Data Governance Maturity Model Thesis

Thesis
Data Governance Maturity Model
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Author : Jan Merkus MSc
Studentnumber : 838074058
Date : November 2015, final version
Master : Faculty Management, Science & Technology (MST)
: Business Process Management and IT (BPM&IT)
1e mentor : prof. dr. Rob Kusters
2e mentor : dr. Ir. Werner Rutten
Examinator : prof. dr. Rob Kusters

Thesis on Data Governance Maturity Model by Jan Rutger Merkus – Nov 2015
http://datagovernancematurity.wordpress.com/ 2 of 75
Index
Preface ............................................................................................................................................ 4
Resume............................................................................................................................................ 5
Introduction .................................................................................................................................... 7
1.1 Background............................................................................................................................ 7
1.2 Relevance .............................................................................................................................. 7
1.3 Objective ............................................................................................................................... 7
1.4 Research Question ................................................................................................................ 8
1.5 Research organisation........................................................................................................... 9
1.6 Structure................................................................................................................................ 9
2. Data Governance (DG) & Maturity Model................................................................................ 11
2.1 Definition data governance................................................................................................. 11
2.2 Definition maturity model................................................................................................... 15
2.3 Data Governance Maturity Model...................................................................................... 18
3. Method Empirical Research...................................................................................................... 23
3.1 Research strategy................................................................................................................ 23
3.2 Data Collection Method ...................................................................................................... 26
3.3 Reliability and Validity Data Collected ................................................................................ 29
3.4 Data Analysis ....................................................................................................................... 31
4. Research Results ....................................................................................................................... 33
4.1 Global description research ................................................................................................ 33
4.2 Background research organization ..................................................................................... 33
4.3 Results sub-question I ......................................................................................................... 36
4.4 Results subquestion II ......................................................................................................... 38
5. Discussion.................................................................................................................................. 40
5.1 Interpretation of data......................................................................................................... 40
5.2 Research evaluation............................................................................................................ 41
6. Conclusions & Recommendations ............................................................................................ 46
6.1 Conclusions.......................................................................................................................... 46
6.2 Discussion............................................................................................................................ 47
6.3 Recommendations for Future Research ............................................................................. 47

7. Reflection .................................................................................................................................. 48
7.1 Meaning research outcomes............................................................................................... 48
7.2 Processreflection................................................................................................................. 48
References .................................................................................................................................... 49
Appendix I Definition Dimensions Data Governance ................................................................... 52
Annex II Quality and design principles maturity models into BPM.............................................. 54
Annex III Data Governance Maturity Model (DGMM).................................................................. 59
Bijlage IV Interviewprotocol ......................................................................................................... 69
Bijlage V DGMM Outcome Assessment........................................................................................ 75

Preface
This thesis is the final assignment of my master in Business Process Management & IT at the
Open University in The Netherlands. I started this study with the motivation to deepen my
knowledge of business and IT. In my bachelor of science in logistics management and IT and in
my career so far this subject always fascinated me. This subject fascinated me so much that it
has been possible to complete this study next to a full-time job and being the father of four
children.
At the same time this thesis is the start of something new. I chose the subject to follow up on
graduation. The subject is scientifically wasteland, which has loosened the pioneer in me to
develop it. And the fundamental research of this thesis gives rise to further investigation.
This thesis was first established under the supervision of Prof. Rob Kusters, who I thank the
most. On his instructions I discovered the world of scientific research and the immense space
that exists therein. I also want to thank dr.ir. Werner Rutten for his work as second supervisor.
I would also like to thank the respondents of the research organizations for their invaluable
contribution to this research, here left anonymous due to commercial reasons.
And I want to thank my partner Annette who, in spite of our busy family life with four children,
her job and her studies, every year left me the time and space to complete my studies.
Jan Rutger Merkus MSc

Resume
Relevance
This research aims to contribute to science by adding new knowledge about data governance
and in particular a maturity model.
First of all, there is a need for this research in the research community. It indicates that there is
still no clear definition of data governance (Begg, 2009, Otto, 2011). Further literature pointed
out that, practical data governance maturity models exist but none all are scientifically sound.
Furthermore, there is the practical need for this study in both large organizations (Otto, 2011)
as in small and medium businesses (Begg & Chaira, 2011). All kind of organizations lack both
theoretical and practical knowledge. And data governance not only plays a role within
organizations but also in the exchange of information between organizations (Kooper & Maes,
2011). Even when organizations offer their data in the cloud data governance plays an
important role (Begg & Chaira, 2011).
Objective
Based on this, the objective of the literature review is to define a model to assess data
governance based on organizational maturity to be able to give recommendations. The
objective of the empirical research is designing a maturity model to assess data governance in
practice.
Research Question
On basis of this objective the next research question is formulated:
How is data governance maturity assessed?
The answer is that the Data Governance Maturity Model provides a good take-off for assessing
organizational maturity of data governance.
This answer is based on answers on research questions deducted from the main research
question for literature research and for empirical research. These questions are answered as
follows.
Data Governance
A definition of data governance is composed on the basis of literature. And experts have
confirmed all dimensions of DGMM, what also confirms the definition of data governance.
Maturity Model
Based on analysis of the literature the design requirements and models of Huner et al (2009),
Becker (2009) and Pöppelbuß (2011) are adopted as methods for the design of a maturity
model for data governance. This method is based on literature about maturity models in the
domains of data governance.

Data Governance Maturity Model (DGMM)
Based on the literature a maturity model is designed with relevant dimensions, levels,
qualifications and criteria to grow in data governance. A translation was made from maturity
assessment criteria from related domains to the dimensions of data governance.
Assessment of organizational maturity data governance
From results of empirical research can be derived that experts on the dimensions of data
governance confirm that DGMM is relevant and valid as a measure to assess the organizational
maturity of data governance. All dimensions were seen as relevant. All but one were considered
relevant qualifications, although some qualifications are still unknown in the context of the
research organization. All qualifications except one have been identified as growth
opportunities in organizational maturity of data governance, although not always unanimous
for each level of maturity.
Research evaluation
In evaluation of the investigation is concluded that with the research method used the DGMM
entirely tested in practice. This by semi-structured interviews with experts within the same
suitable research organization. The DGMM is thus relevant, credible and it is structured in a
logical reasoning. On this basis is concluded that the DGMM has a high internal validity,
reliability and credibility. But because the study is conducted in one organization the
generalizability not so high, despite theoretical generalization from the literature. Further, the
chosen research method returned new knowledge about data governance. Namely a definition
of data governance and a maturity model as measuring instrument for practice.
Discussion
Only one qualification in the DGMM is not recognized as relevant. And there are only
mentioned two new dimensions. Thus the question arises whether the DGMM is complete. This
would qualify for further research to confirm or extend the DGMM.
Recommendations for future research
Based on the results of the above study, the following recommendations were formulated for
further research. Experts have made recommendations to grow into organization maturity
eligible for further investigation. A group discussion with experts is recommended for to
increase the internal validity of the DGMM. Also repeating the survey for other organizations
contributes to this. It is further recommended to conduct participatory research in which the
researcher experiences practical situations. And in further research respondents could be
informed about terms of data governance for preparation on the interviews. This would results
in more relevant examples from practice. To increase the generalizability and reliability of the
DGMM it is recommended to repeat the same examination in time, by another investigator and
for other organizations. This contributes to the multi-case study for confirmation and
generalization of the results of this study.

Introduction
1.1 Background
Data plays an increasingly important role in everyday life. Data is also increasingly in the
spotlight with such a hype as big data. Thus increasing the importance of data quality. And data
quality requires continuous maintenance in the form of data management (Dahlberg,
2011). Also, it is important that value of data is safeguarded being an assets. Both happens with
data governance (Otto, 2011; Otto, 2013).
At the same time is data governance a little researched topic that requires further
definition and validation (Begg & Caira, 2012). Organizations do not have measuring
instruments to review the status of data governance (Otto, 2011). Therefore, additional
qualitative and quantitative research is needed (Otto, 2013). Researchers think thereby to
select the right governance model, depending on the type of organization (Kooper & Maes,
2011). And as requirements for data governance change over time as a result of changing
organizations, it is necessary to deal with the maturity of organization (Otto, 2013).
1.2 Relevance
This research aims to contribute to science by adding new knowledge about data governance
and in particular a maturity model.
First of all, there is a need for this research in the research community. It indicates that
still no clear definition exists for data governance (Begg, 2009, Otto, 2011). Further literature
pointed out that, while practical data governance maturity models exist, none are scientifically
sound, i.e. not freely reproduceable, not empirically tested, etc.
Furthermore, there is the practical need for this study in both large organizations (Otto,
2011) as small and medium businesses (Begg & Chaira, 2011). All types of organizations lack
both theoretical and practical knowledge. And data governance not only plays a role within
organizations but also in the exchange of information between organizations (Kooper & Maes,
2011). Even when organizations offer their data in the cloud data governance plays an
important role (Begg & Chaira, 2011).
1.3 Objective
Based on the relevance of the objective of this research is formulated as follows..
The objective of the literature review is to define a framework for assessing data governance
based on organizational maturity (= maturity) and to give recommendations for organizational
growth in data governance.
The objective of the empirical research is to test the maturity model for data governance into
practice, with the following objectives:

I. The design of a maturity model to assess the status of data governance within an
organization. In the literature there is no references of scientific maturity models of data
governance found.
II. Give recommendations for valuable additions to the DGMM for organizations to grow in
data governance. In literature there is no scientifically based research on this subject
found and this research provides new knowledge.
1.4 Research Question
On the basis of this objective, the following research question arises. The main question is:
To answer this main question it is divided into context and sub-questions for literature and
questions for empirical research.
For the literature review, the following questions are asked:
Context questions:
1. What is the definition of data governance?
2. What is the definition of a maturity model for data governance?
Content questions
3. What are the relevant dimensions and levels of data governance maturity?
4. What are the relevant assessment criteria for data governance maturity?
For the empirical research the following sub-questions have been formulated:
I. Exploratory research to validate that the organization maturity of data governance
maturity can be assessed with the dimensions, levels and criteria from the literature in
the form of the DGMM.
There are many practice-oriented models for data governance maturity known but never
before a maturity model for data governance has been designed which is scientifically
justified. Therefore, based on literature a DGMM is designed on the basis of relevant literature
on data governance domains. For these domains statements are formulated in the form of
assessment criteria which are to be tested in practice for validity and completeness.
The objective of sub-question is to explore whether the established DGMM suffices as
measuring instrument or whether adjustments are needed to the model. The result of this
investigation is whether the dimensions, levels and criteria in the DGMM make it possible to
assess organizational maturity of data governance.
II. Exploratory research to discover what are valuable additions to the DGMM for
organizations to grow in data governance

The scientifically justified method for the design of a maturity model as established in the
literature asks as third key component recommendations for growing into maturity. In the
literature there are no recommendations for known data governance.
The objective of this sub-question is exploratory research to discover new knowledge in
practice on the basis of the DGMM. The proposed answers are additions to the DGMM in the
form of specific dimensions or qualifications to grow in organizational maturity in data
governance as a subjects for further research.
1.5 Research organisation
The research organization has agreed on reporting the survey outcomes anonymously due to
commercial reasons. Therefore, here are listed some characteristics of the research
organization, without making able to trace the organization involved.
- The organization is an internationally organization operating in over 20 countries with
subsidiaries across six continents.
- The organization produces new products and provides maintenance services.
- The clientele ranges from private to public customers of different nationalities.
- Business continuity is more important than profit.
- The organization has mainly grown through acquisitions of other companies to increase
international market share and brand awareness.
- The organization is best to characterize as a network organisation in which affiliates are
profit and cost centers.
- Quality requirements are imposed by the group in order to secure the brand.
- The following IT issues are organized at the holding level: IT infrastructure, network, ERP
system and production applications. Other IT services are decentralized: accounting,
applications, legacy systems, ICT services.
- For subsidiaries acquired the policy for automated exchange of data from distributed
systems is based on interfacing rather than on systems integration.
1.6 Structure
The sub-questions of the main research question are detailed in the following chapters.
In chapter 2 are the questions answered in the literature. The chapter concludes with the
results of the literature which is formed by the maturity model of data governance.
Chapter 3 describes the method of empirical research. Each part of the research design is
described in the following sections: research strategy, data collection method, reliability and
validity, data analysis. It concludes with an outlook to the expected results and conclusions of
the investigation.
Chapter 4 presents the results of empirical research. First of all, a brief description of the data
processing is given. Then follows the analysis of the summarized data about which model
element is confirmed or which one needs improvement. In addition, other notable findings are
included for complementation and missing items are discussed.

In chapter 5, in the discussion on the results portion conclusions are drawn in the first
paragraph. At the end of this section conclusions and recommendations are given for future
research. In the next section this research is assessed, followed by reflection and conclusions
and recommendations for the method for further research.
In chapter 6, the main question of the problem is answered on the basis of the findings of the
empirical research. This also follows the final verdict on the research method. And suggests
recommendations for further research after discussing the claims.
Finally, in chapter 7 follows the reflection on the entire research and lessons learned.

