Organizing Master Data Management

Organizing Master Data ManagementOrganizing Master Data Management
Findings from an Expert Survey
Dr. Boris Otto, Andreas Reichert
Sierre March 23rd 2010
Institute of Information Management
Chair of Prof Dr Hubert Österle
Sierre, March 23rd, 2010
Chair of Prof. Dr. Hubert Österle

Agenda
1 I t d ti1. Introduction
2. Background and Research Approachg pp
3. Result Presentation
4. Discussion and Outlook
5. Project Context
© CC CDQ2 – Sierre, March 23rd, 2010, Boris Otto, Andreas Reichert / 2

1.1 Selected business requirements for master data quality
Compliance to regulations
Reporting (“Single Source of Truth”)
Business process integration
Customer-centric business models (“360 Degree View”)
Corporate purchasingCorporate purchasing
IT consolidation

1.2 Motivation and research question
Master data management (MDM) is referred to as an application-independent
process for the description, ownership and management of core business
data entities1,2
Establishing MDM is a Business Engineering3 task comprising design
activities on strategic organizational and system levelactivities on strategic, organizational and system level
In doing so, companies are confronted with the following questions:
What is the scope in terms of master data classes?
Which tasks does the MDM organization cover?
How much capacity in terms of human resources is required to carry out the
tasks?
Whom does the MDM organization report to?
How should responsibilities be balanced between central and local control?
How do firms organize MDM?
1) DAMA. DAMA Data Management Body of Knowledge (DMBOK): Functional Framework, DAMA International, Lutz, FL, 2007.
2) Smith, H.A. and McKeen, J.D. Developments in Practice XXX: Master Data Management: Salvation Or Snake Oil? Communications of the Association for
Information Systems, 23 (4). 63-72.
3) Österle, H. Business Engineering: Transition to the Networked Enterprise. Electronic Markets, 6 (2). 14-16.

2.1 Background: Master data and MDM
Time reference Change frequency Volume volatility Existential
i d dindependence
Master Data low low low high
Transactional high medium high low
Data
g g
Inventory Data high high low low
MDM aims at creating an unambiguous understanding of a company’s core
entities1.entities .
1) Smith, H.A. and McKeen, J.D. Developments in Practice XXX: Master Data Management: Salvation Or Snake Oil? Communications of the Association for
Information Systems, 23 (4). 63-72.

2.2 Background: Organizational theory
The two major tasks of organizational design are the division of labor and
coordination1
The organization of a company materializes in its organizational structure and
in the process organization1
Grochla divides the goals of an organization into functional goalsGrochla divides the goals of an organization into functional goals
(“Sachziele”) and formal goals (“Formalziele”)2
One can distinguish between primary and secondary organizations
1) Galbraith, J.R. Designing organizations: an executive guide to strategy, structure, and process. Jossey-Bass, San Francisco, 2002.
2) Grochla, E. Grundlagen der organisatorischen Gestaltung. Poeschel, Stuttgart, 1982.

2.3 Research approach
Project context is the Competence Center Corporate Data Quality (CC CDQ)
Surveys objective is of descriptive naturey j p
Online questionnaire covering nine questions was used
Closed questions (except one for master data volumes) were used
Sample consisted of 38 experts in the MDM domain from the e-mail
distribution list of the CC CDQ
Return rate was 50 percentReturn rate was 50 percent
Questions aimed at answer the following:
Is MDM part of the primary organization and - if so - where is it located in the
organizational structure?
What organizational from has been chosen (line function, shared service etc.)?
What are the functional goals (in terms of tasks)?g ( )
What is the scope (in terms of number master data classes such as customer data,
material data etc. and of number of master data records)?
How many employees work in the MDM organization (both central and local)?How many employees work in the MDM organization (both central and local)?

2.4 Survey participants
Company Unit of Analysis
SIC
Code
SIC Description
Revenue 2008
[bn EUR]
Staff 2008 Country
Robert Bosch GmbH Corporation 36 Electrical Equipment and Components 45.0 283'000 Germany
ALSTOM Power Division 36 Electrical Equipment and Components n/a n/a Switzerland
Kuehne + Nagel Inc. Corporation 44 Water Transportation 14.0 54'000 Switzerland
Syngenta AG Corporation 28 Chemicals and Allied Products 12.0 24'000 Switzerland
Deutsche Telekom
AG
Corporation 48 Communications 18.0 45'000 Germany
I d t i l d C i l M hi d
Oerlikon Textile Division 35
Industrial and Commercial Machinery and
Computer Equip
0.6 7'500 Switzerland
Bayer CropScience Corporation 28 Chemicals and Allied Products 6.4 18'000 Germany
Mars Corporation 20 Food and Kindred Products n.a. n.a. Germany
Corning Inc Corporation 32 Stone Clay Glass and Concrete Products 4 1 27'000 USACorning Inc Corporation 32 Stone, Clay, Glass and Concrete Products 4.1 27 000 USA
B.Braun AG Corporation 80 Health Services 3.8 38'000 Germany
ABB Corporation 35
Industrial and Commercial Machinery and
Computer Equip
24.2 119'000 Switzerland
Geberit Corporation 39 Misc. Manufacturing Industries 1.6 5'700 Switzerlandp g
Nestle S.A. Corporation 20 Food and Kindred Products 73.0 283'000 Switzerland
PostFinance Corporation 60 Depository Institutions 1.5 2‘830 Switzerland
Deutsche Bahn AG Division 40 Railroad Transportation 33.5 240'000 Germany
BASF Corporation 28 Chemicals and Allied Products 62.3 106'000 Germanyp y
RWE AG Corporation 49 Electric, Gas & Sanitary Services 49.0 66'000 Germany
Royal Philips
Electronics
Corporation 36 Electrical Equipment and Components 26.0 116'000 Netherlands
Tchibo GmbH Corporation 51 Non-Durable Goods 3.6 12'000 Germany

