Strategic Business Requirements for Master Data Management Systems

Transcription

1 Strategic Business Requirements for Master Data Management Systems Boris Otto, Martin Ofner Detroit, IL, August 5, 2011 University of St. Gallen, Institute of Information Management Tuck School of Business at Dartmouth College

2 Agenda 1. Motivation and Problem Statement 2. Background 3. Research Approach 4. Design Principles and Business Requirements 5. Evaluation 6. Conclusion Detroit, MI, 08/05/11, B. Otto / 2

3 The initial situation in practice User Uncertainty 1 What is the proper sequence of activities in support of MDM? Must we have solid data integration and data quality practices and architectures in place before dealing with MDM? Most of our current data integration requirements are batch-oriented in nature, as we work to physically consolidate silos of master data. What types of packaged data integration tools will be most relevant for our purposes? Has market consolidation already reached the point where the advantages of single-vendor stacks for MDM outweigh the advantages of a best-ofbreed strategy? Diverging Expectations We are flooded by invitations from MDM software vendors to sit together and let them present their solutions, which are always supposed to be the solution to all our problems. When we meet, it s always the same: They present something we aren t looking for. Then we tell them our understanding of the world and what our real requirements are -- what in return they do not want or cannot share. And in the end, everybody goes his own way, highly frustrated because they couldn t sell their product, we didn t get an answer to our problems, and both of us spent time in vain. What are strategic business requirements to be met by MDM systems? How can these requirements be framed to support communication between user companies and software vendors? Detroit, MI, 08/05/11, B. Otto / 3

4 Background: Master Data and MDM Master Data Essential business entities a company s business activities are based on (customers, suppliers, employees, products etc.) 2 Master Data Management (MDM) All activities for creating, modifying or deleting a master data class, a master attribute, or a master data object. 3 Aiming at providing master data of good quality (i.e. master data that is complete, accurate, timely, and well structured) for being used in business processes. 4,5 Detroit, MI, 08/05/11, B. Otto / 4

5 Background: MDM Systems MDM Research Foci Use Cases 6,7 Architecture Patterns 8,9 Market Surveys 10,11 Analytical Operational Leading System Central System Repository Peer-to-peer Detroit, MI, 08/05/11, B. Otto / 5

6 Research process according to the principles of Design Science Research 12 ANALYSIS Expert interviews 13 (02/28/09) to identify and describe problem Future Search 14 activities (05/07 to 05/14/09) to define objectives of a solution DESIGN & DEMONSTRATION Future Search activities to identify design principles Reference modeling 15 for framework design Focus groups 16 (06/24, 09/29, and 12/02/09) to demonstrate objectives and design principles EVALUATION Offline expert evaluation (via , 11/30 to 12/18/09) Focus group evaluation (05/27/10) COMMUNICATION Presentation to practitioners community (05/27/10) Q1/09 Q2/09 Q3/09 Q4/09 Q1/10 Q2/10 Q3/10 Q4/10 Detroit, MI, 08/05/11, B. Otto / 6

7 Structure of the framework of strategic business requirements for MDM Business Context Shortcomings of Current Solutions Strategic MDM Use Cases Design Principles Strategic Business Requirements Framework Detroit, MI, 08/05/11, B. Otto / 7

8 The initial situation in practice Current Shortcomings No downstream visibility of data Poor business semantics management MDM and data quality management separated Stovepipe approach for MDM architectures No consistent master data service approach No predefined content No on the fly mapping and matching Poor support of centralized management of decentralized/federated datasets No integrated business rules management Poor support of distinction between global and local data Poor support of compliance issues Insufficient transition management Use Cases Risk management and compliance Integrated customer management Business process integration and harmonization Reporting IT consolidation Detroit, MI, 08/05/11, B. Otto / 8

9 Design principles Master Data as a Product Deep Integration Market for Master Data Process Quality Design Principles Subsidiarity The Nucleus Contextawareness Detroit, MI, 08/05/11, B. Otto / 9

10 Strategic business requirements ID Requirement Design Area Supports Design Principle(s) R1 Support of Master Data Product Descriptions Strategy Master Data as a Product R2 Sourcing of Master Data Products Strategy Market for Master Data R3 Integration of External Master Data Sources Strategy Market for Master Data R4 R5 R6 Quality Management of Master Data Products and Services Audit Management of Master Data Products and Services Management of Role Access Rights according to Data Governance Roles Controlling Controlling Organization Process Quality Process Quality Subsidiarity R7 Escalation Management Organization Subsidiarity R8 Support of Usage Monitoring of Master Data Products R9 Maintenance for Context-Aware Master Data Products Operations Operations Process Quality Context Awareness R10 Gauging of Master Data Product consumption Operations Process Quality R11 Requirements Engineering for Master Data Products R12 Design and Maintenance of Global/Local Master Data Management Processes Operations Operations Master Data as a Product Process Quality Detroit, MI, 08/05/11, B. Otto / 10

