Data-Driven Decision-Making in SMEs: Adoption, Challenges, and Performance Outcomes
Domracheva, Polina (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025082924259
https://urn.fi/URN:NBN:fi:amk-2025082924259
Tiivistelmä
This thesis aims to investigate the extent of data-driven decision-making (DDDM) in small and medium-sized enterprises (SMEs). Specifically, the goal of this thesis is to assess the level of DDDM integration in SMEs, discover its effect on SMEs performance and identify adoption barriers and enablers.
The scope of the study was limited to SMEs. Interviews were conducted with professionals working in tech SMEs, while the literature review remained cross-sectoral, due to the lack of existing research in the tech sector. The interview sample was limited and skewed toward Russia. The SME context was contrasted with that of large enterprises.
The theoretical framework defined core concepts such as DDDM, data analytics, and performance metrics. Findings from the theoretical framework imply that the advantages of DDDM are widely recognized. However, while DDDM is commonly utilized by large corporations, SMEs often struggle to adopt these practices.
A mixed-methods approach was applied to the empirical portion of the thesis. A systematic literature review provided an overview of DDDM usage in SMEs, along with challenges and outcomes associated with DDDM. Complementing the literature review, a series of semi-structured interviews was conducted with experts working in tech SMEs. However, due to the Russia-skewed sample and limited interview numbers, findings may not be broadly applicable.
Research findings indicate an early-to-intermediate level of DDDM integration in SMEs. Data usage remains leadership-focused, tooling remains basic, and data-driven culture is not widely present. Although DDDM-enabling practices are consistently associated with improved performance, interviewees were unable to directly link their companies’ growth to the adoption of DDDM practices.
Among the challenges associated with DDDM adoption, data quality issues, lack of resources, infrastructural constraints, and cultural resistance were identified as the most prominent for SMEs. The findings suggest that effective DDDM requires SMEs to focus on data validation, strategic KPI identification, and employee training. This not only allows SMEs to improve DDDM efficiency but also promotes more sustainable practices.
Overall, the research provides insight into the current state of DDDM maturity in SMEs and makes practical recommendations for improvement. While DDDM in SMEs is still lacking compared to large enterprises, with focused efforts, SMEs can become more data driven.
The scope of the study was limited to SMEs. Interviews were conducted with professionals working in tech SMEs, while the literature review remained cross-sectoral, due to the lack of existing research in the tech sector. The interview sample was limited and skewed toward Russia. The SME context was contrasted with that of large enterprises.
The theoretical framework defined core concepts such as DDDM, data analytics, and performance metrics. Findings from the theoretical framework imply that the advantages of DDDM are widely recognized. However, while DDDM is commonly utilized by large corporations, SMEs often struggle to adopt these practices.
A mixed-methods approach was applied to the empirical portion of the thesis. A systematic literature review provided an overview of DDDM usage in SMEs, along with challenges and outcomes associated with DDDM. Complementing the literature review, a series of semi-structured interviews was conducted with experts working in tech SMEs. However, due to the Russia-skewed sample and limited interview numbers, findings may not be broadly applicable.
Research findings indicate an early-to-intermediate level of DDDM integration in SMEs. Data usage remains leadership-focused, tooling remains basic, and data-driven culture is not widely present. Although DDDM-enabling practices are consistently associated with improved performance, interviewees were unable to directly link their companies’ growth to the adoption of DDDM practices.
Among the challenges associated with DDDM adoption, data quality issues, lack of resources, infrastructural constraints, and cultural resistance were identified as the most prominent for SMEs. The findings suggest that effective DDDM requires SMEs to focus on data validation, strategic KPI identification, and employee training. This not only allows SMEs to improve DDDM efficiency but also promotes more sustainable practices.
Overall, the research provides insight into the current state of DDDM maturity in SMEs and makes practical recommendations for improvement. While DDDM in SMEs is still lacking compared to large enterprises, with focused efforts, SMEs can become more data driven.