A Reference Process Model for Master Data Management

Andreas Reichert, PD Dr.-Ing. Boris Otto, Prof. Dr. Hubert Österle

Leipzig

February 28, 2013

A Reference Process Model for Master Data Management

© IWI-HSG – Leipzig, February 28, 2013, Reichert, Otto, Österle / 2

Agenda

1. Introduction

2. Related Work

3. Research Methodology

4. Results Presentation

5. Conclusion and Outlook


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

Effective and efficient reporting


Legend: Data quality pitfalls (e. g. migrations, process touch points, poor corporate reporting.

Master Data Quality

Time

Project 1 Project 2 Project 3

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

Case of Bayer CropScience (cf. Brauer 2006)


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)

How to design master data management processes?


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

Design of data governance processes for material master data


Model Focus Assessment

(Dyché & Levy 2006) Customer data integration

No focus on activities (English 1999): Total Quality data Management (TQdM)

(Loshin 2007) Data governance

(Weber 2009) Data governance reference model

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

Otto et al. (2012) Software functionality


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

5. Evaluation


4.1 Overview of the Reference Process Model for Master Data Management

Data Life

Cycle

Data Support

Data

Architecture

Data Model

Data Quality

Assurance

Standards &

Guidelines

Strategic

Functions

1.1

2.1

2.2

2.3 Governance

Strategy

2.4

3.2

3.1

Operations

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

Monitor &

report data

quality

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

Provide

trainings

Provide

user

support

Provide

project

support

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

3.2.1 3.2.2 3.2.3 3.2.4


4.2 Iterative Design and Evaluation in Three Case Studies

Case A B C

Industry High Tech Engineering Retail

Headquarter Germany Germany Germany

Revenue 2011 [bn €] 3.2 2.2 42.0

Staff 2011 11,000 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

project


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

X X X

Process “Mass data changes” added to “Support” (new 3.2.5)

New process added as activity is performed on continuous base and should be covered by data management processes

X X


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

X


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

Applicable for large enterprises mainly


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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