11 Strategic business requirements (cont d) ID Requirement Design Area Supports Design Principle(s) R13 Internal Customer Support Operations Master Data as a Product R14 Management of Business Rules for Data Standards R15 Support of End-to-End Master Data Product Lifecycles Operations Operations Process Quality Context Awareness R16 Support of Master Data Provenance Tracing Operations Process Quality R17 Data Standards Management Integration Architecture R18 Enforcement of Data Standards Integration Architecture R19 Bottom-up Data Modeling using Heuristics Integration Architecture R20 Delivery of Predefined Content Integration Architecture R21 Maintanance of Global/Local Master Data Model Design Integration Architecture The Nucleus The Nucleus The Nucleus The Nucleus The Nucleus R22 Subscription of Master Data Products Applications Deep Integration R23 Support of Interoperability Standards Applications Deep Integration Detroit, MI, 08/05/11, B. Otto / 11

12 Publication as managerial report Co-signed by: Detroit, MI, 08/05/11, B. Otto / 12

13 Multi-perspective framework evaluation 17 Perspective Description Evaluation Result A Economic No statement on direct business benefits possible at present. Focus groups expect improvements regarding internal and external communication. B Deployment Focus group was considered complete, appropriate, and applicable. Community voted for continuation of initiative. C Engineering Rather informal at present. Software vendors participating in focus group on 05/27/2010 demanded more concrete scenarios. D Epistemological Accepted guidelines and research methods were applied. Detroit, MI, 08/05/11, B. Otto / 13

14 Conclusions The framework addresses an acute need in the practitioners community Practitioners benefit from the framework as it facilitates internal and external communication The paper adds to the scientific body of knowledge since it presents an abstraction of an information system in a quite neglected area of IS research. Detroit, MI, 08/05/11, B. Otto / 14

15 Contact Dr.-Ing. Boris Otto University of St. Gallen, Institute of Information Management Tuck School of Business at Dartmouth College Detroit, MI, 08/05/11, B. Otto / 15

16 Appendix Endnotes Detroit, MI, 08/05/11, B. Otto / 16

17 Endnotes 1) Friedman, T. "Q&A: Common Questions on Data Integration and Data Quality From Gartner's MDM Summit", Gartner, Inc., Stamford, CT. 2) Smith, H.A. and McKeen, J.D. "Developments in Practice XXX: Master Data Management: Salvation or Snake Oil? Communications of the AIS (23:4) 2008, pp ) Ibid. 4) Karel, R. "Introducing Master Data Management", Forester Research, Cambridge, MA. 5) Loshin, D. Master Data Management Morgan Kaufmann, Burlington, MA, ) Dreibelbis, A., Hechler, E., Milman, I., Oberhofer, M., van Run, P., and Wolfson, D. Enterprise Master Data Management: An SOA Approach to Managing Core Information Pearson Education, Boston, MA, ) Loshin, D. Master Data Management Morgan Kaufmann, Burlington, MA, ) Loser, C., Legner, C., and Gizanis, D. "Master Data Management for Collaborative Service Processes", International Conference on Service Systems and Service Management, Research Center for Contemporary Management, Tsinghua University, ) Otto, B. and Schmidt, A. "Enterprise Master Data Architecture: Design Decisions and Options", in: Proceedings of the 15th International Conference on Information Quality (ICIQ-2010), Little Rock, USA, ) Radcliffe, J. "Magic Quadrant for Master Data Management of Customer Data", G , Gartner, Inc., Stamford, CT. 11) White, A. "Magic Quadrant for Master Data Management of Product Data", G , Gartner, Inc., Stamford, CT. 12) Peffers, K., Tuunanen, T., Rothenberger, M.A., and Chatterjee, S. "A Design Science Research Methodology for Information Systems Research", Journal of Management Information Systems (24:3) 2008, pp ) Meuser, M. and Nagel, U. "Expertenwissen und Experteninterview", in: Expertenwissen. Die institutionelle Kompetenz zur Konstruktion von Wirklichkeit, R. Hitzler, A. Honer and C. Maeder (eds.), Westdeutscher Verlag, Opladen, 1994, pp Detroit, MI, 08/05/11, B. Otto / 17

18 Endnotes 14) Weisbord, M. Discovering Common Ground: How Future Search Conferences Bring People Together to Achieve Breakthrough Innovation, Empowerment, Shared Vision, and Collaborative Action Berrett-Koehler, San Francisco, ) Schütte, R. Grundsätze ordnungsmässiger Referenzmodellierung: Konstruktion konfigurations- und anpassungsorientierter Modelle Gabler, Wiesbaden, Germany, ) Morgan, D.L. and Krueger, R.A. "When to use Focus Groups and why?" in: Successful Focus Groups, D.L. Morgan (ed.), Sage, Newbury Park, California, 1993, pp ) Frank, U. "Evaluation of Reference Models", in: Reference Modeling for Business Systems Analysis, P. Fettke and P. Loos (eds.), Idea Group, Hershey, Pennsylvania et al., 2007, pp Detroit, MI, 08/05/11, B. Otto / 18