Delivery & Operations Teams Service Delivery Managers Master Data Requestors Business Process Outsourcing Partner Master Data Processors Clients Master Data Request Originators
  26. 26. © BEI St. Gallen – 2015, A. Reichert / 26 Key success factors for implementing Data Governance Demonstrate staying power! Data Governance is a change issue and requires involvement of all stakeholders. No bureaucracy! Use existing board structures and processes. No ivory tower, no silver bullet! Use “real-life” examples to get buy in from local business units. Define clear objectives and standard operation procedures to prevent “firefighting”.
  27. 27. © BEI St. Gallen – 2015, A. Reichert / 27 Contacts & Resources on the Internet http://www.bei-sg.ch Business Engineering Institute St. Gallen http://cdq.iwi.unisg.ch Competence Center Corporate Data Quality https://benchmarking.iwi.unisg.ch/ CC CDQ Benchmarking Platform http://www.xing.com/net/cdqm CC CDQ Community at XING Dr. Andreas Reichert Business Engineering Institute St. Gallen AG Mail: andreas.reichert@bei-sg.ch Phone: +41 (0) 76 72 90 785 http://corporate-data-league.ch/wiki/Main_Page Pilot Corporate Data League for Data Cleansing
  28. 28. © BEI St. Gallen – 2015, A. Reichert / 28 Customers and partners benefit from an unmatched pool of knowledge and expertise 90+ Best Practice Presentations 40 Consortium Workshops (5 p.a.) 27 Partner Companies 14 PhD Students 1 Competence Center Strategy Strategy for CDQ Systeme Applications for CDQ Corporate Data Architecture lokal global Organisation CDQ Controlling CDQ Processes and Methods Organisation for CDQ 850+ Contacts in the overall CC CDQ community 180+ Members in the XING Community1 150+ Bilateral Project Workshops NB: as of June 2013. Data covers period from 2006 until today. 1) See www.xing.com/net/cdqm.
  29. 29. © BEI St. Gallen – 2015, A. Reichert / 29 BEI offers a “tool-box” of services which can be adapted to your specific needs EFQM Excellence Model for Data Quality Management Data Quality Management Strategy Design Method Reference model for Data Governance Method for establishing Data Governance Method for integrating DQ in process management Method for specifying data quality metrics Method for master data integration Reference model for DQ Management software 386 DQ-Cockpit 0 1000 I II III Sponsor Data Owner Corporate Data Steward Fachlicher Datensteward Technischer Datensteward SDQM- Komitee Daten- steward- Team MDM Strategie MDM Standards & Richtlinien Entwicklung Vision Umsetzungs- planung Kommuni- kations- planung Organisations- design Abstimmung Geschäfts/IT- strategie MDM Qualitäts- sicherung MDM Unterstüt- zungsfunktionen Mess- methoden / Reporting Verbes- serungs- prozess Definition Qualitäts- ziele Definition Verantwort- lichkeiten Nomenklatur MD Prozesse Daten- standards Authori- sierungs- konzept Prozeduren & Services Trainings Prozess- Support Techn. Support MDM Datenmodell Definition Anforderung Change- Prozess für Anpassung Entwicklung / Anpassung Datenmodelle Definition Verantwort- lichkeiten MDM Lebens- zyklus Management Daten- erstellung Daten-Update Daten- archivierung Daten- anforderung Daten- freigabe Projekt- unterstüt- zung Lebenszyklus- management für Stammdaten Metadaten- management und Stammdaten- modellierung Qualitäts- management für Stammdaten Stammdaten- integration Querschnitt- funktionen Administration A Stammdatenanlage Stammdatenpflege Stammdaten- deaktivierung Stammdaten- archivierung Datenmodellierung Modellanalyse Datenanalyse Datenanreicherung Datenbereinigung Datenimport Datentransformation Datenexport Automatisierung Berichte Suche Workflow- management Änderungs- management Benutzerverwaltung Metadaten- management B C D E F 1 2 3 4 1 1 1 1 1 2 2 2 2 2 3 3 3 3 4 MDS Quelle 1 Quelle 2 Quelle m Ziel 1 Ziel 2 Ziel n MDS Ziel 1 Ziel 2 Ziel n TransaktionKoexistenz Strategische Anforderungen und WertbeitragA ProzesseB OrganisationC QualitätssicherungD ArchitekturE Umsetzungsplan (Transformation)F
