| dc.description.abstract | Background and purpose: As a consequence of the growing power of data, there is a need
for companies to maximise the value derived from it. However, to maximise the value derived
from data, it needs to be available, secure, relevant, and of high quality, which can be assured
by data governance. In addition, data governance has become crucial for companies to meet
legal requirements and to be competitive. The increasing need for data governance puts
pressure on organisations to control how they work with data and thus a need to improve. To
understand how an organisation works today and what can be improved, a maturity model can
be used. However, available data governance maturity models do not only miss out on aspects
within data governance but also on how to use the model. Thus, the purpose of this study is to
explore how a maturity model can support organisations in improving data governance. The
model is practically contributing as a tool for companies to assess their current level of maturity
and to identify potential improvements.
Methodology: A qualitative research strategy has been used throughout this study. After
investigating existing literature, workshops with data governance experts were conducted.
Based on the findings from literature and workshops, aspects important when creating the
model could be identified and the TMT Data Governance Maturity model was created. To test
the validity of the model and to determine what to take into consideration when using the
model, it was applied to a case company where semi-structured interviews with employees
were conducted. The findings from the interviews were analysed by comparing the answers to
the levels in the model, using a thematic approach. The levels of maturity were then determined
based on the average of all respondents' answers. By comparing the assigned levels with the
higher levels, actions for how to improve were identified and relevant improvement areas could
thereafter be defined.
Main Findings: Based on the theoretical framework and workshops 13 elements were
identified as crucial for data governance maturity models: Strategy & Approach, Leadership,
Structure, Progress Measure, Knowledge & Change Management, Rules, Data Quality, Data
Security & Privacy, Data Lifecycle Management, Metadata Management, Master Data
Management, Business Intelligence, and Adherence. The research also showed that an
important aspect of maturity models is interview questions reflecting the elements and some
sort of measurement, which resulted in five levels being defined: Unaware, Ad Hoc, Proactive,
Managed, and Optimised. When testing the model, one finding was that the model always
needs to be adapted to each specific organisation before use to be of value, since all companies
are unique. If adapting the model to be in line with the characteristics of the organisation, the
current maturity level could be determined and thereby also what is needed to reach the higher
levels by identification of the gap. However, the result from using the maturity model only
works as guidance for what could be improved since the reality usually is more complex than
assigning an organisation a level on a scale. | sv |
