A Reference Process Model for Master Data Management
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This document presents an overview of a reference process model for master data management. It includes an introduction discussing business requirements for master data and challenges in managing master data quality. It also describes the research methodology used to develop an iterative reference process model. The results section provides an overview of the reference process model and discusses its evaluation through three case studies. The conclusion recognizes the model's contribution in explicating the design process for master data management organizations.
![© IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 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 €] 3.2 2.2 42.0
Staff 2011 11,000 11,000 170,000
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](https://image.slidesharecdn.com/mdmprocessmodel04bot-130515024309-phpapp02/85/A-Reference-Process-Model-for-Master-Data-Management-10-320.jpg)
A Reference Process Model for Master Data Management
- 1. Andreas Reichert, PD Dr.-Ing. Boris Otto, Prof. Dr. Hubert Österle Leipzig February 28, 2013 A Reference Process Model for Master Data Management
- 2. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 2 Agenda 1. Introduction 2. Related Work 3. Research Methodology 4. Results Presentation 5. Conclusion and Outlook
- 3. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 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
- 4. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 4 Legend: Data quality pitfalls (e. g. migrations, process touch points, poor corporate reporting. Master Data Quality Time Project 1 Project 2 Project 3 1.2 Difficulties in practice when it comes to managing master data quality Case of Bayer CropScience (cf. Brauer 2006)
- 5. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 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?
- 6. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 6 1.4 Enterprises are in need of support in this matter * Source: Workshop presentations at the CC CDQ Workshops by companies 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
- 7. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 7 Model Focus Assessment (Dyché & Levy 2006) Customer data integration No focus on activities (English 1999): Total Quality data Management (TQdM) (Loshin 2007) Data governance (Weber 2009) Data governance reference model 2.1 Related Work in Research and Practice Process models related to master data management Role models related to master data management Model Focus Assessment ITIL IT service management No integrated process focus (Batini & Scannapieco 2006) Data quality management activities Otto et al. (2012) Software functionality
- 8. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 8 3.1 Research Methodology and Process 2009 2010 2011 2012 1. Identify problem & motivate 1.1 Identification of challenges within practitioners community 2. Define objectives of a solution 2.1 Focus group A (2009-12-01) 2.2 Principles of orderly reference modeling A 6. Communication 6.1 Scientific paper at hand 4.1 Three participative case studies 3.1 Literature review 3.2 Principles of orderly reference modelling 3.3 Process map techniques 3.4 Focus groups B (2010-11-26), C (2011-11-24) B C 5.1 Focus group C (2011-11-24) 5.2 Three participative case studies 5.3 Multi-perspective evaluation of reference models C 3. Design & development 4. Demonstration 5. Evaluation
- 9. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 9 4.1 Overview of the Reference Process Model for Master Data Management Data Life Cycle Data Support Data Architecture Data Model Data Quality Assurance Standards & Guidelines Strategic Functions 1.1 2.1 2.2 2.3 Governance Strategy 2.4 3.2 3.1 Operations Develop and adapt vision Align w/ business & IT strategy Define strategic targets Set up responsibi- lities Define roadmap Develop communic. and change Adapt nomencla- ture Adapt data life cylce Adapt standards & guidelines Adapt authori- zation concept Adapt support processes Adapt measure- ment metrics Adapt reporting structures Define quality targets Monitor & report data quality Initiate quality improve- ments Identify data require- ments Model data Analyze implications Test & implement changes Roll out data model changes Identify business issues Identify require- ments Model data architecture Model workflows / UIs Analyze implications on change Roll out data architecture Test & implement Manage requests Create data Update data Release data Use data Archive / delete data Adapt user trainings Provide trainings Provide user support Provide project support Process Area Main Process Process 1 2 3 1.1.1 1.1.2 1.1.3 1.1.4 1.1.5 1.1.6 2.1.1 2.1.2 2.1.3 2.1.4 2.1.5 2.1.6 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.4.1 2.4.2 2.4.3 2.4.4 2.4.5 2.4.6 3.1.1 3.1.2 3.1.3 3.1.4 3.1.5 3.1.6 3.2.1 3.2.2 3.2.3 3.2.4
- 10. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 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 €] 3.2 2.2 42.0 Staff 2011 11,000 11,000 170,000 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
- 11. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 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 X 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 X 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 X 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 X X Processes of main process “Life Cycle” renamed (3.1) Naming of processes aligned with company specific naming conventions as processes were already defined X X X 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 X X
- 12. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 12 4.3 Design Decisions (continued) Design Decision Justification A B C Process “Develop and adapt vision” removed (1.1.1) Company strategies not defined by visions but by strategic targets X Processes “Adapt data life cycle”, “Adapt standards and guidelines”, “User trainings”, and “Support Processes” merged to “Standards for operational processes” (2.1.2 - 2.1.6) 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) X Processes “Test and implement (data model)” and “Roll out data model changes” removed (2.3.4 - 2.3.5) 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 X Main process “Data Architecture” removed (2.4) Activities defined within IT service portfolio Clear separation between business requirements and modeling of data and IT realization (integration architecture etc.) X 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 X
- 13. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 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
- 14. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 14 PD Dr.-Ing. Boris Otto University of St. Gallen Institute of Information Management [email protected] +41 71 224 3220 Your Speaker This research was supported by the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen.
- 15. © IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 15 References BRAUER, B. 2009. 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. 2006. Customer Data Integration, Hoboken (USA), John Wiley. ENGLISH, L. P. 1999. Improving Data Warehouse and Business Information Quality, New York et al., Wiley. FRANK, U. 2006. 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. 2006. 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. 2010. Master Data Compliance: The Case of Sanction Lists. 16th Americas Conference on Information Systems. Lima, Peru: Universidad ESAN. MERTENS, P. 1997. Integrierte Informationsverarbeitung, Wiesbaden, Gabler. OTTO, B. 2011. A Morphology of the Organisation of Data Governance. 19th European Conference on Information Systems. Helsinki, Finland. OTTO, B., HÜNER, K. & ÖSTERLE, H. 2012. Toward a functional reference model for master data quality management. Information Systems and e-Business Management, 10, 395-425. OTTO, B. & REICHERT, A. 2010. 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. 2003. Customer data management in practice: An insurance case study. J. of Database Mark., 10, 327-341. SMITH, H. A. & MCKEEN, J. D. 2008. Developments in Practice XXX: Master Data Management: Salvation Or Snake Oil? Communications of the AIS, 23, 63-72. STAHLKNECHT, P. & HASENKAMP, U. 1997. Einführung in die Wirtschaftsinformatik, Berlin, Springer.