A Reference Process Model for Master Data Management

Transcription

1 A Reference Process Model for Master Data Management Andreas Reichert, PD Dr.-Ing. Boris Otto, Prof. Dr. Hubert Österle Leipzig February 28, 2013

2 Agenda 1. Introduction 2. Related Work 3. Research Methodology 4. Results Presentation 5. Conclusion and Outlook IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 2

3 1.1 Business Requirements for Master Data Master data describes key business objects in an enterprise (e.g. Stahlknecht & Hasenkamp 1997; Mertens 1997) Examples are product, material, customer, supplier, employee master data Master data of high quality is important for meeting various business requirements (e.g. Knolmayer & Röthlin 2006; Kokemüller 2010; Pula et al. 2003) Compliance with legal provisions Integrated customer management Automated business processes Effective and efficient reporting IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 3

4 1.2 Difficulties in practice when it comes to managing master data quality Case of Bayer CropScience (cf. Brauer 2006) Master Data Quality Project 1 Project 2 Project 3 Time Legend: Data quality pitfalls (e. g. migrations, process touch points, poor corporate reporting. IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 4

5 1.3 Master Data Management must be organized Master data management is an application-independent function (Smith & McKeen 2008) The organizational structure of master data management has been research to some extent Empirical analysis regarding the positioning of master data management within an organization (Otto & Reichert 2009) Master data governance design (Otto 2011) How to design master data management processes? IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 5

6 1.4 Enterprises are in need of support in this matter Company Main Challenges Establishing a central master data Shared Service Center for governance and operational tasks Support of high quality master data for online sales channels Central governance for new data processes Set up of a central master data organization for material, customer, and vendor master data due to changing business model, and hence, processes New organization of medical and safety division Design of data governance processes for material master data * Source: Workshop presentations at the CC CDQ Workshops by companies IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 6

7 2.1 Related Work in Research and Practice Process models related to master data management Model Focus Assessment ITIL (Batini & Scannapieco 2006) Otto et al. (2012) IT service management Data quality management activities Software functionality No integrated process focus Role models related to master data management Model Focus Assessment (Dyché & Levy 2006) (English 1999): (Loshin 2007) (Weber 2009) Customer data integration Total Quality data Management (TQdM) Data governance Data governance reference model No focus on activities IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 7

8 3.1 Research Methodology and Process 1. Identify problem & motivate 1.1 Identification of challenges within practitioners community A 2. Define objectives of a solution 2.1 Focus group A ( ) 2.2 Principles of orderly reference modeling 3. Design & development 3.1 Literature review 3.2 Principles of orderly reference modelling 3.3 Process map techniques 3.4 Focus groups B ( ), C ( ) B C 4. Demonstration 4.1 Three participative case studies 5. Evaluation C 5.1 Focus group C ( ) 5.2 Three participative case studies 5.3 Multi-perspective evaluation of reference models 6. Communication 6.1 Scientific paper at hand IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 8

9 4.1 Overview of the Reference Process Model for Master Data Management Process Area Main Process Process 1 Strategy 1.1 Strategic Functions Develop Align w/ Define Set up Develop Define and adapt business & strategic responsibilities and communic. roadmap vision IT strategy targets change 2 Governance 3 Operations 2.1 Standards & Guidelines 2.2 Data Quality Assurance 2.3 Data Model 2.4 Data Architecture 3.1 Data Life Cycle 3.2 Data Support Adapt Adapt Adapt Adapt Adapt data authorization trainings Adapt user nomenclature guidelines processes standards & support life cylce concept Adapt Initiate Identify Adapt Define measurement improve- quality business reporting quality issues structures targets metrics ments Identify Test & Roll out data Analyze Model data implement data model requirements implications changes changes Identify Model Analyze Roll out Model data Test & requirements UIs on change workflows / implications data architecture implement architecture Manage Update Release Archive / Create data Use data requests data data delete data Provide Provide Monitor & Provide user project report data trainings support support quality IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 9

10 4.2 Iterative Design and Evaluation in Three Case Studies Case A B C Industry High Tech Engineering Retail Headquarter Germany Germany Germany Revenue 2011 [bn ] Staff ,000 11, ,000 Role of main contact person for the case study Head of Enterprise MDM Head of Material MDM Project Manager MDM Strategy Initial situation Specification of existing data management organization Merger of two internal data management organizations Design of new data management organization within project IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 10