2. Data Governance (DG) & Maturity Model
In each section of this chapter a context or sub-question is answered with the results of the
literature review.
Research of scientific literature on Data Governance (DG) shows interest in this topic. Not only
in recent years but still scientist are researching this topic.
The attention for DG is explained by its growing importance. According to Begg (2011) DG is of
value for the survival of an organization. Organizations use DG to control the organization.
Particularly in large companies know data quality problems through mergers and distributed
systems for which DG is needed. Also Korhonen (2013) noted that data handling is inadequate
and needs DG. Large, middle and small sizes organizations need quality data for their processes
and due to administrative and legal obligations. The unilateral focus on data aspects need to
change to a focus on organizational maturity in handling data [Huner et al (2009)].
The many interests of stakeholders from the business relevant in executing data quality
management in the technical IT domain are translated by DG [Wende (2007)]. Also, data quality
is important for compliance, customer policy and reporting or business processes [Otto
(2011b)]. According to Gregory (2011a), DG is part of the framework for corporate governance,
risk management and compliance (GRC) of Racz (2010). The goal of DG and GRC is adding value
and reducing risk. Around data arise prejudice risks like loss or theft, privacy violation, violation
of law, low data quality, but also liability.
2.1 Definition data governance
But what is DG? According to Begg (2011) the definition of data governance is still
emerging. This is recently underlined by Otto (2013) indicating that a standard definition for DG
does not exist in the science and practice. So it is necessary to further define DG.
2.1.1 Corporate Governance - Risk management – Compliance (CGRC)
To find the definition of DG it requires positioning among other organizational activities.
Wende (2007) already stated that DG determines organization-wide guidelines and standards
for data quality management. DG assures specific compliance with corporate strategy and laws
about driving data. With DG organizations implement organization-wide responsibilities for
data quality management (DQM) which include both professionals from the business and IT.
Even Weber et al. (2009) recognized that DG combines business-driven IT technical
perspectives. Because global presence requires harmonization of (master) data for business
networking, customer management, decision making and business intelligence. This is done by
Business and IT working together in DG for DQM. Gregory (2011a) confirms this: data
governance also looks in his opinion at the interests of the owners and users of the data, the
business. He shows the following interactions. DG is important for corporate governance, risk
management and compliance (CGRC) because it ensures administrative information and self-
control from CGRC to exploit the maximum value of DG. According to him, the first priorities are
risk assessment, data audit and compliance gap analysis.

Vitolla (2014) states that recent holistic risk management has become part of corporate
governance. IT Governance as part of many business activities is an integral part of corporate
governance and thus falls within the scope of holistic risk management. Enterprise Risk
Management (ERM) inevitably requires the existence of an effective IT Governance system. In
the same way ERM also includes data governance. Moreover, DG deals with value of data and
value means risk. Risk management is therefore applicable to DG.
From the above we can conclude that DG is part of the greater whole of Corporate Governance
(Enterprise) Risk Management and Compliance.
Racz (2010a) suggested that the definition of GRC is an integrated, holistic approach to
enterprise-wide governance, risk and compliance. GRC ensures that organizations act ethically
in accordance with his risk assessment, internal policies and external regulations. This by
aligning strategy, processes, technology (product) and men (people). This increases efficiency
and effectiveness. For this he set up the model for GRC as in Figure 1.
Figuur 1 - Frame of reference for integrated GRC [Racz(2010a)]
GRC distinguishes and controls the domains strategy, processes, people and
technology in the GRC organization governance model. Gregory (2011a) supports
this theory. According to him, people, processes and technology are core capabilities
for data governance. Gregory elaborates the domain of people in the form of
organizational vision and ownership of the business, processes in the form of the
data life cycle and technology in the form of tools. In subsequent research Racz
(2010b) has found that IT GRC is about Information Security, IT Compliance, IT and
Data Governance, IT Risk Management and IT overhaul. IT GRC is hereby indirectly
aligned with the organization operations. Therefore, the dimensions strategy,
processes, technology and people are of interest for data governance.

2.1.2 Limitation
DG is limited, it is not about software tools but about the underlying data [Huner et al (2009)].
And there is a difference between information governance and data governance, the first
involves interactions the second on assets [Kooper (2011)]. And DG partially overlaps with IT
Governance and Data Quality, but not for the perspective, interest and understanding of data
from the business stakeholders [Wende (2007)].
2.1.3 Data as Asset
Maes and Kooper stated that DG sees data as an enterprise asset [Kooper et al (2011)]. Others
confirm this. According Otto, the formal goal of DG is to increase the value of data assets [Otto
(2011a),(2011b)]. DG manages data assets and maximizes data value through quality control.
And further, Otto (2011c): "DG is based on data as a company asset with value". According to
him, the relationship between DG and data quality management is based on the value of data
that, because of its value, needs to be managed and its quality needs to be monitored and
secured.
Also Korhonen (2013) identifies data as an asset, to separate from other IT assets based on
business value. Later Otto (2013) presents once again that DG is focused on maximizing the
value of data assets in organizations. As part of DG data quality management looks at the
quality of data in itself and it is a sub function of data management. But data management only
includes planning, monitoring and provision of data assets. DG thus comprises the maximizing
value of data as asset.
Interesting to note is that Otto (2013) proposed that the value of data is determined by the use
thereof. This use is linked to the data quality, which is defined as fitness for use. Wende (2007)
states that DG ensures that relevant, high-quality information products are delivered to users.
Here, the use of data plays an evaluative role again. DG increases the value of data as asset by
increasing data quality so that the use of it increases.
2.1.4 Organization, Domains and Accountabilities
Research shows that DG comprises organization, roles, and decision-making in order to control
the value of data.
Wende (2007) states that DG helps companies with structuring and documenting data
quality responsibilities. A DG model should consist of roles, accountabilities and responsibilities.
She claims that such a model is company specific. Weber et al. (2009) state that DG specifies
the framework authorities and responsibilities to encourage desirable behavior in the use of
data. To promote this broad policy guidelines and standards are to be developed under DG
organization that are consistent with the mission, strategy, values, norms and culture of the
organization. So DG organizes policies, roles, responsibilities and authorities at the strategic
level to better handle data.
According Khatri & Brown (2010) DG includes five interrelated decision-making
domains: data principles, data quality, metadata, data access and data life cycle. According
them DG determines who is authorized and who can be held responsible for organizational
decision making about data assets.

Otto (2011b) builds on this and lets DG to answer 3 questions. The first is that decisions
are taken in data management, data quality, data quality meetings, metadata, master data,
authorization and data lifecycle. The second is what roles there are in the form of data
stewards, data owners and data committees. The third question which authorizations and
responsibilities are delegated according to the RACI method: Responsible, Accountable,
Consulted and Informed.
In short, DG specifies who decide on dates for the tasks and duties are.
According to Otto (2011a), DG 3 has dimensions. The first dimension is the formal goal,
namely increasing the value of data assets in which creating access rights is the functional
purpose. The second dimension is organization structure in the form of decision-making
position, organization, task allocation of roles and committees. The third dimension is roles and
committees. In addition, DG takes place at a strategic and tactical level, centralized and often
with metadata. Organizing DG is an assurance of data quality and securing data as assets,
according Otto (2011c). He bases his findings on somewhat older work of Weil (2004), on the
practice of commercial organizations and continue on their own work.
Otto (2013) argues that DG provides a decision framework for data management. It's about
what decisions are made and by whom. DM is about making decisions and implementing them.
Korhonen (2013) confirmes that DG controls the allocation of decision rights and
responsibilities of data quality. There are several naming DG roles: executive sponsor, DG
Council, Chief Steward, Business Data Steward, Technical Steward. Korhonen moreover relies
on work by Wende (2007), Otto (2011) and Griffin.
In short, DG domains are data management, data principles, data quality, metadata, master
data, data access and data life cycle. DG determines who is authorized and who is responsible
for making decisions about data assets.
2.1.5 Definition Data Governance
Literature reads the following definitions for Data Governance:
- Otto (2011): a companywide framework for assigning decision-related rights and duties
in order to be able to adequately handle data as a company asset.
- Gregory(2011): the business practice that defines and manages strategies for people,
processes and technologies to ensure that valuable data assets are formally protected
and managed throughout the organization.
- Korhonen(2013): an organizational approach to data and information management that
formalizes a set of policies and procedures to encompass the full life cycle of data, from
acquisition to use and to disposal.
None of the three definitions contain all the basic elements put forward by the scientific
literature. Therefore it is advisable to tighten the definition of data governance. As described
above, the following elements are relevant for DG:

- DG component is part of the larger whole of Corporate Governance (Enterprise) Risk
Management and Compliance.
- The dimensions strategy, processes, technology and people are of interest for data
governance.
- DG involves maximizing value of data as an asset.
- DG increases the value of data as asset by increasing data quality to increase its use.
- DG organized policies, roles, responsibilities and accountibilities at the strategic level to
better handle data.
- DG domains are data management, data principles, data quality, metadata, data access
and data lifecycle.
- DG determines who is authorized and who is responsible for decisions on data assets.
Therefore, based on literature, we propose the following definition for data governance:
Data Governance is
from Corporate Governance, Risk Management and Compliance
determining the strategy for processes, people and technology
to maximize the value of data assets
by arranging organization, responsibilities and accountibilities
for the domains data management, data principles, data quality, metadata, master
data, data access and data lifecycle.
2.2 Definition maturity model
2.2.1 Current state of knowledge
Literature study shows that currently no data governance maturity model (DGMM) exists.
However, there are available all kinds of maturity models for data governance, but they are not
all scientifically sound, i.e. not freely reproduceable, not empirically tested, etc.
Two currents seem to emerge in this domain. The Anglo-Saxon flow relies mainly on
best practices for a data governance maturity model. These models are often drawn up by
businesses IS and IS consulting firms, but have little or no scientific basis. For example, IBM
models Data Flux, SAP, Gartner, Gregory et al. (2010, 2011, 2012).
The European Movement is trying to apply scientifically sound methodologies and to
develop empirical research-based models, but limits itself to maturity models for data quality
management [Otto (2013)]. Here, cooperation is established with the European Foundation for
Quality Management (EFQM) Excellence Model. A DGMM is avoided because establishing Data
Governance would be too dependent on internal and external contingency factors (Otto
(2011c)). A maturity model would be too rigid because too many fixed criteria. However, Otto
(2013) incorporates data governance as part of quality model EFQM. Otto claims that the
selection of relevant criteria depends on the situation of an organization. And then it would be
depending on the situation which dimensions or criteria are (always) valid and which are not.
The EFQM Excellence Model is a quality model, a management model or even a strategic model
for organizational management. The EFQM Excellence Model is indeed a non-normative

management model, but is also based on 9 fixed criteria. And with that rigidity is not a valid
argument anymore. Moreover governance transcends quality and / or strategy. Governance,
after all, is about governing an organization. The Governance, Risk Management and
Compliance (GRC) model Razc (2011a) shows that organizations are controlled with strategy,
people, processes and technology. The EFQM quality model, as a strategic model, could be part
of that.
2.2.2 Maturity Models
According to Huner et al (2009) maturity models are intended to describe maturity of
organizations, to assess them and to give directions on how to grow. Becker (2009) is in line
with this and states that maturity models determine the status quo of an organization to make
guidelines to improve. Also according Pöppelbuß (2011) are maturity models growth models for
organizations. According him applications of maturity models are primarily descriptive if it is
used as an assessment tool of the as-is situation, secondly it is prescriptive in providing
guidelines for improving and thirdly comparative for benchmarking.
Huner et al (2009) have analyzed a lot of existing maturity models for their application in
data governance relevant domains (BPM), in particular data quality management. He indicates
that maturity model are usually validated in case studies.
According Huner et al (2009), Becker (2009) and Pöppelbuß (2011) is a maturity model a
frame with maturity levels and dimensions, divided into qualifications. According Pöppelbuß
(2011), organizational skills develop according a anticipated, desirable or logical growth path
from one level to the next level, so called maturity levels .
A dimension according Pöppelbuß (2011) is a “central construct related to the application
domain" that knows a specific 'granularity' which Pöppelbuß (2011) calls qualities. Otto (2011)
calls them qualifications. This study opted for qualifications because the term quality can be
confusing as a term. The degree of organization growth per qualification is determined by one
or more assessment criteria per maturity level, according to Becker (2009). The basic design of
an organizational maturity model is shown below in Table 1 Definition Maturity Model.
Table 1 Definition Maturity Model
Dimensions Qualifications Maturity
level 1
Maturity
level 2
Maturity
level 3
Dimension A Qualification A1 assessment criteria
A1 – level 1
assessment criteria
A1 – level 2
assessment criteria
A1 – level 3
Qualification A2 assessment criteria
A2 – level 1
assessment criteria
A2 – level 2
assessment criteria
A2 – level 3
Dimension B Qualification B1 assessment criteria
B1 – level 1
assessment criteria
B1 – level 2
assessment criteria
B1 – level 3

