eThesis - Kauppakorkeakoulun sähköiset gradut

Objectives of the study The purpose of this study was to find suitable and effective method for designing data governance model. The objective is to develop data governance model by utilizing general frameworks and theories from the literature that could be applied to the empirical part of the study. Thus the goal is to build design method with frameworks and theories combined with empirical research and test this method in case context.

The main motivator for the study was an organizational issue in the case company as they were planning and implementing new data governance structure for different data objects, starting from customer data.

Methodology The study is pursued as a single case study within the case company and applied methodology is canonical action research. This research describes the first action research cycle focusing on customer data. In the first phase of the study literature review is conducted and important frameworks and theories have been identified. In the second part findings and frameworks from the existing literature are used to structure and guide workshops and interviews within the case company.

Workshops are used for information gathering and initial analysis regarding to case settings. After initial data collection, existing theories and contingency theory based frameworks are used to formulate themes for semi structured interviews. Findings from interviews are used to build model for customer data governance, with roles, decision areas and accountabilities mapped.

Findings The key finding of this research is that contingency approach combined with cyclical action research cycles and method offers a valuable set of tools for designing data governance model. Findings also support the view that there is no universal data governance model suitable for every company. Since data governance related contingencies have rarely been tested in case contexts, this research offers valuable information on this aspect.

Research also revealed that former literature does not take into account that companies manage their data dividing accountabilities and roles by different objects such as customer, material and vendor data. Former research assumes that one data governance model for the whole company is enough, when in truth; all data objects need different governance models