Data-Driven Decision-Making in Finland's Public Social and Healthcare Sector
Mäenpää, Vilho (2025-05-16)
Data-Driven Decision-Making in Finland's Public Social and Healthcare Sector
(16.05.2025)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025060358555
https://urn.fi/URN:NBN:fi-fe2025060358555
Tiivistelmä
This thesis examines the implementation of data-driven decision-making (DDDM) in Finland’s public social and healthcare sector, focusing particularly on the integration of Business Intelligence (BI) and Knowledge Management (KM) to improve organizational efficiency and strategic outcomes. The sector is currently facing increasing organizational complexity, resource limitations, and rising demands for quality and accountability, which traditional decision-making practices struggle to address effectively. Fragmented information systems, siloed knowledge, and inconsistent data use have hindered efforts to optimize operations and improve service delivery.
To address these challenges, this study explores DDDM as a systematic approach that replaces intuition-based practices with evidence-based insights. In this framework, BI is seen as a critical enabler of timely and accessible data, while KM provides the organizational processes needed to capture, share, and use knowledge effectively. By combining these elements, the study outlines a coherent framework for improving operational efficiency, strategic alignment, and the overall quality of care in the public social and healthcare sector.
The research applies a qualitative method based on semi-structured interviews conducted with the information services team of one of the newly established Finnish wellbeing services counties. Using grounded theory and the Gioia method in the data analysis process, the study identifies key drivers and barriers to DDDM implementation, such as technological infrastructure, cross-departmental collaboration, and organizational culture. The results reveal that while strategic commitment to data-driven models is evident, successful implementation requires aligned practices, knowledge-sharing frameworks, and clear communication across departments and professional boundaries.
The study contributes to existing literature by providing current views on DDDM practices in the context of the Finnish social and healthcare reform of 2023, as the topic is still relatively unexplored. By combining BI and KM within a coherent DDDM framework, this thesis demonstrates how these tools can support better decision-making, transparency, and collaboration in public social and healthcare sector. The findings highlight that DDDM is not just a technological upgrade, but an organizational and cultural transformation that needs to be implemented at all levels of an organization to realize its full potential.
To address these challenges, this study explores DDDM as a systematic approach that replaces intuition-based practices with evidence-based insights. In this framework, BI is seen as a critical enabler of timely and accessible data, while KM provides the organizational processes needed to capture, share, and use knowledge effectively. By combining these elements, the study outlines a coherent framework for improving operational efficiency, strategic alignment, and the overall quality of care in the public social and healthcare sector.
The research applies a qualitative method based on semi-structured interviews conducted with the information services team of one of the newly established Finnish wellbeing services counties. Using grounded theory and the Gioia method in the data analysis process, the study identifies key drivers and barriers to DDDM implementation, such as technological infrastructure, cross-departmental collaboration, and organizational culture. The results reveal that while strategic commitment to data-driven models is evident, successful implementation requires aligned practices, knowledge-sharing frameworks, and clear communication across departments and professional boundaries.
The study contributes to existing literature by providing current views on DDDM practices in the context of the Finnish social and healthcare reform of 2023, as the topic is still relatively unexplored. By combining BI and KM within a coherent DDDM framework, this thesis demonstrates how these tools can support better decision-making, transparency, and collaboration in public social and healthcare sector. The findings highlight that DDDM is not just a technological upgrade, but an organizational and cultural transformation that needs to be implemented at all levels of an organization to realize its full potential.