2.2.3 Developping a new Maturity Model
Because no existing scientifically justified model for data governance maturity is found in
literature such a maturity model for data governance is developed in this study.
Although there are some practical oriented maturity models for data governance, by lack of
scientific grounding we disregard them in this study and also because the models from practice
do not meet the requirements of scientific model development methods, such as controllability
or free reproduceability.
The DGMM is developed in accordance with a scientifically sound method. This design
method is based on scientific literature on maturity models in business process management.
Furthermore DGMM is based on existing science-based maturity models from domains that are
derived from the definition of data governance prepared in section 2.1. The scientific grounding
of the applied development method is justified in paragragh 2.2. The application for the DGMM
follows in section 2.3.
2.2.4 Quality and design principles maturity models into BPM
According Pöppelbuß (2011) there are hundreds of maturity models of varying quality and
design needs quality requirements. Becker (2009) also saw a need for a new form of maturity
models, earlier versions need improvement. To this end, Becker (2009) has mapped the design
process of maturity models. And research of Pöppelbuß (2011) mapped design principles by
means of a checklist. Becker (2009) and Pöppelbuß (2011) embrace the method of Huner et al
(2009) completing each other's methods. The quality and design principles that are mentioned
in this literature are all gathered in Appendix II Quality and design principles maturity models in
business process management (BPM).
2.2.5 Design method: Roadmap
To develop a scientific sound design method for the preparation of a maturity model for data
governance this study uses the quality and design principles for maturity models of business
process management from the literature of Huner et al (2009), Becker (2009) and Pöppelbuß
(2011) .
The DGMM is designed according the maturity model like Becker (2009) defines (see Annex II),
allthough mirrored diagonally with maturity levels horizontally and dimensions and
qualifications listed vertically. The DGMM is also designed according to the roadmap of Becker
(2009) in Appendix II. Step R2 of the roadmap of Becker (2009) has been refined to achieve a
determination of all elements in the maturity model of Becker (2009). And the following design
steps are determined based on the design principles of Pöppelbuß (2011).

1- As first design step in the development of the basic maturity model for data governance
maturity all possible dimensions are collected from literature on maturity models from
domains that are derived from the definition of data governance (design principle 1.2a,
Pöppelbuß (2011)).
2- As a second design step in the preparation of the base maturity model the maturity levels
are set. (design principle 1.2b, Pöppelbuß (2011)). In step R2 of the roadmap of Becker
(2009) all maturity levels found in the literature about maturity models are collected from
the domains of data governance. Then this list of maturity levels is transformed into a
maturity level classification based on similarities in meaning.
3- As third step in the preparation of the basic maturity model all the qualifications that
correspond to the dimensions found in step 1 are collected (design principle 1.2c,
Pöppelbuß (2011)).
4- As fourth step in the preparation of the descriptive part of the maturity model the
assessment criteria required to perform the assessment are collected (design principle 2.2b,
Pöppelbuß (2011)). For that all assessment criteria found in literature about maturity
models in domains of data governance are collected.
5- As fifth design step the long list of qualifications and assessment criteria are collected from
step 3 and 4 and reduced to a maturity model that can be used as organization assessment
instrument. The qualifications found are classified per dimension and as much as possible
under similar headings on the basis of similarities in meaning.
The first list with assessment criteria are reduced according to the classification of the
qualifications to a list of new operationalized assessment criteria. Here, the first assessment
criteria for each maturity level and a new qualification are summarized or aggregated into new
assessment criteria based on similarities in meaning.
During the design along with this plan, all quality and design principles of Huner et al (2009),
Becker (2009) and Pöppelbuß (2011) from Appendix II are included as conditions, except the
following:
 Due to the limited time available for the investigation, there is no iterative improvement
in design principle as one of Becker (2009).
 For the same reason, there is also no incremental publication of the results.
 The third, prescriptive part according Pöppelbuß (2011) fails because no material is
found in literature on maturity models in the domains of data governance nor
elsewhere (!). Moreover, the assessment criteria of the subsequent maturity levels give,
just like the maturity levels themselves, direction to advices on growth in organizational
maturity.
2.3 Data Governance Maturity Model
This section describes the implementation of the development of the maturity model for data
governance based on the developed design method from the previous section.

2.3.1 Step 1 Maturity Dimensions
As the first step, all domains are determined from the definition of data governance from
paragraph 2.1:
- corporate governance, risk management en compliance.
- people, processes en technology
- data assets
- organization
- data management: data management, data principles management, data quality, meta
data management, master data, data access management, data lifecycle management
Secondly all maturity models are collected that are found in literature that has been used for
drawing up the definition of data governance. All dimensions of those maturity models are
collected and classified under the same headings. During that collection a new dimension was
discovered which was not to classify under any of the other dimensions; business alignment .
And all data management-related dimensions were classified under the main heading of data
management.
Table 2 Maturity Models and Dimensions for DGMM shows the literature on dimensions
of data governance maturity models and the dimensions used therein. The definitions of the
dimensions are derived from the same literature and are described in Appendix I. Definition
dimensions Data Governance.
Table 2 Maturity Modells and Dimensions for DGMM
V = Referencce for dimension in article
2.3.2 Stap 2 Maturity Levels
Als tweede stap is de indeling van de volwassenheidsniveaus (maturity levels) voor het DGMM
bepaald. Daartoe zijn op dezelfde manier als in de vorige paragraaf 2.3.1 alle maturity levels
verzameld uit alle maturity models die zijn gevonden in de literatuur over de domeinen van
data governance en gerubriceerd. De levels uit de literatuur zijn weergegeven in tabel 3
Literatuur maturity levels en dimensies.
Smits Baten
burg
Car-
cary
Otto Greg
ory
Pöppe
lbuß
Racz Pee Cur-
ley
Luft-
man
2014 2014 2013 2013 2011 2011 2009 2009 2008 2003
Governance V V V V V
Risk management V V V V
Compliance V V
Processes V V V V V
People V V V V V V V V
Technology v V V V V
Data assets V V V
Business Alignment V V
Organisation V V
Data management V V

The second step is the classification of maturity levels (maturity levels) for the DGMM. To this
end, in the same way as in the previous section 2.3.1 all maturity levels are collected from all
maturity models that have been found in the literature on the domains of data governance, and
classified. The levels from the literature are shown in Table 3 Literature maturity levels and
dimensions.
Table 3 Literature maturity levels and dimensions
Luftman(2003) Without
process
Beginning
process
Established
process
Improved
process
Optimal
process
Curley(2008) Unmanaged Basic Intermediate Advanced Optimising
Gregory(2011a) Aware Reactive Proactive Managed Optimal
Otto (2013) Nothing has
been done
Same
approaches
Full
deployment
Structured
review
Systematic
measurement
Rifae(2009) Ad hoc Repeatable Defined Managed Optimized
Pee(2009) Initial Aware Defined Managed Optimized
Carcary(2013) Initial Basic Intermediate Advanced Optimising
Fath (2013) - Presence Interaction Transaction Integration
Batenburg(2014) Forming Developing Normalized Established Optimized
Chosen
formulation
No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
In order to achieve a usable format of maturity levels for the DGMM the following is
determined on the basis of the literature on maturity models in this study. First, there is a
distinction between situations without (1) and with (2-5) the presence of a maturity process.
Then a distinction is made between a few (beginning) or far (advanced) advanced maturity
level. This level of advancement determines also the average (established) maturity level. And
the ultimate extent (optimized) of organization maturity is appointed to the last level. The
maturity level classification thus created is used in the DGMM.
The dimension-selection and level-division results in the base DGMM as shown in Table 4.
Tabel 4 Basis Data Governance Maturity Model (DGMM)
No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Governance
Risk management
Compliance
Processes
People
Technology
Data assets
Business Alignment
Organization
Data mangement

2.3.3 Step 3 Differentiating Dimensions: Qualifications
In step 3, all qualifications are literally and completely taken over from all maturity models that
have been reported in the literature about maturity models in the domains of data governance
(see Table 2 Maturity Models and Dimensions for DGMM). This list is the first roughly shaped
DGMM. All qualifications together form a long list of 81 qualifications. These qualifications are
classified according to the new classification of dimensions as specified in paragraph 2.3.1.
2.3.4 Step 4 Assessment Criteria
In step four, all with the qualifications associated assessment criteria are literally and entirely
taken from the literature on maturity models in the areas of data governance (see Table 2
Maturity Models and Dimensions for DGMM) to the first roughly shaped DGMM. Together with
the 81 qualifications from paragraph 2.3.3 the list of assessment criteria establishes a very large
model. In pracctive, such a large model is unuseable as a measuring instrument due to its size
and duplicate entries. The model is due to the size shown in a separate
annex SamenstellingDataGovernanceMaturityModel_Nov2015.xls in Excel format.
2.3.5 Step 5 Data Governance Maturity Model
In step 5 are, because of the frequent occurrence of qualifications with the same scope or
meaning, similar qualifications classified unambiguously. The first list of 81 qualifications is
therefore reduced to a new list of 29 qualifications. Also the qualifications accompanying list of
assessment criteria are, according design step 5, brought back into line with the new 29
qualifications. Because of the reduction process reproducibility this is described in the separate
annex SamenstellingDataGovernanceMaturityModel_Nov2015.xls . The end result of step 5 is
the assessment model shown in Annex III Data Governance Maturity Model (DGMM).
2.3.6 Conceptueel model DGMM en operationalisatie
The results of the literature review are the following two things:
1. The model described in Table 5 Data Governance Maturity Model provides the
conceptual model for this study.
2. Reference is made to the interview form in Annex III Data Governance Maturity Model
(DGMM) for the operationalization of the DGMM. This describes the maturity model for
data governance enclosing the qualifications for each dimension and operationalized
assessment criteria.

Table 5 Data Governance Maturity Model
Dimensions
of Data
Governance
Qualifications Without
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Governance,
Risk
management
& Compliance
Structure
Authority
Controlling
People Capability
Policy
Culture
Processen Processes
Service and Product
Portfolio
Planning & Monitoring
Technology Technology
Application Landscape
Data Storage &
Distribution Architecture
Business Object Model &
Corp. Data Dictionairy
Data assets Value
Innovation
Assessments
Business
Alignment
Contribution to business
Relationship
Knowledge sharing
Organisatie Functions, roles, tasks and
responsibilities
DG Goals, Objectives &
Strategy
DG Tactics
Data
management
Data management
Data principles
management
Data quality management
Meta data management
Master data management
Data access management
Data lifecycle
management

3. Method Empirical Research
This chapter describes the empirical research method used to answer the sub-questions. The
first paragraph describes the research strategy. The second paragraph describes the data
collection method of which the review follows in the third paragraph. And The fourth paragraph
describes the data analysis method.
3.1 Research strategy
3.1.1 Required data
To answer both sub-questions of the empirical research the research looks for specific
information in practice.
Sub-question I will search for practical situations and experiences that lend them selves for an
assessment based on the dimensions, levels and criteria from the DGMM. This is done in a way
that the DGMM is tested to see if it satisfies as a model to assess organizational maturity or
data governance .
To answer sub-question II additions to the DGMM are looked for, for example dimensions,
Qualifications, maturity levels or criteria based on specific advice from the experts which are
supported by practical examples.
3.1.2 Approach and Philosophy
The research is approached from theory to practice and therefore it has a deductive approach;
DGMM it is tested as a conceptual model into practice. The way of thinking is in the direction of
realism. From perceptions of several people we are trying to establishement an objective
picture of reality.
3.1.3 Research method
To answer sub-questions and to achieve the objectives of this study, the methodology of a
single, holistic case study of a real-life situation is chosen in an existing organization based on
the following arguments.
The definition of a case study is "a method of doing research that uses empirical
research of a contemporary phenomenon within the current context, which is using evidence of
various kinds," Robson (2002: 178). First of all, this definition is consistent with realism as
philosophy for this research; we look for the objective truth behind a phenomenon. This also
applies for the approach of this study, which is deductive; the DGMM is being tested in
practice. And by examining a case in practice, the subject is automatically a contemporary
phenomenon. In this method the context of an organization is also relevant to exploratory
research as thesis opportunities for growth in understanding. This conceptualization is used to
supplement the lack of scientific knowledge about data governance. In addition, the method
does not limit the technique of data collection for evidence, although interviews with experts
are obvious to gather knowledge.
According to Yin (2003), a case study makes it possible to choose a holistic approach, all
which is necessary because data governance occupies entire organisaties. Herewith this subject
is examined in its context, so that real-life characteristics are preserved. And the amount of

time available for this study does not make it possible to examine more than one organization-
what has opted for a single case.
The disadvantage of using a case study is that it is an intensive method. Compared to other
methods it costs much more to examine different cases. This limits the generalizability of the
results.
Except a multi-case study among experts at several organisations, alternative methods are less
suitable for this study. An experiment does not show the holistic nature and context of the
organization. A survey limits the exploratory nature of the research by the closed nature of the
responses and its quantitative nature. 'Action research' is not an option because the time for
the investigation is limited. Typically ethnography is an inductive method, which is not suitable
for deductive research. Archival research would still be possible depending on the organization
to investigate, although the research deals with such new concepts that few archives on this
topic are available.
For the time being with the time available is chosen for a single method of qualitative research.
This is done to gain knowledge about this new subject as much as possible. In future research
confirming the DGMM can happen in quantitative research, with a survey method in the form
of questionnaires on the Internet. By inviting many respondents to apply the DGMM to their
own situation arises knowledge about the usefulness of the criteria in the DGMM.
To properly carry out the case studies severall respondents will be needed to get an objective
view of the case and its context. Also required is that the respondents are grounded in (some
of) the dimensions of data governance in order to contribute meaningful knowledge to the
investigation.
3.1.4 Time horizon
Because organizational growth has a much longer time horizon than the duration of this study a
cross-sectional study is adopted.
3.1.5 Access
The empirical study is conducted according to the case study method in a practical situation at
a suitable organization. Several experts should be active in this organization. In the Netherlands
there are many of these organisations. Therefore, some organization will be approached with a
request to participate in the study. With positive response from several organisations, one
organization will be selected to conduct the study.
The selection criterion is an estimate of the degree of maturity in data governance in order to
test the DGMM as much as possible and discover if that's feasible. It is desirable to have access
to the necessary data as much as possible, but this will also depend on the respondents.

