University of St. Gallen, Institute of Information ManagementTuck School of Business at Dartmouth College

Strategic Business Requirements for Master Data Management Systems

Boris Otto, Martin OfnerDetroit, IL, August 5, 2011

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Agenda

1. Motivation and Problem Statement

2. Background

3. Research Approach

4. Design Principles and Business Requirements

5. Evaluation

6. Conclusion

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■ “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-of-breed strategy?”

The initial situation in practice

User Uncertainty1

“We are flooded by invitations from MDM software vendors to sit together and let them present their solutions, which arealways supposed to be the solution to all our problems. When we meet, it’s always the same: They present something wearen’t looking for. Then we tell them our understanding of the world and what our real requirements are -- what in return theydo not want or cannot share. And in the end, everybody goes his own way, highly frustrated because they couldn’t sell theirproduct, we didn’t get an answer to our problems, and both of us spent time in vain.”

Diverging Expectations

■ 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?

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Background: Master Data and MDM

Master DataEssential 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

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Background: MDM Systems

MDM Research Foci

Architecture Patterns8,9 Market Surveys10,11Use Cases6,7

Analytical

Operational

Leading System

Central System

Repository

Peer-to-peer

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Research process according to the principles of Design Science Research12

Q1/09 Q2/09 Q3/09 Q4/09 Q1/10 Q2/10 Q3/10 Q4/10

ANALYSIS

DESIGN & DEMONSTRATION

EVALUATION

COMMUNICATION

■Expert interviews13 (02/28/09) to identify and describe problem■ “Future Search”14 activities (05/07 to 05/14/09) to define objectives of a

solution

■“Future Search” activities to identify design principles■Reference modeling15 for framework design■Focus groups16 (06/24, 09/29, and 12/02/09) to demonstrate

objectives and design principles

■ “Offline” expert evaluation (via email, 11/30 to 12/18/09)

■ Focus group evaluation (05/27/10)

■Presentation to practitioners community (05/27/10)

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Structure of the framework of strategic business requirements for MDM

Shortcomings of Current Solutions

Design Principles

Strategic MDM Use Cases

Strategic Business Requirements

Business Context

Framework

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■ Risk management and compliance

■ Integrated customer management

■ Business process integration and

harmonization

■ Reporting

■ IT consolidation

■ 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

The initial situation in practice

Current Shortcomings Use Cases

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Design principles

Design Principles

Master Data as a Product

Market for Master Data

Subsidiarity

Context-awareness

The “Nucleus”

Process Quality

Deep Integration

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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 Quality Management of Master Data Products and Services

Controlling Process Quality

R5 Audit Management of Master Data Products and Services

Controlling Process Quality

R6 Management of Role Access Rights according to Data Governance Roles

Organization Subsidiarity

R7 Escalation Management Organization Subsidiarity

R8 Support of Usage Monitoring of Master Data Products

Operations Process Quality

R9 Maintenance for Context-Aware Master Data Products

Operations Context Awareness

R10 Gauging of Master Data Product consumption Operations Process Quality

R11 Requirements Engineering for Master Data Products

Operations Master Data as a Product

R12 Design and Maintenance of Global/Local Master Data Management Processes

Operations Process Quality

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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

Operations Process Quality

R15 Support of End-to-End Master Data Product Lifecycles

Operations Context Awareness

R16 Support of Master Data Provenance Tracing Operations Process Quality

R17 Data Standards Management Integration Architecture

The Nucleus

R18 Enforcement of Data Standards Integration Architecture

The Nucleus

R19 Bottom-up Data Modeling using Heuristics Integration Architecture

The Nucleus

R20 Delivery of Predefined Content Integration Architecture

The Nucleus

R21 Maintanance of Global/Local Master Data Model Design

Integration Architecture

The Nucleus

R22 Subscription of Master Data Products Applications Deep Integration

R23 Support of Interoperability Standards Applications Deep Integration

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Publication as managerial report

Co-signed by:

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Multi-perspective framework evaluation17

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.

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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.

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Contact

Dr.-Ing. Boris Otto

University of St. Gallen, Institute of Information Management

Tuck School of Business at Dartmouth College

Boris.Otto@unisg.ch

Boris.Otto@tuck.dartmouth.edu

+1 603 646 8991

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Appendix

Endnotes

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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 63-72.

3) Ibid.

4) Karel, R. "Introducing Master Data Management", Forester Research, Cambridge, MA.

5) Loshin, D. Master Data Management Morgan Kaufmann, Burlington, MA, 2008.

6) 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, 2008.

7) Loshin, D. Master Data Management Morgan Kaufmann, Burlington, MA, 2008.

8) 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, 2004.

9) 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, 2010.

10) Radcliffe, J. "Magic Quadrant for Master Data Management of Customer Data", G00206031, Gartner, Inc., Stamford, CT.

11) White, A. "Magic Quadrant for Master Data Management of Product Data", G00205921, 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 45-77.

13) 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. 180-192.

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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, 1992.

15) Schütte, R. Grundsätze ordnungsmässiger Referenzmodellierung: Konstruktion konfigurations- und anpassungsorientierter Modelle Gabler, Wiesbaden, Germany, 1998.

16) 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. 3-19.

17) 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. 118-139.