University of Twente Student Theses
Developing a Focus Area Maturity Model for Data-driven Decision-making
Raad, Benjamin Carlos de (2024) Developing a Focus Area Maturity Model for Data-driven Decision-making.
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| Abstract: | As the global market becomes more competitive, data-driven organizations that excel at transforming data into meaningful actions, are gaining an increasing advantage. Companies strive to become data-driven to enhance efficiency, improve customer experiences, and make more informed and accurate business decisions. However, research suggests that organizations struggle with developing the capabilities necessary to become data driven. This challenge stems from a lack of maturity literature that offers a holistic approach to data-driven capability development. In this research we present a Focus Area Maturity Model (FAMM) for Data-Driven Decision-Making (DDDM) as an artifact designed to assist practitioners with assessing and developing data-driven capabilities. Focus area maturity models can be used to assess the maturity level of an organization in specific domains and serve as a launchpad for the development of an improvement strategy. We present a model consisting of 12 focus areas and 54 capabilities that embody DDDM. The model is designed using established FAMM development methods, which are grounded in the design science research methodology. Model elements are derived from literature and practice through triangulation, consisting of a semi-systematic literature review, expert focus groups and a case study. The design process of the model and its components are described in detail and its application is illustrated using a case study. We present the Data Driven Decision-making Focus Area Maturity Model (DDDMFAMM) and assessment instrument as tools for practitioners that provide actionable insights and a structured approach to enhance data-driven maturity iteratively. We propose that DDDM maturity development functions as a causal loop and provide intra- and interdependencies for capabilities across focus areas that illustrate how different aspects of DDDM are related. Model components are evaluated for their relevance and accuracy and our study shows that the DDDMFAMM is an effective tool that can help organizations incrementally improve their exploitation of data. We also propose a novel assessment method that improves assessment repeatability and institutionalization and offer various recommendations within the context of our case study and data-driven capability development in general. |
| Item Type: | Essay (Master) |
| Clients: | Thales NL, Hengelo, Netherlands |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 85 business administration, organizational science |
| Programme: | Business Administration MSc (60644) |
| Link to this item: | https://purl.utwente.nl/essays/100513 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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