Strategic Business Requirements for Master Data ...

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

Detroit, MI, 08/05/11, B. Otto / 2

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?


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


Background: MDM Systems

MDM Research Foci

Use Cases6,7

Architecture Patterns8,9

Analytical

Leading System

Operational

Central System Repository

Peer-to-peer


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


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


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


Design principles

Master Data as a Product

Deep Integration

Market for Master Data

Design Principles Process Quality

Subsidiarity

The “Nucleus”

Contextawareness


Strategic business requirements ID

Requirement

Design Area

Supports Design Principle(s)

R1

Support of Master Data Product Descriptions

Strategy


R2

Sourcing of Master Data Products

Strategy


R3

Integration of External Master Data Sources

Strategy


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


R12

Design and Maintenance of Global/Local Master Data Management Processes

Operations

Process Quality

Engineering

for

Master


Strategic business requirements (cont’d) ID

Requirement

R13

Internal Customer Support

R14

Management Standards

Design Area

Supports Design Principle(s)

Operations


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


The Nucleus

R19

Bottom-up Data Modeling using Heuristics


The Nucleus

R20

Delivery of Predefined Content


The Nucleus

R21

Maintanance of Global/Local Master Data Model Design


The Nucleus

R22

Subscription of Master Data Products

Applications

Deep Integration

R23

Support of Interoperability Standards

Applications

Deep Integration

of

Business

Rules

for


Publication as managerial report Co-signed by:


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.


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.


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


Appendix 

Endnotes


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.


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.