A possible indicator whether organisations are working with this subject are vacancies in the
area of master data management. Another is participation in interest groups in data
management or in user groups of application software that support data governance and data
governance. Because it is very difficult to find an organization that scores the highest of all
maturity levels, an organization with a lower maturity score is also acceptable as long as it does
not score level 1. It is important that the dimensions and qualifications are recognized or
denied as a part of data governance. The maturity levels are already derived from literature and
previously confirmed in other studies.
To achieve that organisaties are interested to participate in the study, first the sponsor within
the organisation is informed about the importance of the stud. Subsequently, the investigation
of the workload will be shown. Not including introductions and reporting, the visit taes half a
day for an interview with an expert. After completion of the investigation the organisation will
receive a report with the findings of the research.
3.1.6 Ethical issues
Since research purposes do not sanctify the means, during the acquisition of generalized
knowledge a great effort will be made during investigations to prevent damage to persons.
To achieve this the following research ethics will be observed during the investigation. Already
with the study design and while gaining acces we attempt to do useful research.
When collecting data, the privacy rights of the respondents will be respected by
requiring informed consent of the sponsor and the respondents. If deception might
unfortunately occur this is feed back. Further, the well-being of the respondents will be
observed during the execution of the investigation. An attempt is made to minimize the
negative impact of the external researcher.
When processing and the collected data damage to the research organization and
respondents is precented by working with anonymized data. The collected data will not be
disclosed to third parties and not saved.
The analysis and reporting report generalized knowledge as much as possible. Over
more, the personal data protection law is respected. Data are rendered anonymous so that the
outcomes are not traceable to the research organization or to the respondents. The collected
data will not be used against the interest of respondents or organization.
3.1.7 Samples
The choice of using a sample depends on the research questions and objectives. Given that the
exploratory investigation is focused on knowledge creation is no point to look at the entire
population, but to work with samples. It is not necessary to consult the population of an
organization since all knowledge is spread over only a few workers with different roles.
Therefore it is important to make a selection among the workers on the basis of their
knowledge of one or more of the dimensions of data governance within the organization.

3.2 Data Collection Method
3.2.1 Secondary data sources
The literature study showed that is no scientific knowledge exists about data governance
maturity models. Also, there are no recommendations for organizational growth in data
governance known, not in the body of knowledge and not in practice as consultants keep this
knowledge to themselves for commercial reasons. Therefore the use of secondary data sources
is much less suitable for this research.
Because of the time available for the research the study is carried with a single organization. To
get a good impression of this organization information is collected from various websites. This
information serves to verify the suitability as research organization. To this end online job
postings give insight in how organisation is active in one of the dimensions of data governance.
From the interests of the investigation to explore all aspects of the DGMM it is desirable that
the organization where the research is conducted meet to the following requirements. These
requirements are used in the selection of the research organization on the basis of secondary
sources.
 Organization maturity in data governance on multiple dimensions of the DGMM, or
even more dimensions to the extent relevant, to test the DGMM as much as possible.
 Availability of at least three experts with knowledge or data governance. An expert is
someone who has built up expertise in a domain through research, experience and / or
profession. The number of experts must be such that conclusions can be drawn about
the validity of the DGMM. It assumes at least one expert per dimension of data
governance. When two experts, or even three, bring examples of a dimension the
validity of the research increases because the perceptions of two or three experts
constitutes a more objective picture.
3.2.2 Primary Data Source
Research Technique: semi-structured interview
In the study design we chose a case study in the context of a research organization. Due to the
limited time of the study we chose in the research strategy the cross-sectional study as time
horizon. Because of this we also waive participatory observation as a technique for data
collection. Instead, we opt for the technique or individual interviews of experts in the field of
data governance.
As the type of interview a semi-structured interview is selected with the as DGMM subject. This
on the basis of the following arguments.
First of all, the objective of the research is of exploratory nature for which a qualitative
technique is the most suitable [Blumberg et al (2008)]. As a result, the possibility arises for the
development of theory. Qualitative research is needed to learn to understand the reasons for
decisions, attitudes and opinions of the respondents on the subject.

Second, the DGMM is itself a standardized questionnaire. To discuss all aspects of the
DGMM possible DGMM is used as assessment tool and functions as a topic of conversation.
This creates the opportunity to not only test the DGMM assessment tool as well as to discuss
the dimensions, qualifications, maturity levels and criteria as separate themes. The latter aims
for respondents to give the opportunity to share their insights and knowledge about the issues
or complement the meaning of the elements in the DGMM. This will enrich existing knowledge.
This approach requires the technique of a semi-structured interview.
A third argument is the controlled collection of the data. The DGMM contains many
different aspects and themes. To investigate all these issues closely monitoring the progress of
the investigation process is required. More over, it may happen that further explanation is
required. Then personal contact with the respondents works much better than a questionnaire.
Also, it is possible to request by at unanswered questions.
The disadvantage of this research technique Is that the results are qualitative in nature and are
not confirmed by quantitative research. This limits the generalizability of the results. The
DGMM as standardized online questionnaire on the Internet can be used for research of
quantitative nature to quantitatively validate the criteria of the DGMM in further research and
in application in other organisaties
Another major drawback is that there is no participatory observation done so that the
answers of the respondents are not verified in the everyday work. In subsequent studies the
effect of the introduction of one or more corrective measures could be examined to eliminate
this disadvantage.
Interview
Based on information from secondary data sources a research organization is selected where
the investigation could happen.
To not to burden the research organization and respondents too much but still cater for the
advisability of test DGMM sufficiently thorough we chose to reserve a hour for an interview per
respondent. It is expected that distortion by decreasing willingness to participate may happen.
For the sake of preserving context of the organization the interviews will be held at the location
of the organization. To carry out the investigation a quiet environment on location in the
organization is requested to conduct the interviews.
To Increase the credibility of the research we chose to interview each respondent separately.
Themain reason is that maximum attention to the contribution of the respondent is achieved in
a one-on-one conversation. The respondent will feel freer to discuss the issues. The
conversational form of one-on-one interview is also important to register as many non-verbal
cues of the respondent.
In Addition, the following measures are taken to protect the internal validity. When selecting
participants for the semi-structured interviews attention will be given to what extent
participants:

- Have been active in the organization for a longer time so that their history counts in the
judgment
- Experience no positive or negative effect or research experience in their functioning
- Are confronted with the same DGMM in order not to have differences in readings by
abnormalities of the measuring instrument
- Intend (not) to leave the organization in the short term
- Undergo interviews reasonably simultaneously to filter out effects of organizational
developments in research
Prior to the interview to each respondent is explicitly asked to consent to the use of the data.
This by signing an information form by respondent and researcher. Only after obtaining the
consent the interview continues.
To leave no confusion in conceptualization we chose not to send the respondents detailed
information in advance. Thus, the very first responses to the measured themes is observed
which may be useful in adjusting the design and in repetition of the studies.
To introduce the topic data governance and this research an information sheet is drawn for
respondents and other interested parties as shown in Annex IV Interview Protocol. At the
beginning of the interview, the purpose and usefulness of the research is explained on the basis
of this information sheet.
In the interview the DGMM functions as a starting point. Data is collected for each DGMM
element (level, dimension, or qualifying criterion). This by asking question and answer per
DGMM element tha make it possible to draw conclusions about the importance of and the
relationship between DGMM elements (part question I). Also a question per DGMM element is
asked to identify additions to the DGMM to grow in data governance (Part II question).
The next two questions are operationalized per DGMM element:
A. This is Important and why?
This question is asked with the aim to find out ractical experiences substantiating the
DGMM element. This is necessary in order to confirm the importance of the DGMM
element, and the importance of the DGMM as a whole, to reject it or modify the
DGMM-off element.
Further, an attempt is made to find out backgrounds or argument of the
Importance of the DGMM element. This is necessary in order to gain insight in support
of the element of the DGMM and the DGMM as a whole.
B. How to improve this element and why?
This question is asked to identify insights for recommendations for organizational
growth in data governance.
Discovered insights have to be incorporated into theoretical models which are to
be enshrined in the literature, which serve as recommendations for further research.

The collection of data is done with the operationalized questions about each element as shown
in the interview form as part of Annex IV interview protocol.
Per qualification is asked for critical incidents as specific experiences which are useful for the
investigation. Further more, it is also asked to Illustrate certain statements with documents for
reasons of triangulation. Afterwards the collected data is fed back to the respondent for
verification and addition, with or without additional documents.
The interviews will be held according a standard method described in the following interview
protocol.
1. As a first step, the consent form is signed and discussed.
2. As second step follows processing of the interview and the DGMM and the under lying
themes are introduced on the basis of the information sheet in annex IV interview protocol.
3. As third the questions from the interview form as shown in the attachment interview
protocol are imposed on the respondent. While answering the questions the DGMM and
the under lying themes are discussed. The resu ting answers are recorded on the interview
form. And the discussions and debates of the themes are noted.
4. As fourth the continuation of the investigation is explained, the respondent is thanked for
his/her contribution and agreed is how to feed back the collected data.
The survey data will be anonymized after the interview by registering context information of
the organization separately from interview data and not disclosing the key between them.
3.3 Reliability and Validity Data Collected
The reliability of the collected data will be judged on the criteria or Yin (2008): reliability,
internal validity and external validity or generalizability. In addition also for credibility and
logical reasoning volgens Saunders M et al (2013).
Reliability
Qqualitative research is done and conclusions are drawn on the basis of knowledge, insights
and opinions of various experts to achieve better reliability. Opportunity is given to free
expression of ideas and insights. At the same time the investigation is limited to the research of
three experts in one organization. The disadvantage is that the scope of the investigation is
limited. The depth of the investigation is served by the qualitative approach to empirical
research. There is opportunity for reflection and applying nuances and new insights to the
DGMM. In Addition, each section examines and discusses what can provide a richer picture. The
disadvantage Is that the draft is limited to three experts.The reliability of the data collected has
been improved by the respondents reducing bias by interviewing more than one respondent.
Further research of this study could increase the reliability of the results further, i.e.
when another researcher repeats this research and thus reduces observer bias. In this study,
observational bias is reduced displaying quotations literally and taking a neutral stance during
the interviews.

internal validity
A high degree of internal validity will be achieved with the deepening of topics and apply
various angles.
A potential threat to the internal validity is limited expertise of the respondent,
respondents bias, because it is ased too much from a single expert to control all dimensions or
DGMM. The same applies to the everpresent limitations of the discretion of the investigator,
the researchers bias.
generalizationability
The external validity or generalizability is increased by approaching governance experts in
various dimensions or data.
To reduce the contrast in generalizability with survey samples, the case study of a
specific case is carried out with several respondents in an organization. As a result, an
organisation situation is examined from the point of view of different persons.
The outcomes of the study are also generalizable because of an association with existing
theory. The new theory of DGMM is based on existing theory in the literature. This theory will
be tested in practice. For this the connection between existing and new theory is proofed and
evidence arises for generalizability of the results of the investigation.
Because of the time available the research is conducted at only one organization, which
limits the generalizability of the results. This gives rise to carry out the same investigation for
several organisations. In subsequent studies, the generalization can also be improved by online
quantitative research with DGMM as questionnaire for which many respondents can be inited
to participate in the study.
Another disadvantage of this form of investigation is that it is a snapshot in time putting
the generalizability under pressure. This can be remedied by repeating the examination later
on. The research is carried out in the context of a Dutch company that, although it operates
internationally. Possible outcomes are not entirely valid for a non-private, non-Dutch or non-
Western culture. This could be improved by further research within a public authority in
another country or another culture.
Credibility
The credibility of the research increases by one-on-one interviews with respondents where
they can express them selves freely. This is much less possible in other types of interviews like
in a phone call, a group or by filling out an online questionnaire.
The disadvantage of the choice for an individual interview opposed to a group of experts
is that it is not possible to react to eachother and discuss topics in order to arrive at
correspondance on concept formation. Future research could take the form of a group
interview.
Another disadvantage is the difference in experience of the respondents. Not every
respondent shall be an expert in every field. Thereby consistency in DGMM be underestimated.
Further research can raise this disadvantage by consulting more experts.

