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
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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The initial situation in practice User Uncertainty1
Diverging Expectations
■ “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?”
“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?
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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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Background: MDM Systems
MDM Research Foci
Use Cases6,7
Architecture Patterns8,9
Analytical
Leading System
Operational
Central System Repository
Peer-to-peer
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Market Surveys10,11
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
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Q3/10
Q4/10
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
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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
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Design principles
Master Data as a Product
Deep Integration
Market for Master Data
Design Principles Process Quality
Subsidiarity
The “Nucleus”
Contextawareness
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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 Products
Data
Operations
Master Data as a Product
R12
Design and Maintenance of Global/Local Master Data Management Processes
Operations
Process Quality
Engineering
for
Master
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Strategic business requirements (cont’d) ID
Requirement
R13
Internal Customer Support
R14
Management Standards
Design Area
Supports Design Principle(s)
Operations
Master Data as a Product
Data
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
of
Business
Rules
for
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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
[email protected] [email protected] +1 603 646 8991
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Appendix
Endnotes
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Endnotes 1) 2)
3) 4) 5) 6) 7) 8)
9) 10) 11) 12) 13)
Friedman, T. "Q&A: Common Questions on Data Integration and Data Quality From Gartner's MDM Summit", Gartner, Inc., Stamford, CT. 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. Ibid. Karel, R. "Introducing Master Data Management", Forester Research, Cambridge, MA. Loshin, D. Master Data Management Morgan Kaufmann, Burlington, MA, 2008. 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. Loshin, D. Master Data Management Morgan Kaufmann, Burlington, MA, 2008. 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. 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. Radcliffe, J. "Magic Quadrant for Master Data Management of Customer Data", G00206031, Gartner, Inc., Stamford, CT. White, A. "Magic Quadrant for Master Data Management of Product Data", G00205921, Gartner, Inc., Stamford, CT. 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. 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.
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