The document outlines the strategic business requirements for master data management (MDM) systems, addressing user uncertainties and divergent expectations in MDM initiatives. It provides a framework for improving communication between companies and software vendors, highlighting design principles and essential requirements for effective MDM systems. The research emphasizes the importance of delivering high-quality master data and offers insights into operational challenges and necessary improvements in current MDM practices.

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
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3. The initial situation in practice
User Uncertainty1 Diverging Expectations
■ “What is the proper sequence of “We are flooded by invitations from MDM
activities in support of MDM? Must we software vendors to sit together and let
have solid data integration and data them present their solutions, which are
quality practices and architectures in always supposed to be the solution to all
place before dealing with MDM?” our problems. When we meet, it’s always
■ “Most of our current data integration the same: They present something we
requirements are batch-oriented in aren’t looking for. Then we tell them our
nature, as we work to physically understanding of the world and what our
consolidate silos of master data. What real requirements are -- what in return they
types of packaged data integration do not want or cannot share. And in the
tools will be most relevant for our end, everybody goes his own way, highly
purposes?” frustrated because they couldn’t sell their
■ “Has market consolidation already product, we didn’t get an answer to our
reached the point where the advantages problems, and both of us spent time in
of single-vendor stacks for MDM vain.”
outweigh the advantages of a best-of-
breed strategy?”
■ 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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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
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5. Background: MDM Systems
MDM Research Foci
Architecture
Use Cases6,7 Market Surveys10,11
Patterns8,9
Analytical Leading System
Operational Central System
Repository
Peer-to-peer
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6. Research process according to the principles of Design Science
Research12
ANALYSIS
■ 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
DESIGN & DEMONSTRATION
■ “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
EVALUATION
■ “Offline”
expert evaluation (via email, 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
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7. Structure of the framework of strategic business requirements for MDM
Business Context
Shortcomings of Strategic MDM Use
Current Solutions Cases
Design Principles
Strategic Business
Requirements
Framework
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8. The initial situation in practice
Current Shortcomings Use Cases
■ No downstream visibility of data
■ Poor business semantics management ■ Risk management and compliance
■ MDM and data quality management
separated ■ Integrated customer management
■ “Stovepipe” approach for MDM
architectures ■ Business process integration and
■ No consistent master data service
approach harmonization
■ No predefined content
■ No “on the fly” mapping and matching ■ Reporting
■ Poor support of centralized management
of decentralized/federated datasets ■ IT consolidation
■ No integrated business rules
management
■ Poor support of distinction between
“global” and “local” data
■ Poor support of compliance issues
■ Insufficient transition management
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9. Design principles
Master Data
as a Product
Deep Market for
Integration Master Data
Design
Principles
Process
Subsidiarity
Quality
The Context-
“Nucleus” awareness
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10. Strategic business requirements
Supports Design
ID Requirement Design Area
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 Controlling Process Quality
and Services
R5 Audit Management of Master Data Products and Controlling Process Quality
Services
R6 Management of Role Access Rights according to Organization Subsidiarity
Data Governance Roles
R7 Escalation Management Organization Subsidiarity
R8 Support of Usage Monitoring of Master Data Operations Process Quality
Products
R9 Maintenance for Context-Aware Master Data Operations Context Awareness
Products
R10 Gauging of Master Data Product consumption Operations Process Quality
R11 Requirements Engineering for Master Data Operations Master Data as a Product
Products
R12 Design and Maintenance of Global/Local Master Operations Process Quality
Data Management Processes
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11. Strategic business requirements (cont’d)
Supports Design
ID Requirement Design Area
Principle(s)
R13 Internal Customer Support Operations Master Data as a Product
R14 Management of Business Rules for Data Operations Process Quality
Standards
R15 Support of End-to-End Master Data Product Operations Context Awareness
Lifecycles
R16 Support of Master Data Provenance Tracing Operations Process Quality
R17 Data Standards Management Integration The Nucleus
Architecture
R18 Enforcement of Data Standards Integration The Nucleus
Architecture
R19 Bottom-up Data Modeling using Heuristics Integration The Nucleus
Architecture
R20 Delivery of Predefined Content Integration The Nucleus
Architecture
R21 Maintanance of Global/Local Master Data Model Integration The Nucleus
Design Architecture
R22 Subscription of Master Data Products Applications Deep Integration
R23 Support of Interoperability Standards Applications Deep Integration
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12. Publication as managerial report
Co-signed by:
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13. 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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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.
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15. 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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16. Appendix
 Endnotes
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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 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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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,
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.
Detroit, MI, 08/05/11, B. Otto / 18