11 4.3 Design Decisions Design Decision Justification A B C Process Define strategic targets removed (1.1.3) Activities integrated in process Align with business/it strategy No explicit MDM strategic targets required as they should be integrated in existing target systems Process Model Workflows/UIs (User Interfaces) moved from main process Architecture to Standards & Guidelines (2.4.3) Focus for activity is set on conceptual design rather than technical implementation aspects Technical implementation needs to be covered by IT-processes. Case A only covers the conceptual part of the workflow design. The implementation process will be described outside of this process Process Monitor & report (in context of Quality Assurance) moved from main process Support to Quality Assurance (3.2.4) Mix of governance and operational activities in main process Governance However, focus is set on end-to-end process including both aspects Process Test & Implement (in context Architecture) removed (2.4.5) Testing activities defined within IT-processes and do not need to be covered by data management processes Removal will eliminate double definitions within company Processes of main process Life Cycle renamed (3.1) Naming of processes aligned with company specific naming conventions as processes were already defined Process Mass data changes added to Support (new 3.2.5) New process added as activity is performed on continuous base and should be covered by data management processes IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 11

12 4.3 Design Decisions (continued) Design Decision Justification A B C Process Develop and adapt vision removed (1.1.1) Processes Adapt data life cycle, Adapt standards and guidelines, User trainings, and Support Processes merged to Standards for operational processes ( ) Processes Test and implement (data model) and Roll out data model changes removed ( ) Main process Data Architecture removed (2.4) Company strategies not defined by visions but by strategic targets Activities of all processes remain existing Goal is simplification of process model Description of all activities, which have been merged to the new process, will be created on the work description level, which will underlay the process model for execution of processes (including process flows, responsibilities, etc) Activities defined within IT service portfolio outside of this process model As activities are already defined, they do not need to be covered within this structure Activities defined within IT service portfolio Clear separation between business requirements and modeling of data and IT realization (integration architecture etc.) Process Data analysis in main process Support added (new 3.2.6) Requests for one-time analysis of master data as service offering defined which are not covered by standard reports IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 12

13 5.1 Conclusion and Outlook Results The reference model supports the design process of master data managements organizations as well as the specification of existing structures The reference model was evaluated from an economic, deployment, engineering and epistemological perspective (cf. Frank 2006) by researchers and practitioners Contribution Innovative artifact in a relevant field of research Explication of the design process Engaged scholarship case Limitations Qualitative justification of design decisions Further design/test cycles necessary Applicable for large enterprises mainly IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 13

14 Your Speaker PD Dr.-Ing. Boris Otto University of St. Gallen Institute of Information Management This research was supported by the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen. IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 14

15 References BRAUER, B Master Data Quality Cockpit at Bayer CropScience. 4. Workshop des Kompetenzzentrums Corporate Data Quality 2 (CC CDQ2). Luzern: Universität St. Gallen. DYCHÉ, J. & LEVY, E Customer Data Integration, Hoboken (USA), John Wiley. ENGLISH, L. P Improving Data Warehouse and Business Information Quality, New York et al., Wiley. FRANK, U Evaluation of Reference Models. In: FETTKE, P. & LOOS, P. (eds.) Reference Modeling for Business Systems Analysis. Hershey, PA: IGI Publishing. KNOLMAYER, G. F. & RÖTHLIN, M Quality of Material Master Data and Its Effect on the Usefulness of Distributed ERP Systems. In: RODDICK, J. F. (ed.) Advances in Conceptual Modeling - Theory and Practice. Berlin: Springer. KOKEMÜLLER, J Master Data Compliance: The Case of Sanction Lists. 16th Americas Conference on Information Systems. Lima, Peru: Universidad ESAN. MERTENS, P Integrierte Informationsverarbeitung, Wiesbaden, Gabler. OTTO, B A Morphology of the Organisation of Data Governance. 19th European Conference on Information Systems. Helsinki, Finland. OTTO, B., HÜNER, K. & ÖSTERLE, H Toward a functional reference model for master data quality management. Information Systems and e-business Management, 10, OTTO, B. & REICHERT, A Organizing Master Data Management: Findings from an Expert Survey. In: BRYANT, B. R., HADDAD, H. M. & WAINWRIGHT, R. L. (eds.) 25th ACM Symposium on Applied Computing. Sierre, Switzerland. PULA, E. N., STONE, M. & FOSS, B Customer data management in practice: An insurance case study. J. of Database Mark., 10, SMITH, H. A. & MCKEEN, J. D Developments in Practice : Master Data Management: Salvation Or Snake Oil? Communications of the AIS, 23, STAHLKNECHT, P. & HASENKAMP, U Einführung in die Wirtschaftsinformatik, Berlin, Springer. IWI-HSG Leipzig, February 28, 2013, Reichert, Otto, Österle / 15