As the researcher himself is an expert of the dimensions of data governance the risk of
misunderstanding about the activities of the respondents, the researchers bias, is reduced. The
study therefore can better focus on how data governance is designed in the research
organization.
By understanding and knowledge the trust of respondents will easily grow which
facilitates some deeper penetration into the soil of the processes. However, there is a risk that
both researcher and respondent plunge too quickly and too deep into the art as they are both
working in the field. The risk Is that aspects of the discipline, which are seen as obviously for
professionals, are overlooked.
Logical reasoning
For the logical reasoning and assumptions to withstand the test of criticism as much as possible,
the following measures have been taken:
- The study population was made up of of various experts.
- For the analysis of the data a deductive method of analysis is used according Yin (2008).
- The (provisional) conclusions will be fed back to the experts interviewed to obtain their
feedback.
3.4 Data Analysis
Because the survey and the collected data are qualitative in nature, qualitative data analysis is
chosen. The approach of the empirical research is deductive what makes that the primary
approach to qualitative analysis is also deductive. But during the investigation, it is well possible
that there will be found new themes during the discussion of the DGMM. These new themes
are explored with the inductive approach.
Each DGMM element is test according the following steps [Yin (2008)]:
1. Design propositions on theoretical basis (already happened in literature review resulting
in the DGMM model)
2. Collecting data in case study using semi-structured interview
3. Compare collected data and make theoretical assumption and draw conclusions
4. If necessary, adjust theoretical assumption.
With this method, it is to be expected that the dimensions, qualifications, levels and criteria of
the DGMM are adapted or supplemented as a result of the outcome of the interviews. Or
otherwise, the validity of parts of the DGMM is confirmed.
Because the DGMM gives the interview categorized structure we did not chose for the
technique or transcribing and coding interview recordings. Instead analyzing the given answers
for each DGMM element is chosen. Based on the responses from the three interviews
conclusions are drawn per DGMM element about its validity or that it deserves a feature
adaptation. The same is done for consistency within the DGMM.
If at least one expert confirms a DGMM element and supports it by a practical example
the DGMM element is considered confirmed. A difference between a denial or confirmation of
a DGMM element consists out of that one expert can and the other expert can not make
practical example.

When an expert needs explanation of a DGMM element caused by defective
explanation or lack of expertise, the confirmation of this element DGMM is not seen as fully
recognized in the evaluation and confirmation is not counted.
Based on the notes on the new themes DGMM is adjusted (sub-question I) or additions are
listed on the DGMM (sub-question II) as recommendations for further research.

4. Research Results
In this chapter the results of the empirical research are given. The research in which the DGMM
has been tested in practice to proof its design is adequate as an assessment tool for
determining organizational maturity or data governance.
4.1 Global description research
The research data consist of responses to the operationalized questions in the DGMM in
Appendix III. These responses consist of arguments and reasons, but mainly from relevant
examples which happen repeatedly within the research organization. Further advice and
remarkable statements from the experts were noted during the interviews as quotes. Also the
score of the assessment is included.
In addition, information is collected about the research organisation which forms the context of
the research. This context is important to better interpret the resulting research data.
4.2 Background research organization
Data Governance within the research organization
How Data Governance is setup in the research organization can be characterized as follows.
- At the time of the investigation the examined organization is engaged engaged in
organizing data management, data quality and data governance across the organization
for two years. Back then an expert in the dimensions of DG was engaged and at the
moment there are plans to expand the team with several members more, like data
stewards. For this there is a first inventory of the status of the organization in the field
of data management and data governance. This is done by capturing and documenting
the existing Business Objects and the Corporate Data Dictonairy
Next there is a data management and data governance strategy formulated. And the
organisation has started to realize 26 individual plans to improve data and its
organization.
- The strategy give attention mainly to the following topics:
o Connectivity and synchronization
 Integration issues between departments
 Shaping ERP system by compiling global data
 Growing into adulthood for 'maintained' and 'monitored' interfaces
o Community
o Ownership for data quality delegated to departments
o Drafting Data definitions for serving Business Intelligence (BI) reporting
- Capturing and sharing of data between different companies is organized at group level.
There'd rather interfaces between systems then made to work in one system. At the
moment there is mostly manually information exchanged with structured text files
(CSV). There are no automatic interfaces yet. Documentation of systems and data is
captured at the source
- A central reporting point for Data Governance, Data Management and Data Quality
questions and issues via the intranet is used.

- A project was started to select a technical application for performing for Data
Governance, Data Management and Data Quality.
- A number of qualifications are addressed strategically. Another number of qualifications
are addressed case by case or in parts. For most qualifications is a planning but the final
maturity stage will be reached much later, and only for a number of relevant
qualifications.
- There is a lot of product information which is not available in digital form. The
organisation is working to digitize non-digital documentation-which data can be
accessed digitally.
- "Standardised product data forms data assets of strategic value for the company and
these are independent of operating systems or correct tooling." This citation reflects the
design of the ICT division, all which is split into an Information Technology and
Information Management department.
The presence of a data management maturity model, the presence of experts with knowledge
of the dimensions of data governance and effective implementation of data governance enable
the organization to test the DGMM. It should be noted, however, that the organization is not at
the highest level of maturity, which has somewhat limited the validity of the claims.
Respondents
Three respondents are interviewed, all experts and working in multiple dimensions of data
governance.
The first respondent holds the position of Master Data Manager. He has experience with data
management in several organisations in the Netherlands and abroad. He holds a masters
degree in information management and is familiar with maturity models. He himself introduced
a maturity model for master data management in the past and continued to use it. During the
interview was noticed that very recent developments in literature included in the DGMM were
not included in the maturity model used in the research organization, being 2 years old. For
example, the fifth maturity level 'Optimizing' was new. Same happened forr the dimension
Governance, Risk Management and Compliance (GRC). Both level and dimension were referred
to as relevant.
The second respondent holds the position of Application & Information Architect. He holds a
bachelor's degree in engineering and has many practical experience in ICT built up in its own
organization and with other organizations in the Netherlands as a consultant.
The third respondent is the manager of the Business Intelligence department (BI). She holds a
master's degree in management and years of experience within the organization. Being
responsible for the Business Intelligence department (BI) she is interested in good data
governance.
Respondents are expert in the dimensions of data governance displayed in Table 6 Coverage
dimensions of data governance by experts.

Tabel 6 Coverage dimensions of data governance by experts
Dimensions of Data Governance Expert 1 Expert 2 Experts 3
Governance, Risk management & Compliance v v
People v v v
Processen v v v
Technology v v
Data assets v v
Business Alignment v v v
Organisatie v v
Data management v v
The three respondents Interviewed are all experts in multiple dimensions of data governance
and together they have knowledge of all dimensions. The education level and work experience
of the respondents in the relevant dimension were more than sufficient which is relevant for
determining multiple examples of all DGMM elements. There are striking similarities observed
between the individual reported examples and description of the organization. That made it
possible to examine the whole DGMM and assess its relevance. With separate interviews the
respondents bias was reduced. This provides a high reliability of the results.
Context research organisation
During the interviews and visits at the research organization the following notable quotations
related data governance are observed. The quotes are usually related to the practical context of
the examined organization.
- Quote 1: "For data governance responsibilities for data are distributed over the
organization."
- Quote 2: "Data Governance is about aligning the management of data."
- Quote 3: "Often is (functional) management oriented towards applications, while it is
better to align it with data. For example the responsibility for client data that's stored in
multiple applications."
- Quote 4: "Previously DG was based on the business information model, nowadays it is
based on the business model which better reflects the perception of the business."
- Quote 5: "Business Intelligence (BI) as an interested party is a driver for Data
Governance."
- Quote 6: "Meta data management follows the same trend as master data management
and is important for Business Intelligence (BI) as the basis of Key Performance Indicator
(KPI) reports."
- Quote 7: "Information and knowledge from projects is not transferred consciously and
disappears because projects are inherently finite."
- 8 quote: "The effectiveness of innovation is measured in the area of the data assets in
savings or production costs"
- Quote 9: "Data assets are not assessed because no problems have occurred so far."
- Quote 10 "Governance, Risk Management and Compliance returns especially into
compliance"

These ratios are an indication That data governance is setup within the research organization.
Organization Assessment with DGMM
During the interviews, all DGMM elements are discussed. But due to time constraints not all
DGMM elements separately collected relevant examples. Nevertheless, for at least one each
qualification a sample was noted, sometimes several.
There was a substantiated estimate for each classification of the maturity level of the
organization. And there's a planning for the realization of the next stages of maturity, unless it
wasn’t considered relevant for every qualification.
Applying the DGMM at the time of the interviews actually created an organizational assessment
of organizational maturity of data governance. This is done as a practice test for application of
the model as DGMM organizational assessment tool. The results of this assessment can be
found in Annex V DGMM Outcome Assessment.
The result of the assessment is that the research organization scored for data governance
maturity level for all dimensions predominantly level two, except dimensioning GRC-which
scored mostly level 3. Respondents acknowledged the score and confirmed the outcome.
Therefore DGMM can be used in practice as a measuring tool to assess organizational maturity
data governance.
4.3 Results sub-question I
The first exploratory subquestion investigates how the DGMM can be tested as a measuring
instrument for assesing organizational maturity in data governance. To that end is asked
whether a DGMM element (a) is relevant and why and (b) how to improve and why. Summary
of the research shows the following results.
Confirmation of the DGMM:
- All levels, dimensions, qualifications and criteria are acknowledged by one or more
experts from their own maturity model for master data management and confirmed on
the basis of practical examples.
- The fifth organizational maturity level 'Optimized' from the DGMM is seen as a new
addition to the own maturity model of the research organization, which had four
maturity levels. And this fifth level was seen as valid and relevant.
- The Governance, Risk Management and Compliance dimension was seen as highly
relevant in practice, mainly the Compliance aspect:
o The organization is ISO certified and meeting international quality standards
o The organization and its subsidiaries comply with financial reporting
requirements in different countries
o Holding subsidiaries comply to rules for production that are imposed by the
governmens of the various countries
o Subsidiaries keep customer-specific requirements, like the safety demands of
several at customers

o A central system for all subsidiaries is planned to enable checking the quality and
compliance of parts
o Risk Management and Governance of data and master data management are not
completed within the research organization according the respondents.
- The research organization's scores in the assessment an overall maturity level 2, but for
some dimensions level 4. For most of the qualifications a project is planned for the short
and long term to increase its maturity levels. Also some qualifications score only level 1.
And for some is wondered whether that subject is ever being developped as
qualifications.
- By effectively assesing the research organization on the basis of the DGMM a rating of
the level of maturity of data governance results which is confirmed by the respondents.
Amendments to the DGMM:
The research organisation recommends to remove the following elements from the DGMM or
change them.
Qualification:
o DG product portfolio can be removed. This because it is not advisable to leave
prioritization of work to (internal) customers of DG. And because it is not certain
whether insight and transparency into the DG dimension is present. Possibly this
qualification is recognized by other organisaties.
Assessment criteria:
o Job rotation, this does not occur in the research organization and the experts have
no experience with it at other organisaties
o Sponsorship by the business is replaced by ownership of the business. This is more
contemporary language of the same ie involvement.
o In dimensioning GRC the qualification can be completed with EDP auditing. EDP
auditing occurs in the context of the audit of internal control.
Unknown or undisclosed research organization
All dimensions and qualifications of the DGMM model were confirmed with examples from
practive by one or more of the respondents. Some qualifications were considered no used, but
none of the elements were unanimously considered irrelevant. Table 7 Confirmed
Qualifications shows-which respondents confirmed the qualification.

Tabel 7 Confirmed Qualifications
Dimensions of Data
Governance
Qualifications Expert 1 Expert 2 Expert 3
Governance, Risk
management &
Compliance
Structure v v* v
Authority v v* v
Controlling -* v* v
People Capability v v v
Policy v v v
Culture - - v
Processen Processes v - v
Service and Product Portfolio - - v
Planning & Monitoring v v v
Technology Technology v v v*
Application Landscape v v v
Data Storage & Distribution Architecture v v v
Business Object Model & Corp. Data Dictionairy v v v
Data assets Value -* v v
Innovation -* v v
Assessments v* -* v
Business Alignment Ownership by business v v v
Relationship v v v
Knowledge sharing v v v
Organisatie Functions, roles, tasks and responsibilities v v v
DG Goals, Objectives & Strategy v v* -
DG Tactics v v v
Data management Data management v v v
Data principles management v v -*
Data quality management v v v
Meta data management v v v
Master data management v v v
Data access management v v v*
Data lifecycle management - v -*
v = conformation, - = denial, grijs = no expertise (zie table 6)
For this DGMM element further explanation was necessary for the expert. By inadequate explanation or
by lack of expertise this DGMM element can not fully be taken into account in the assessment
4.4 Results subquestion II
The second exploratory subquestion is to detect additions to the DGMM for further research.
For the following elements DGMM was recommended by an expert to further Investigate
whether these can be added to the DGMM model.
New dimensions:
- Connectivity and synchronization: concerns integration issues between business units or
with other organisaties
- Community: concerns communication with stakeholders on data governance

New Qualifications:
- Drafting data definitions, for example for Business Intelligence (BI) reporting
- Data Harmonization (deduplication)
- Data Profiling
- Degree of (de) centralization
- Key Performance Indicator (KPI) according Service Level Agreement (SLA), for example
Corrections
These dimensions and qualifications are not derived from the literature but arise in the practice
of data management. These elements can be confirmed in further research in an inductive way.

