A Reference Process Model for Master Data Management

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Agenda

1. Introduction

2. Related Work

3. Research Methodology

4. Results Presentation

5. Conclusion and Outlook

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1.1 Business Requirements for Master Data



Master data describes key business objects in an enterprise (e.g. Stahlknecht &

Hasenkamp 1997; Mertens 1997)



Examples are product, material, customer, supplier, employee master data



Master data of high quality is important for meeting various business requirements (e.g.

Knolmayer & Röthlin 2006; Kokemüller 2010; Pula et al. 2003)



Compliance with legal provisions



Integrated customer management



Automated business processes

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Time Project 1 Project 2 Project 3

1.2 Difficulties in practice when it comes to managing master data quality

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1.3 Master Data Management must be organized



Master data management is an application-independent function (Smith & McKeen

2008)



The organizational structure of master data management has been research to some

extent



Empirical analysis regarding the positioning of master data management within an organization

(Otto & Reichert 2009)



Master data governance design (Otto 2011)

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1.4 Enterprises are in need of support in this matter

* Source: Workshop presentations at the CC CDQ Workshops by companies

Company

Main Challenges



Establishing a central master data Shared Service Center for

governance and operational tasks



Support of high quality master data for online sales channels



Central governance for new data processes



Set up of a central master data organization for material, customer,

and vendor master data due to changing business model, and hence,

processes



New organization of medical and safety division

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Model

Focus

Assessment

(Dyché & Levy 2006)

Customer data integration

No focus on activities

(English 1999):

Total Quality data Management (TQdM)

(Loshin 2007)

Data governance

2.1 Related Work in Research and Practice

Process models related to master data management

Role models related to master data management

Model

Focus

Assessment

ITIL

IT service management

No integrated process focus

(Batini & Scannapieco

2006)

Data quality management activities

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3.1 Research Methodology and Process

2009 ₂₀₁₀ ₂₀₁₁ 2012

1. Identify problem & motivate

1.1 Identification of challenges within practitioners community

2. Define objectives of a solution

2.1 Focus group A (2009-12-01)

2.2 Principles of orderly reference modeling A

6. Communication

6.1 Scientific paper at hand 4.1 Three participative case studies

3.1 Literature review

3.2 Principles of orderly reference modelling 3.3 Process map techniques

3.4 Focus groups B (2010-11-26), C (2011-11-24)

B C

5.1 Focus group C (2011-11-24) 5.2 Three participative case studies

5.3 Multi-perspective evaluation of reference models C

3. Design & development

4. Demonstration

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4.1 Overview of the Reference Process Model for Master Data Management

Data Life Cycle Data Architecture Data Model Data Quality Assurance Standards & Guidelines Strategic Functions 1.1 2.1 2.2 2.3 Governance Strategy 2.4 3.1 Develop and adapt vision Align w/ business & IT strategy Define strategic targets Set up responsibi-lities Define roadmap Develop communic. and change Adapt nomencla-ture Adapt data life cylce Adapt standards & guidelines Adapt authori-zation concept Adapt support processes Adapt measure-ment metrics Adapt reporting structures Define quality targets Initiate quality improve-ments Identify data require-ments

Model data Analyze implications Test & implement changes Roll out data model changes Identify business issues Identify require-ments Model data architecture Model workflows / UIs Analyze implications on change Roll out data architecture Test & implement Manage

requests Create data

Update data

Release

data Use data

Archive / delete data Adapt user trainings

Process Area Main Process Process

1 2 3 1.1.1 1.1.2 1.1.3 1.1.4 1.1.5 1.1.6 2.1.1 2.1.2 2.1.3 2.1.4 2.1.5 2.1.6 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.3.1 2.3.2 2.3.3 2.3.4 2.3.5 2.4.1 2.4.2 2.4.3 2.4.4 2.4.5 2.4.6 3.1.1 3.1.2 3.1.3 3.1.4 3.1.5 3.1.6

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4.2 Iterative Design and Evaluation in Three Case Studies

Case

A

B

C

Industry

High Tech

Engineering

Retail

Headquarter

Germany

Revenue 2011 [bn €]