  30. 30. © BEI St. Gallen – 2015, A. Reichert / 30 BEI has a proven record of supporting companies in setting up Data Governance structures and organizations
  31. 31. © BEI St. Gallen – 2015, A. Reichert / 31 The CC CDQ “knowledge pool” provides access to a variety of existing knowledge and expertise
  32. 32. © BEI St. Gallen – 2015, A. Reichert / 32 The “CC CDQ Awards” recognize excellent results CDQ Good Practice AwardCDQ Excellence Award Apply for the CDQ Awards On-site visits and interviews Winners are recognized at the annual Business Engineering Forum Winners are selected on basis of the assessment score Winners are selected by a jury1 (representatives from both scientific and practitioner’s community) Jury Members (* = To be confirmed)  Prof. Dr. Andy Koronios (University of South Australia)  Henning Uiterwyk (Managing Director of eCl@ss)  Jodi Maciejewski (Amerian SAP User Group, ASUG)  Màrta Nagy Rothengrass (European Commission, Content and Technology Unit - Data Value Chain)*  Bernhard Thalheim (Head of the German Chapter of DAMA International)*  Frank Boller (VP SwissICT, Swiss ICT industry association)*  Lwanga Yonke (International Association for Information and Data Quality)* Starting 2014 Starting 2013
  33. 33. © BEI St. Gallen – 2015, A. Reichert / 33 AstraZeneca wins CDQ Good Practice Award 2014! Link to official announcement: http://cdq.iwi.unisg.ch/en/veranstaltungen/cdq-award-2013/winners-2014/ Interviews http://youtu.be/jMDGLA5ig00 Award ceremony http://youtu.be/Aq91v98dIGE Award laudatio http://youtu.be/Lqgf72v7O74 Links to the videos:
  34. 34. © BEI St. Gallen – 2015, A. Reichert / 34 The Framework for CDQM is a standardized maturity and benchmarking model Download PDF: http://benchmarking.iwi.unisg.ch/Framework_for_CDQM.pdf (Print-Version): http://www.efqm.org/ Getragen durch die Praxis
  35. 35. © BEI St. Gallen – 2015, A. Reichert / 35 BEI is setting up a trusted network of user companies for the exchange of business partner data First-movers have exceptional advantages  Improve data maintenance processes through network collaboration  Start collaboration with 6-8 trusted and renowned companies Goals  30% lower maintenance efforts / costs by the use of verified data in the network  First-movers define the rules for collaboration Benefits Prospects Corporate Data League Collective Business Partner Data read write http://corporate-data-league.ch/wiki/Main_Page Pilot Corporate Data League for Data Cleansing
  36. 36. © BEI St. Gallen – 2015, A. Reichert / 36 The «CDQ Academy» is a training program for Master Data and Data Governance Professionals Conceptual Model of Corporate Data Data Distribution and Storage Architecture Applications for Master Data Management EFQM Excellence Model for Master Data Management Module 3: Data Architecture and Applications Business Perspective on Master Data Management Design Areas of Master Data Management Master Data Management Strategy Master Data Quality Controlling Module 1: Strategy and Controlling Master Data Management as a Corporate Function Roles and Responsibilities for the use of Master Data Standard procedures and Guidelines in company’s daily processes Analysis and Optimization of Master Data Processes Module 2: Organization, People, Processes & Methods 11. - 12. November 2015 Duesseldorf, DE 1.- 2. October 2015 St. Gallen, CH 6. - 7. May 2015 Duesseldorf, DE 21.-22. January 2016 St. Gallen, CH 9.-10. June 2016 St. Gallen, CH