5. Discussion
This chapter describes the conclusions and findings of the research.
5.1 Interpretation of data
The research data of chapter 2 and 4 are interpreted in this paragraph.
5.1.1 Conclusions literature review
The DGMM is derived from the definition of data governance. And experts have confirmed all
dimensions of the DGMM. This also confirms the definition of data governance. There is a
nuance: in this research data concerns explicitly digital data.
5.1.2 Conclusions research findings context
The following conclusions can be drawn from the findings of the context of the research
organization.
Within the research organization, there are indications that data governance is setup, All which
is supported by several at quotes. There are also experts who collectively possess knowledge of
all aspects of data governance. And the DGMM is used as a measuring instrument for the
assessment of organization maturity of data governance. Nevertheless, the assessment
indicates that the research organization is not yet fully mature in data governance. It may well
be concluded that the research organization is suitable for testing the DGMM in practice.
5.1.3 Conclusions subquesion I
To answer subquestions I, the following conclusions are drawn out of the research results as
summarized in paragraph 4.3:
- The relevance and validity of the DGMM elements separately and DGMM as a whole in
the empirical study can be seen in Table 7. All dimensions were confirmed on the basis
of examples from practice. All but one of the DGMM qualifications were relevant to the
organization, namely product portfolio processes. But this qualification is recognized by
other organisaties.
- Some of theDGMM criteria are lyrically edited on the advice of the respondents
- GRC has been confirmed as the basis for data governance, especially compliance is seen
as a relevant part of DG. This is because there are many requirements for companies
and their services and products, both by governments and by customers. This
conclusion is important because before Racz (2010a, 2010b) GRC was not previously
involved in data governance, even not within the research organization, but it turns out
to be important.
- In particular, the dimensions data value and people are not at all known dimensions of
data governance within the research organization, but are considered relevant. With
more information about the dimensions during the interview, The importance is
acknowledged. At the same time the need for explanation shows that a respondent is
not an expert in the relevant dimension, which is factored into the survey results.

- Recent developments in the literature are listed in DGMM and considered relevant and
valid as the fifth level 'Optimizing process’.
- The assessment showed a clear score, which was confirmed by the respondents and in
which the situation of the organization was recognized. From this review of the DGMM
as a measurement instrument can be concluded that it is possible to indicate
organization maturity or data governance with DGMM in the studied organization.
From the results of empirical research can be concluded that experts in the dimensions of data
governance confirm that the DGMM is relevant and valid, after adjustment, as a measure to
assess the organizational maturity of data governance.
All dimensions were seen as relevant. All but one were considered relevant qualifications,
although some are still unknown in the context of the research organization. All qualifications
except one have been identified as growth opportunities in organizational maturity of data
governance although not always unanimous for each level of maturity.
5.1.4 Conclusions subquestion II
To answer subquestion II, there are two new dimensions to grow in organisation maturity of
data governance organization recommended by one expert, that qualify for further research.
These recommendations relate to two new dimensions:
- Connectivity and synchronization: concerns integration issues between business units or
with other organisaties
- Community: groups of communication with stakeholders on data governance
5.2 Research evaluation
To evaluate the research follows a Strenths-Weaknesses analysis of which conclusions and
recommendations are made for further research.
5.2.1 Beoordeling Methode Empirisch Onderzoek
The case study with as aim exploratory research of qualitative nature as chosen method for this
empirical research is rated as follows.
+ To explore the scientific unknown concepts the right method is chosen because the case
study has delivered much knowledge about data governance and the DGMM.
+ The choice of qualitative research above quantitative resulted in the discovery of new
knowledge about data governance.
+ Using the chosen research method has yielded a high internal validity and reliability of
the results.
+ By working with the case study of method Yin (2008.2013), theory of literature is tested
in practice what results in confirmation of the theoretical model and so reliable
research.
- The generalizability of the research method can be improved, although measures have
been taken to this improve this like theoretical generalization according Yin (2008),
through support with literature and interviews with several at experts.
- Because of the limited time the research was carried out in one organization-which
limits the generalizability of the results, although there are several experts consulted

who confirmed eachother's ideas, practical situations and the theory of literature. Also
Yin (2008) recommends multi-case study over a single case study.
The conclusion Is that the chosen research method of exploratory research of qualitative nature
in the form of a case study was just to enable knowledge discovery on the unknown subject of
data governance. The method results in research with high internal validity, although the
generalizability could be better.
Recommendation is to replicate the same research in another organization so that a multi-case
study is carried out.
5.2.2 Data collection method
The data collection method is evaluated as follows.
+ As primary data collection method the semi-structured interview is chosen. This
research technique proved highly suitable for exploratory research because the DGMM
with its operationalized questions forms a semi-structured questionnaire.
+ the choice of the individual interviewing of experts has delivered so much information
that DGMM could be tested and be a basis for the definition of data governance based
on facts and real-life situations and critical incidents. This provides a high degree of
internal validity.
+ Through the use of the DGMM as questionnaire all components of the DGMM, and the
internal cohesion between them, are examined as propositions in all three interviews.
All parts of the entire model are discussed three times. Because of this there was plenty
of time for testing the many propositions in the model and theories about data
governance. Respondents had the opportunity to contribute their own ideas and discuss
new ideas.
+ The research organization complied with the requirements so it was a suitable research
organization and more over internationally active in more than 20 countries on six
continents.
+ Repeating the interviews according predetermined steps had a positive effect because
nothing was missed during the interviews, and the conversation line was clear line for
those involved.
+ Making survey data anonymous by using a signature form has helped the respondents
to speak freely.
± The choice to confront respondents only with knowledge about data governance and
the information sheet during the interview has resulted that some explanation about
data governance was needed during interviews. The aim was to not to create confusion
about understanding. In retrospect this purpose worked. Examples from practice were
brought forward spontaneously Instead that there had been preparation.
- Because of the limited time participatory research is not opted for so that results of the
interviews are not experienced by the researcher.

Aanbeveling is participatief onderzoek uit te voeren zodat de onderzoeker zelf de
praktijksituaties ervaart. Verder zou in verder onderzoek de respondenten tevoren ingelicht
kunnen worden over begrippen van data governance om zich voor te kunnen bereiden op
interviews. Mogelijk dat er dan meer relevante praktijkvoorbeelden worden aangedragen.
Verder wordt aanbevolen om het onderzoek te repliceren door een andere onderzoeker zodat
de onderzoeksresultaten meer generaliseerbaar en betrouwbaarder worden
The conclusion Is that the used data collection method of semi-structured interview with
experts within the same appropriate research organization has produced a high degree of
internal validity, reliability and credibility of the data.
It is recommended to carry out participatory research so that the researcher experiences the
practical situations himself. In further research respondents could be informed in advance
about terms of data governance to prepare for interviews. Maybe that more relevant examples
are contributed.
It is also recommended to replicate the study by another researcher so that the
research results become more generalizable and more reliable.
5.2.3 Assessment Data collection method
The data analysis method is assessed as follows.
+ Using the method of analysis for case studies of Yin (2003, 2008, 2013) a high internal
validity is achieved because for each theoretically grounded DGMM element is
determined in practice whether it is confirmed or rejected. This promotes the logical
reasoning, especially because the analytical method is provable and reproducible.
+ With the two operationalized questions for relevance and improvement of each DGMM
element is reliably determined the relevance and validity of the whole DGMM.
- This method does not ask specifically for the missing. Completeness of the model is
determined separately from the method chosen for subquestion II.
From this it is concluded that use of this data analysis method yields a model in which each
DGMM element and the DGMM as a whole is supported substantially. This shows that the
DGMM is relevant and credible, and it is structured in logical reasoning.
5.2.4 Assesment Data processing and analysis
The data processing and analysis are assessed as follows.
+ The DGMM was founded in empirical research for all individual components and
consistency of its parts.
+ The propositions of the DGMM which are derived from the literature are substantiated
on the basis of data from empirical research. These are collected and analyzed by the
method of Yin(2008).
+ There is ample room for improvement of the DGMM or recommendations for further
research.

It is concluded that the data processing of the empirical study is designed such that the
theoretical propositions of the elements of the DGMM and their relationships are confirmed.
This confirmation is done on the basis of data obtained from empirical research, which are
collected and analyzed accordion the method of Yin (2008).
5.2.5 Assessment Research data
The research data are assessed as follows.
+ The research organization has such a knowledge of data governance maturity that it was
possible to test the DGMM. Many dimensions were actually completed or plans for
realization are present.
+ There are only a few improvements and recommendations for the DGMM. That means
that the DGMM sufficiently covers the content of data governance to determine its
organizational maturity levels. More over, for each qualifying practice cases are found
within the organization that underpin the model. The few practice situations that are
contradictory with the DGMM are recorded as improvements or as a recommendation
for further research.
- The study should be repeated in order to determine if trends over time can be
measured by the DGMM, all which increases the reliability of the DGMM.
- The study was conducted by a single researcher with the risk of observer bias. In this
study, observational bias is reduced by noting quotes and a neutral stance during the
interviews.
- There was no group discussion with experts in order to discover more nuances or to
enrich the conceptualization.
The conclusion is that the DGMM is tested substantive enough in practice to determine that the
measuring instrument DGMM has a high internal validity.
To win on internal validity, we recommend a group discussion with experts. It is also
recommended to further investigate the recommendations for two new dimensions.
5.2.6 Reflection
Reflecting on the research the following is stated.
+ The research yielded new scientific knowledge about data governance in the form of a
definition and a maturity model for data governance.
+ DGMM is a measuring instrument for measuring data governance maturity og
organisations in practice.
- The research organization is limited in organizational maturity of data governance and
the experts limited in their knowledge and expertise thereof. New and unfamiliar
concepts or data governance that do not belong to their own expertise will only be
recognized after explanation as theory and in practice. Because the research initially
relied upon confirmation by experts with knowledge of the relevant dimension, the
influencing effect of respondents on the research results is minimized.
- Incomprehension of a respondent for a DGGM element as dimension, qualification or
criteria supplies sometimes 'not relevant'.

- Because the theme DG is new certain themes that are still relevant can be
irreproachable.
- Further research is needed to confirm the results of this study and to generalize it.
The conclusion is that new knowledge about data governance is found in the form of a
definition and a maturity model as practicable measuring instrument. This new knowledge
requires clarification and explanation to recognize it in practice.
Recommendation is to replicate the same research at other organisations to confirm the
findings and generalize it.

6. Conclusions & Recommendations
This section answers the research question of the study. The earlier drawn conclusions are
summarized In the first paragraph, followed in the second paragraph by a discussion on those
conclusions. In the last paragraph follows the recommendations for further research.
6.1 Conclusions
Based on the objective of the study the problem was formulated for which the main question
is:
The answer is that the Data Governance Maturity Model offers a good impetus for assesing
organizational maturity of data governance. This response is based on answers to both
contextual and subquestions for literature and answers to questions for empirical research. The
answers are as follows.
Data Governance
On the basis of literature the definition of data governance is composed. And experts have
confirmed all dimensions or DGMM. This also confirmes the definition of data governance.
Maturity Model
Based on analysis of the literature, requirements and models are Huner et al (2009), Becker
(2009) and Pöppelbuß (2011) are adopted as a method for the preparation of a maturity model
for data governance. This method is based on literature about maturity models in the areas of
data governance.
Based on literature a maturity model is built with relevant dimensions, levels, qualifications and
criteria on how to grow in data governance. A translation was made on criteria from related
domains to the dimensions of data governance.
Assessment of organizational maturity data governance
From results of empirical research can be derived confirm that experts on the dimensions of
data governance confirm that the DGMM is relevant and valid as a measure to assess the
organizational maturity of data governance. All dimensions were seen as relevant. All but one
were considered relevant qualifications, although some were still unknown in the context of
the research organization. All qualifications except one have been identified as growth
opportunities in organizational maturity of data governance although not always unanimous for
each level of maturity.
Recommendations for organizational growth in data governance
There are two new dimensions to grow into maturity in data governance organization
recommended by experts that qualify for further research.