3.2

2.2

42.0

Staff 2011

11,000

170,000

Role of main contact person for

the case study

Head of Enterprise

MDM

Head of Material

MDM

Project Manager

MDM Strategy

Initial situation

Specification of existing

data management

organization

Merger of two

internal data

management

organizations

Design of new data

management

organization within

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4.3 Design Decisions

Design Decision

Justification

A

B

C

Process “Define strategic

targets” removed (1.1.3)  Activities integrated in process “Align with business/IT strategy” No explicit MDM strategic targets required as they should be integrated in existing target systems

X

Process “Model Workflows/UIs (User Interfaces) moved from main process “Architecture” to “Standards & Guidelines” (2.4.3)

 Focus for activity is set on conceptual design rather than technical implementation aspects

 Technical implementation needs to be covered by IT-processes. Case A only covers the conceptual part of the workflow design. The implementation process will be described outside of this process

X

Process “Monitor & report” (in context of Quality Assurance) moved from main process “Support” to “Quality Assurance” (3.2.4)

 Mix of governance and operational activities in main process “Governance”

 However, focus is set on end-to-end process including both aspects

X

Process “Test & Implement” (in context Architecture) removed (2.4.5)

 Testing activities defined within IT-processes and do not need to be covered by data management processes

 Removal will eliminate double definitions within company

X X

Processes of main process

“Life Cycle” renamed (3.1)  Naming of processes aligned with company specific naming conventions as processes were already defined

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4.3 Design Decisions (continued)

Design Decision

Justification

A

B

C

Process “Develop and adapt

vision” removed (1.1.1)  Company strategies not defined by visions but by strategic targets X Processes “Adapt data life

cycle”, “Adapt standards and guidelines”, “User trainings”, and “Support Processes” merged to “Standards for operational processes” (2.1.2 - 2.1.6)

 Activities of all processes remain existing  Goal is simplification of process model

 Description of all activities, which have been merged to the new process, will be created on the work description level, which will underlay the process model for execution of processes (including process flows, responsibilities, etc)

X

Processes “Test and implement (data model)” and “Roll out data model changes” removed (2.3.4 - 2.3.5)

 Activities defined within IT service portfolio outside of this process model

 As activities are already defined, they do not need to be covered within this structure

X

Main process “Data

Architecture” removed (2.4)  Activities defined within IT service portfolio Clear separation between business requirements and modeling of data and IT realization (integration architecture etc.)

X

Process “Data analysis” in main process “Support” added (new 3.2.6)

 Requests for one-time analysis of master data as service offering defined which are not covered by standard reports

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5.1 Conclusion and Outlook



Results



The reference model supports the design process of master data managements organizations

as well as the specification of existing structures



The reference model was evaluated from an economic, deployment, engineering and

epistemological perspective (cf. Frank 2006) by researchers and practitioners



Contribution



Innovative artifact in a relevant field of research



Explication of the design process



Engaged scholarship case



Limitations



Qualitative justification of design decisions



Further design/test cycles necessary

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PD Dr.-Ing. Boris Otto

University of St. Gallen

Institute of Information Management

[email protected]

+41 71 224 3220

Your Speaker

This research was supported by the Competence Center Corporate Data Quality (CC CDQ) at the

University of St. Gallen.

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References

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DYCHÉ, J. & LEVY, E. 2006. Customer Data Integration, Hoboken (USA), John Wiley.

ENGLISH, L. P. 1999. Improving Data Warehouse and Business Information Quality, New York et al., Wiley.

FRANK, U. 2006. Evaluation of Reference Models. In: FETTKE, P. & LOOS, P. (eds.) Reference Modeling for Business Systems Analysis. Hershey, PA: IGI Publishing.

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PULA, E. N., STONE, M. & FOSS, B. 2003. Customer data management in practice: An insurance case study. J. of Database Mark., 10, 327-341.

SMITH, H. A. & MCKEEN, J. D. 2008. Developments in Practice XXX: Master Data Management: Salvation Or Snake Oil? Communications of the AIS, 23, 63-72.