3.1 Reporting lines
16%
Linked to centralIT or Information
Management
32%
5%
Management
Linked to anothercentral
departments (e.g.Purchasing,
Controlling)
11%
Controlling)
Linked to a businessdepartmentof
a businessunit
Linked to IT or InformationLinked to IT or Information
Managementin a business unit
Other
37%
n = 19.

3.2 Organizational form
5%
42%
21%
Line Function
ProjectOrganization42% ProjectOrganization
Shared Service
Staff Function
VirtualOrganization
16%
VirtualOrganization
Other
5%11%
n = 19.

3.3 Tasks
11%Other
84%
74%
Project support
Training of users
79%
8 %
Measurement and reporting of master data quality
j pp
84%
58%
D l t d i t f t d d d id li
Master data lifecycle activities (e.g. creation, maintenance, deactivation)
90%
84%
Development and maintenance of the master data strategy
Development and maintenance of standards and guidelines
74%Business user support
47%Application management for a master data management software
0% 20% 40% 60% 80% 100%
n = 19.

3.4 Scope
5%Other
63%Supplier/ vendor master data
68%
37%
Material and product master data
Organizational master data (e.g. cost center structures)
21%
68%
Human resources master data (e.g. employees, e-mail accounts)
p
47%Financial accounting master data (e.g. chart of accounts)
84%Customer master data
26%Asset master data
0% 20% 40% 60% 80% 100%
n = 19.

3.5 Team size
26%26%
32%
Less than 5Less than 5
Between 5 and 10
Between 10 and 20
More than 20More than 20
26%16%
n = 19.

3.6 Results summary
MDM is seen as both an organizational and technical topic.
Data quality management is considered to be an integral part of the MDMq y g g p
organization.
Companies do not specialize the MDM organization on certain master data
classes Instead more than 63% are responsible for at least 3 differentclasses. Instead, more than 63% are responsible for at least 3 different
master data classes.
MDM per se is not a new topic. 42 percent of the respondents state that MDM
activities have been carried out for more than 5 years.
MDM organizations are relatively big in size. More than 45 percent of the
companies employ more than 10 full time employeescompanies employ more than 10 full time employees.
No clear statement can be made regarding the positioning of MDM in the
organizational structure. 37 percent report to either a central or a local IT
fdepartment whereas 47 percent report to a business function such as
purchasing or financial accounting.

4.1 Discussion and outlook
Discussion
Results provide first insight into current status of organizing master data
tmanagement
Results are descriptive and form a starting point for future research
Limitations are due to the nature of expert interviews as a researchLimitations are due to the nature of expert interviews as a research
method1:
Intentionally selected individuals rather than random selection of
lsample
Typically small number of respondents
Outlook to future researchOutlook to future research
Case studies (e.g. on shared service center approaches)
Analysis of “formal goals”
Method support for the establishment of MDM
1) Meuser, M., Nagel, U.:(2002) :ExpertInneninterviews - vielfach erprobt, wenig bedacht. Ein Beitrag zur qualitativen Methodendiskussion. In: Bogner, A.,
Littig, B., Menz, W. (eds.): Das Experteninterview. Theorie, Methode, Anwendung. Leske und Budrich, Opladen, 71-93

5.1 Project context Competence Center Corporate Data Quality (CC CDQ)
Objective Development of strategies, concepts and solutions for corporate data qualityj p g , p p q y
management (CDQM)
Consortium Bayer CropScience AG (since 2006)
Beiersdorf AG (since 2008)
Daimler AG (2006 - 2008)Daimler AG (2006 - 2008)
DB Netz AG (since 2007)
Deutsche Telekom AG (2006 - 2009)
E.ON AG (2007 - 2008)
ETA SA (2006 - 2008)ETA SA (2006 2008)
Hewlett-Packard GmbH (since 2008)
IBM Deutschland GmbH (since 2006)
Migros-Genossenschafts-Bund (since 2009)
Nestlé SA (since 2008)( )
Novartis Pharma AG (since 2008)
Siemens Enterprise Communications GmbH & Co. KG (since 2010)
Syngenta AG (since 2009)
ZF Friedrichshafen AG (2007 - 2008)

Contact Person
Dr. Boris Otto
University of St. Gallen
Institute of Information Management
E-mail: boris.otto@unisg.ch@ g
Phone: +41 71 224 32 20
Andreas Reichert
University of St. Gallen
Institute of Information ManagementInstitute of Information Management
E-mail: andreas.reichert@unisg.ch
Phone: +41 71 224 38 80

Organizing Master Data Management

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