Research evaluation
From evaluation of the investigation is concluded that with the research method used the
DGMM is substantially and entirely tested in practice. This in the form of semi-structured
interviews with experts within the same, suitable research organization. Herewith the DGMM is
relevant, credible and it is structured in logical reasoning. On this basis, it is concluded that the
DGMM has a high internal validity, reliability and credibility. But because the study is conducted
in one organization the generalizability not so high, despite theoretical generalization from the
literature. Further the chosen research method returned new knowledge about data
governance. Namely a definition and a maturity model as useable measure for practice.
6.2 Discussion
From the DGMM only one qualification is not recognized as relevant. And there are only two
new dimensions mentioned. Thus the question arises whether the DGMM is complete. This
would qualify for further research to confirm or extend the DGMM.
6.3 Recommendations for Future Research
Based on the results of the above study, the following recommendations for further research
are formulated. The experts have given recommendations to grow in organizational maturity
which qualify for Further investigation.
To increase the internal validity of the DGMM a group discussion with experts is
recommended. Also repeating the survey at other organisaties contributes to this. Further is
recommended to do participatory research in which the researcher experiences the practical
situations. And in further research tespondents could be informed about terms of data
governance in advance to prepare for interviews. It is expected that that will be trigger more
relevant examples.
To increase the generalizability and reliability of the DGMM it is recommended to
repeat the same examination intime, by another investigator and at other organisations. This
forms a multi-case study to confirm the results of this study and generalize it.

7. Reflection
7.1 Meaning research outcomes
First It is special to the ascertain that his research yielded a validated instrument that proves
useful in practice. More about, there is new knowledge created in a young field that's still
developing to add to literature.
At the same time during the investigation occurred that scientific knowledge is still lacking in
the practice of the Investigated dimensions. Within this part of the field there is a lot of work on
the basis of skills, rules of thumb and methods formed in practice. That means that there is still
much to research scientifically.
During the research process the limitation of thesis research was revealed. There was limited
time available for empirical study what limited the scope of the investigation. Especially the
generalizability would be enhanced if the investigation is extended to other organisaties.
7.2 Processreflection
The following learning experiences are gained in the research project.
- When looking for a research organization a high demand for knowledge about data
governance occured. Thereby was noticed that there is little knowledge about DG. At
the same time during the study was notices that scientific research takes time.
However, there is a clear need for further research.
- The implementation of the empirical study took a few months because it coincided with
the summer holiday period so that the availability of the experts and the researcher was
limited. Therefore it is advisable to plan empirical research outside the holidays.
- My own intention was to set up a wider research. Inexperience in research was the
cause of this. Only on direction of the supervisor it became clear how to carry out the
final research in correspondance with scheduled time and planned size.
- Studying publications during the study helped a lot in the design of the study. Through
several choices in research methods, each research gets results in a different way.
Recognising this opened up the possibility of applying different research methods and
techniques in this research. This improved the quality of the research. From this we can
learn that adding structure with known methods improves the quality of research.

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Appendix I Definition Dimensions Data Governance
Definitie Bron
Governance Governance involves how the board and management
are structured and what are their roles and
responsibilities. Governance also means that appropriate
procedures are established and communicated. In
addition, these policies and procedures are to ensure
that these are followed.
Batenburg
(p45,2014)
Risk management Risk management aims to mitigate and minimize the
impact of risks. There is always a tradeoff between risk
and chance. It is important how these risks are identified,
analyzed, evaluated and treated, in short: managed.
Batenburg
(p45,2014)
Compliance Compliance indicates that an organization operates in
accordance with existing laws, regulations, protocols,
standards and specifications. This is guaranteed during
the design of controls.
Batenburg
(p45,2014)
Governance,
Risk management
&
Compliance (GRC)
GRC is an integrated, holistic approach of enterprise-
wide governance, risk and compliance. It ensures that an
organization is acting ethically and in accordance with its
ris flavor, internal policies and external regulations
through alignment in strategy, processes, technology and
people, thereby enhancing efficiency and efficacy.
In itself is GRC not new. As individual cases, governance,
risk management and compliance were always
fundamental concerns of the business and its leaders.
What is new is the growing perception of GRC as an
integrated set of concepts that, when applied holistically,
can add significant value and deliver competitive
advantage.
Racz(p8.2010
a), Gregory
(2010a)
Batenburg
(2014)
Process,
People &
Technology
Process, People and Technology are the areas of interest
(Key Process Area’s), both within data quality
management and data governance.
See also definition GRC above.
Pee(2009),
Batenburg
(p46,2014)
Pöppelbuß
(2011),
Otto(2013)
Asset Value (Used) Data are a valuable business asset that needs
careful protection and the value should actively be
managed and / or "governed" (= controlled).
Gregory(201
1),
Otto(2013)

Business Alignment Alignment between user organization and IT. Luftman
(2003),
Curley(2008)
DG Organisation DG organization has two dimensions:
- DG goals include formal (business and IT) and
functional goals.
- DG structure is determined by locus of control
(functional and hierarchical) form and roles &
commissions
Otto(2011a)

Annex II Quality and design principles maturity models into BPM
In scientific literature, the following quality and design principles of maturity models in business
process management (BPM) found.
Huner et al (2009) suggest the following quality requirements on a maturity model:
1. A maturity model is not only an assessment tool but offers usefull guidelines to achieve
a higher maturity level.
2. Vagueness in the investigated dimension must be resolved by use of assessable parts.
3. As growth model, the model should also be applicable in starting situations, level 0.
4. Different maturity dimensions have to be distinguished, (CMMI distinguished only the
dimension).
5. An informed assessment should include clear, measurable guidelines and tools.
6. A self-assessment should be supported.
Huner et al (2009) describe the following components of a maturity model:
Becker (2009) provides the following design principles :
1. That iterative improvements happen step by step.
2. Improvements are evaluated.
3. Improving happens according different scientific methods.
4. Improvements are innovative and relevant.
5. The domain, application conditions and benefits to achieve are determined in advance.
6. Presentation of results are targeted towards users.
7. All steps in the model, stakeholders, methods and results should be described in detail.

Becker (2009) gives the following procedure for the preparation of a maturity model
Explanation
- R1 - The model must be relevant compared to its predecessors
- R8 - The design method is based on a comparison of previous methods
- R2 - Iterative method, the design with the highest level of abstraction determines the
specific architecture, multi-dimensional architectures are customary
- R4 - Methods for compiling assessment criteria are determined for each dimension, eg
based on success factors.
- R3 – In evaluation completeness, consistency and adequacy are to be testing.

- R4 - documentation can be created for publication, but also usefull software tools to
carry out discriminatory tests in experiments
- R7 - The model implementation must be described, like the self-assessment tools
- R3 – An evaluation demonstrates the utility of the MM in e.g. a case study or through
web-based forms that validation of the model can be done by many users
- Optionally, the models usefulness can be rejected by too many negative results
- The model must be updated to changing circumstances.
The preparation of a maturity model synthesis follows steps:
1. Problem definition
2. Comparison of existing Maturity Models
3. Determination of development strategy
4. Iterative development MM
5. Publication and evaluations: software tool
Becker (2009) describes his resulting maturity model in the next figure.
Figure 28

Pöppelbuß (2011) gives the following design principles.
Figure 29
- 1.2 The path to maturity should be described with therein the change that needs to happen.
High level steps are for external stakeholders, low-level steps for internal stakeholders.
- 1.3 Constructs and activities should be defined by their relationship to the domain
- 1.4 The relationship between construct and activities must also be described, possibly in
groups.
- 2.1 criteria are interrelated, precise and distinguishing between levels
- 2.2 Adviced is how to assess and how to apply criteria
- 3.1 Prescriptively best practices for improvement of action are given

- 3.2 Prescriptively a weighting model is given to decide which are alternative priorities in
maturation.
- 3.3 A procedure model should be in place which stipulates the sequence of improvement
steps. And existence of previous applications are also requested.
Pöppelbuß (2011) additionally suggests the following quality standards :
1. Maturity models combine state descriptions, which are models for distincted maturity
levels and activities, and which are methods for assessment, indicating need for action
and the selection or improvement measures.
2. Maturity models consist or maturity levels (level) indicators, dimensions, qualities (=
qualifications (Otto, 2011)) ordered in dimensions, descriptions, process areas, activities
per process area and a description of each activity.
3. Domain reference models and assessment models are different from each other.
4. A maturity model should be valid, reliable, cost-efficient, underpinned empirically, a
have software tool support, implement standardization, possess flexibility / adaptation,
possibility of benchmarking, make certification possible, reveal potential improvements
and prove relation between model adoption and performance.
5. Design principles for form and function include design principles for
- basic model (domain model)
- descriptive model (assessment tool)
- prescriptive model (improvement guidelines)
Otto (2013) mentions that:
- A maturity model consists of a domain model and assessment tool.

Annex III Data Governance Maturity Model (DGMM)
Elaborated DGMM frameworkk
The DGMM consist out of the frameworkas shown hereunder in table 1 Data Governance
Maturity Model. For the research of the DGMM are for each cell the following questions asked :
A. Is this of interest and why ?
B. How can this better and why ?
The operationalized questions are given hereunder in a table per dimension.
table 1 Data Governance Maturity Model
Dimensies
van Data
Governance
Kwalificaties Without
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Governance,
Risk
management
& Compliance
Structure
Authority
Controlling
People Capability
Policy
Culture
Processen Processes
Service and Product
Portfolio
Planning & Monitoring
Technology Technology
Application Landscape
Data Storage &
Distribution Architecture
Business Object Model &
Corp. Data Dictionairy
Data assets Value
Innovation
Assessments
Business
Alignment
Contribution to business
Relationship
Knowledge sharing
Organisatie Functions, roles, tasks and
responsibilities
DG Goals, Objectives &
Strategy
DG Tactics
Data
management
Data management
Data principles
management
Data quality management
Meta data management
Master data management
Data access management
Data lifecycle management

Tabel 2 Dimension Governance, Risk Management & Compliance (GRC)
Qualification No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Structure No Govern-
mance,
Risk
manage-
ment &
Compliance
process in
place
Little
attempt to
standardize
Governance,
Risk
manage-
ment &
Compliance
processes
Similar
Governance,
Risk manage-
ment &
Compliance
processes are
standardized
across parts
of the
organisation
Similar
Governance,
Risk
managemen
t &
Compliance
processes
are
reviewed
across the
organisation
Similar
Governance,
Risk
management &
Compliance
processes are
standardized
across the
organisation
Authority Ad-hoc
authority,
no DG
officer, no
steering
committee
DG officer is
appointed,
without any
power,
steeringcom
mittee
meets
informally
when
needed
DG officer
reports
directly to the
board and has
the power,
formal
steering
committee
meets
regularly
DG officer
has
authority to
enact
changes &
users do no
oppose,
steering
committee
has proven
to be
effective
DG officer
works closely
with the board,
users share the
power in a
balanced way,
steering
committee
includes
external
partners
Controlling No GRC
control
proces or
results
analysis, ad
hoc
reporting
GRC controls
are
requirement
s, results
reactively
reviewed,
internal
reporting on
paper
GRC Controls
cause review
policies,result
s analysis
process in
place,
external
automated
reporting
Users feel
safe with
GRC control
process,
organisation
wide results
analysis,exte
rnal audits
are
conducted
users trust GRC
control process,
results analysis
integrated wirh
R&D,
stakeholders
are alerted with
automated
system

Tabel 3 Dimension People
Qualification No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Capability Unmanage
ged
Technology
Supplier &
Utility
provider
Technical
expert
Strategic
Business
partner
Strategic core
competency
Policy No
retention
program,
poor
recruiting,
no internal
opportuni-
ties
DG hiring
focused on
technical
skills,
Training &
job rotation
occasionally
at unit level
Technology
and business
focus;
retention
program,
Training &
job rotation
for unit
manage-
ment
Formal
program for
hiring and
retaining,
Advanced
training & job
rotation
across
enterprise
Effective
program for
hiring and
retaining,
Advanced
training & job
rotation with
partners
Culture Organizatio
n and its
people are
not aware
of the need
to formally
manage
DG,
Changes
discoura-
ged
Managemen
t is aware of
the need for
formal DG,
Changes
encouraged
at unit level
Managemen
t is aware of
its role in
encouraging
DG, basic DG
strategy is in
place,
Individual
DG roles are
defined,
Incentive
systems are
in place,
Change
programs at
unit level
Common
strategy and
standardized
approaches
towards DG,
incorporated
into the
organizational
strategy,
Organizationa
l standards,
Change
programs at
corporate
level
Culture of DG is
institutionalized
, Change
programs with
partners

