Master Thesis - summary - Data Governance Maturity Model - Jan Rutger Merkus MSc 20151119 - English version

1. Master Thesis
Data Governance Maturity Model
Summary
Author : Jan Merkus BSc
Date : November 2015, final version
Master study : Faculty Management, Science & Technology (MST)
: programme Business Process Management and IT (BPM&IT)
1st
tutor : Prof. Dr. Rob Kusters
2nd
tutor : Dr. Ir. Werner Rutten
Examiner : Prof. Dr. Rob Kusters

2. Summary
Relevance
This research aims to contribute to the body of knowledge by adding new knowledge about
data governance in general and specifically a maturity model.
First, this research is requested by the research community. It indicates that there is no
unambiguous definition for data governance (Begg, 2009, Otto, 2011). Secondly, the literature
study indicated that maturity models for data governance exist, but these are based on best
practices. None are scientifically justified.
There is a need for this research by both larger organizations (Otto, 2011) and middle
and small organizations (Begg&Chaira, 2011). All kind of organisations lack both theoretical and
practical knowledge. And Data Governance does not only plays a role in organisations, but also
in exchanging data between organisations (Kooper&Maes, 2011). Also when organisations
store their data in the cloud data governance is very important (Begg&Chaira, 2011).
Objectives
On basis of the above the objective of the literature study is to define a theoretical model
which can assess data governance on basis of organisation maturity in order to give
recommendations. The objective of the empirical research is to verify the defined maturity
model for data governance in practice.
Problem definition
On basis of these objectives the following problem definition is formulated for which the main
question is:
How is data governance maturity assessed?
The answer is that the Data Governance Maturity Model (DGMM©) is a good start to assess
organisation maturity of data governance. On the one hand this answer is based on answers on
context- and sub-questions for literature research. On the other hand it is based on answers on
sub-questions for empirical research. These questions are answered as follows.
Data Governance
On basis of the body of knowledge a definition of data governance has been formulated.
Subject matter experts have acknowledged all dimensions of the DGMM. By doing so also the
definition of data governance is confirmed.
Maturity Model
On basis of analysis of relevant literature on maturity models the demands and models of
Huner et al (2009), Becker(2009) en Pöppelbuß(2011) are used as method for the design of a
maturity model for data governance. This method is based on literature over maturity models
from the domains of data governance.

3. Data Governance Maturity Model (DGMM©)
On basis of relevant literature a maturity model is designed with relevant dimensions, maturity
levels, qualifications and assessment criteria for growth in data governance. For this criteria
from domains related to data governance were translated to the dimensions of data
governance.
Assessment of organisation maturity of data governance
From the results of the empirical research can be concluded that experts in the dimensions of
data governance confirm that the DGMM is relevant and valid as measuring instrument for
assessing organisation maturity of data governance.
All dimensions were confirmed as relevant. Except for one, all qualifications were seen as
relevant, although some qualifications were unknown in the context of the research
organisation. All qualifications except one are acknowledged as possibilities to grow in
organisation maturity of data governance, although not always unanimous for each level of
maturity.
Evaluation Research
From evaluation of the research is concluded that, while using the selected research method,
the DGMM concerning the content is entirely verified in practice. This is investigated using
semi-structured interviews with experts in the same research organisation. Becasue of this it
can be stated that the DGMM is relevant, credible and it is designed according logical
reasoning. On basis of this can be concluded that the DGMM has a high internal validity,
reliability and credibility. But because the research is conducted in one large organisation
generalisation is not so high, despite theoretical generalisation from literature. Further, the
selected research method has created new knowledge on data governance. Namely a definition
and a maturity model as a measuring instrument which is practically useable.
Discussion
Only one qualification of the DGMM is not recognized as relevant. And only two new
dimensions are suggested by the experts. With that the question of completeness of the
DGMM can be raised. This is reason for further research in order to reconfirm or expand the
DGMM.
Recommendation for further research
On basis of the outcomes of above research the following recommendations for further
research are formulated. The interviewed experts have made recommendations for further
growth in organisation maturity of data governance, which are candidates for further research.
In order to raise the internal validity of the DGMM a group discussion among experts is
recommended. Also repetition of the research in other organisations will contribute to this.
Further participative research is recommended in which the researcher experiences practice.
And in further research respondents could be briefed about concepts of data governance in
order to prepare on interviews. By doing so it is expected that more practice experiences will be
collected. To raise generalisation and reliability of the DGMM it is recommended to repeat the
research over time, by other researchers and in other organisations. In this way a multi-case
study is conducted to confirm the results from this research and for generalisation.