Tabel 4 Dimension Processes
Qualification No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Processes No formal
processes
to capture,
share and
reuse
organizatio
nal DG
issues
DG
processes
critical for
performing
routine task
are
documented
and mapped
Processes
for DG are
formalized,
metrics are
used to
measure the
increase in
productivity
due to DG
Quantitative
measurement
of DG
processes
(i.e., use of
metrics)
DG processes
constantly
reviewed and
improved, easily
adapt new
business
requirements
and integral
part of
organization
Service and
Product
Portfolio
No DG
service &
product
portfolio in
place
DG Portfolio
of critical
services &
products in
place
DG Portfolio
covers basic
services&pro
ducts
DG
Service&prod
uct portfolio
is being
monitored
DG service &
product
portfolio is
being shared &
improved
Planning &
Monitoring
No DG
planning &
monitoring
DG planning
&
monitoring
attempts
Integral DG
planning &
monitoring
DG planning
& monitoring
proces is
managed
DG planning &
monitoring
process is
integrated &
shared with
stakeholders

Tabel 5 Dimension Technologie
Qualification No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Technology No specific
DG
technology
or
infrastruc-
ture in
place
Pilot DG
projects are
initiated (not
necessarily
by manage-
ment)
Basic DG
Infrastructur
e in place,
some
enterprisele
vel DG
projects are
put in place
Enterprise-
wide DG
systems are in
place and
usage at a
reasonable
level.
Existing DG
infrastructure is
continually
improved
Application
Landscape
No formal
planning or
documenta
tion
Attempts for
planning &
documentati
on at unit
level
Formalized
planning &
documentati
on for basic
elements at
organization
al level
Controlled &
audited
process of
planning &
documenta-
tion
Automated
plannign &
documentation
process,
reveiwed with
partners
Data Storage
&
Distribution
Architecture
No formal
planning or
documenta
tion
Attempts for
planning &
documentati
on at unit
level
Formalized
planning &
documentati
on for basic
elements at
organization
al level
Controlled &
audited
process of
planning &
documenta-
tion
Automated
plannign &
documentation
process,
reveiwed with
partners
Business
Object Model
& Corporate
Data
Dictionairy
No formal
planning or
documenta
tion
Attempts for
planning &
documenta-
tion at unit
level
Formalized
planning &
documentati
on for basic
elements at
organization
al level
Controlled &
audited
process of
planning &
documenta-
tion
Automated
plannign &
documentation
process,
reveiwed with
partners

Tabel 6 Dimension Data Assets
Qualification No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Value No or
ad hoc
practices
Total Cost of
Ownership
Simple ROI
& DG
Business
Case
Discipline
Options/Portfoli
o management
Optimized
Value
(Investment
Return)
Innovation None Rare;
effectivenes
s not
measured
Rare;
starting to
measure
effectivenes
s
Rare; frequently
measure
effectiveness
Practices
and
measures
well
established
Assessments No assess-
ments,
investment
s measured
rarely, if
ever
Only when
there is a
problem,
technical
cost
measured,
metrics
rarely
reviewed
Assessments
becoming
routine
occurrence,
reviews, act
on technical
ROI metrics
Routinely assess
and act on
findings, also
measure
customer value
Routinely
assessments
, act on, and
measure
results,
balanced
scorecard,
includes
partners

Tabel 7 Dimension Business Alignment
Qualification No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Contribution
to business
No
sponsoring,
considered
costs of
doing
business
Often have a
senior spon-
sor or DG
champion,
becoming an
asset, enab-
les business
processes
"DG and
business
Contribution to
business
No
sponsoring,
considered
costs of
doing
business
Relationship DG takes all
the risks &
IT receives
no rewards,
Conflict and
mistrust,
use only as
needed,
communica
tion
business to
IT only and
formal,
relationship
is not
managed
DG takes
most risks
with little
reward,
relationship
managed on
an ad hoc
basis &
agreement
with units,
communicati
on one-way
& somewhat
informal
DG and
Business
start sharing
risks&re-
wards &
relationship
processes
exist but not
always follo-
wed, DG be-
coming a va-
lued service
provider &
enterprisewi
de agree-
ments &
knowledge
transfer faci-
litated, com-
munication
two-way &
formal
Risks, rewards
always shared &
relationship
processes exist
and are complied
with, long-term
partnership &
enterprise wide
& trust &
confidence
achieved,
communication
two-way &
somewhat
informal
Managers
incented to
take risks &
relationship
processes
are
continuously
improved,
partner &
trusted
vendor or
DG services
&
relationship
with other
partners,
communicati
on two-way
& informal &
flexible
Knowledge
sharing
Casual con-
versation
and mee-
tings, no
benchmar-
king
Some struc-
tured sha-
ring emer-
ging, news-
letters, re-
ports, group
e-mail,
sometimes
benchmark
informally
Structured
around key
processes,
training,
department
al meetings,
May bench-
mark formal-
ly, seldom
act
Formal methods,
sharing at all
levels, Routinely
benchmark,
usually act
Formal
sharing with
partners,
learning
monitored,
Routinely
benchmark,
act on, and
measure
results

Tabel 8 Dimension Organization
Qualification No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Contribution
to business
No sponsoring,
considered
costs of doing
business
Often have a
senior sponsor
or DG
champion,
becoming an
asset, enables
business
processes
DG and
business
No sponsoring,
considered
costs of doing
business
Often have a
senior sponsor
or DG
champion,
becoming an
asset, enables
business
processes
Relationship DG takes all
the risks & IT
receives no
rewards,
Conflict and
mistrust, use
only as
needed,
communication
business to IT
only and
formal,
relationship is
not managed
DG takes most
risks with little
reward,
relationship
managed on an
ad hoc basis &
agreement
with units,
communication
one-way &
somewhat
informal
DG and
Business start
sharing
risks&rewards
& relationship
processes exist
but not always
followed, DG
becoming a
valued service
provider &
enterprisewide
agreements &
knowledge
transfer
facilitated,
communication
two-way &
formal
Risks, rewards
always shared
& relationship
processes exist
and are
complied with,
long-term
partnership &
enterprise
wide & trust &
confidence
achieved,
communication
two-way &
somewhat
informal
Managers
incented to
take risks &
relationship
processes are
continuously
improved,
partner &
trusted vendor
or DG services
& relationship
with other
partners,
communication
two-way &
informal &
flexible
Knowledge
sharing
Casual
conversation
and meetings,
no
benchmarking
Some
structured
sharing
emerging,
newsletters,
reports, group
e-mail,
sometimes
benchmark
informally
Structured
around key
processes,
training,
departmental
meetings, May
benchmark
formally,
seldom act
Formal
methods,
sharing at all
levels,
Routinely
benchmark,
usually act
Formal sharing
with partners,
learning
monitored,
Routinely
benchmark, act
on, and
measure
results

Tabel 9 Dimension Data Management
Qualification No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Data
management
No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Data
principles
management
No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Data
quality
management
No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Meta
data
management
No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Master
data
management
No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Data
access
management
No
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Data
lifecycle
management
No
process
Beginning
process
Established
process
Managed
process
Optimizing
process

For the assessment questionnaire, see the online software tool:
https://datagovernancematurity.wordpress.com/
Direct Access:
https://docs.google.com/forms/d/1WgcpcvqpkGISSTrRencjAvpg0CRV3KrHj_B00nRMIJM

Bijlage IV Interviewprotocol
The following components are part of the interview protocol
- Consent form
- Information Sheet Data Governance Maturity Model
- Interview Form: See Annex III Data Governance Maturity Model

Consent Form
Research: Data Governance Maturity Model
Researcher: Jan Merkus, graduation in Business Process Management, Open Universiteit the
Netherlands
Paraph
yes no
1. I hereby confirm that I have read and inderstood the
information sheet for this research, and that I have had the
opportunity to ask questions.
2. I understand that my participation is voluntary and that I can
terminate it at any time without giving any reason.
3. I am aware that, although every effort will be made to
ensure the eep the confidentiality of the information I give,
this can only be guaranteed within the limits of the law.
Tick box
4. I agree to participate in the study
5. I give permission to use anonymous quotes in publications
Name participant: Date: Signature:
Jan Merkus, researcher: Date: Signature:

Information Sheet
Introduction
Data plays an increasingly Important role in everyday life. Data is also of increasing interest
given the hype around big data. Thus increases the importance of data quality. And data quality
requires maintenance in the form of data management. Along with ensuring the quality
shouldering the value of data as an asset is to be safeguarded, which both takes place under
data governance (DG).
Relevance
Research of the scientific literature on DG shows the interest of this topic. Not only in recent
years but even now scientific research on this topic is carried out.
The attention for DG can be explained by its growing importance. DG is of value for the survival
of an organization. Organisations use DG o control the organization. In larger companies data
quality issues happen through mergers and distributed systems which requires DG. It was noted
that there data quality was handled inadequately with required DG also. And large and small
organisations need data with quality for their processes and due to administrative and legal
obligations. The unilateral focus on data aspects must change to organizational maturity in
dealing with data.
The many interests of stakeholders from the business are translated by DG in conducting data
quality management in technical IT domain. And data quality is important for compliance,
customer policy, reporting and business processes. DG also makes part of the framework of
corporate governance, risk management and compliance (GRC). The goal of DG and GRC is
adding value and reducing risk. Around data prejudice risks arise like loss and theft, privacy
violation, violation of law, low data quality, but also liability.

Definition Data Governance
Literature study has come to the following definition of data governance:
Data Governance is
from Corporate Governance, Risk Management and Compliance
determining the strategy for processes, people and technology
to maximize the value of data assets
by arranging organization, responsibilities and accountibilities
for the domains data management, data principles, data quality, metadata, master data, data
access and data lifecycle.
Definition dimensions
Tabel 1 Definition dimensions Data Governance
Definitie
Governance Governance involves how the board and management are structured and
what are their roles and responsibilities. Governance also means that
appropriate procedures are established and communicated. In addition,
these policies and procedures are to ensure that these are followed.
Risk management Risk management aims to mitigate and minimize the impact of risks. There
is always a tradeoff between risk and chance. It is important how these
risks are identified, analyzed, evaluated and treated, in short: managed.
Compliance Compliance indicates that an organization operates in accordance with
existing laws, regulations, protocols, standards and specifications. This is
guaranteed during the design of controls.
Governance,
Risk management
&
Compliance (GRC)
GRC is an integrated, holistic approach of enterprise-wide governance, risk
and compliance. It ensures that an organization is acting ethically and in
accordance with its ris flavor, internal policies and external regulations
through alignment in strategy, processes, technology and people, thereby
enhancing efficiency and efficacy.
In itself is GRC not new. As individual cases, governance, risk management
and compliance were always fundamental concerns of the business and its
leaders. What is new is the growing perception of GRC as an integrated set
of concepts that, when applied holistically, can add significant value and
deliver competitive advantage.
Process,
People &
Technology
Process, People and Technology are the areas of interest (Key Process
Area’s), both within data quality management and data governance.
See also definition GRC above.

Tabel 2 Basis Data Governance Maturity Model (DGMM)
Without
process
Beginning
process
Established
process
Managed
process
Optimizing
process
Governance, Risk
management,
Compliance (GRC)
Processes
People
Technology
Asset Value
Business Alignment
DG Organization
Data Management
Asset Value (Used) Data are a valuable business asset that needs careful protection
and the value should actively be managed and / or "governed" (=
controlled).
Business Alignment Alignment between user organization and IT.
Organization DG organization has two dimensions:
- DG goals include formal (business and IT) and functional goals.
- DG structure is determined by locus of control (functional and
hierarchical) form and roles & commissions

Interview Form
To answer the research question it is translated into the following sub-questions.
Sub-questions:
I. Exploratory research to determine whether the organization maturity of data
governance can be assessed with the dimensions, levels and criteria from literature in
the form of the DGMM and if any changes or additions are needed.
II. Exploratory research to discover valuable recommendations for organisaties to grow in
data governance.
The translation of the sub-questions in operationalized question is as follows. Respondents are
asked in interviews to determine on the basis of relevant situations in practice for each DGMM
element or for cohesion within the DGMM whether:
A. Is it important and wy ?
B. How to improve it and why ?

Bijlage V DGMM Outcome Assessment
Table 1 Assessment Score
This table give which level is scored during the three interviews of this research.
Other research data
Other research data can be requested from the author : janmerkus@gmail.com
Dimension Qualifications No process Beginning
process
Established
process
Managed
process
Optimizing
process
Structure 1 2
Authority 1 2
Controlling / EDP auditing 1
People Capability 2 1
Policy 3
Culture 1 2 1
Processes Processes 1 2
Planning & Monitoring 1 1 1
Technology Technology 2 1
Application Landscape 1 1 1
Data Storage & Distribution
Architecture
2 1
Business Object Model & Corporate
Data Dictionairy
2 1
Data Assets Value 1 1 1
Innovation 1 1 1
Assessments 3
Business alignment Ownership by business 1 2
Relationship 3
Knowledge sharing 1 2
Functions, roles, tasks and
responsibilities
3
DG Goals, Objectives & Strategy 2 1
DG Tactics 3
Data management Data management 2 1
Data principles management 1 2
Data quality management 1 2
Meta data management 2 1
Master data management 1 2
Data access management 1 2
Data lifecycle management 2 1
Corporate Governance,
Risk management &
Compliance (GRC)
Data Governance (DG)
Organization

Data Governance Maturity Model Thesis

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