Data-driven strategic decision-making in SMEs

Abstract

The ability of data analytics (DA) to improve strategic decision-making (SDM) by analyzing large volumes of data has been well documented in larger enterprises. In Small- and Medium-sized Enterprises (SMEs), the importance of DA is increasingly acknowledged. However, most of the SME-specific research has focused on DA use in non-strategic contexts. This research focuses on the current usage and adoption of DA in the SDM of SMEs.

The primary objective of this research is to understand how decision-makers in SMEs use DA in SDM, and to identify the perceived barriers and opportunities for its usage. This study addresses the main research question: "How do decision-makers in Small- and Medium-sized Enterprises utilize data analytics in strategic decision-making, and what are the perceived opportunities and barriers associated with its application?" By addressing this question, this study contributes to the existing literature by providing an understanding of the current state of DA usage in SMEs and identifying SDM-SME-specific barriers and opportunities.

This research is of qualitative nature, conducted with data gathered through semi-structured interviews with 13 decision-makers from various SMEs. Through thematic analysis of the interviews in the context of the existing literature, the research concluded on the usage of DA in five cases within the SDM of SMEs:

SMEs use DA to support decisions regarding market positioning, by analyzing market trends and competitors. SDMs regarding market responsiveness are also supported by DA, by supporting SDM by conducting procurement and sales analysis and resource planning. Furthermore, DA supports SDMs regarding customer relations, value proposition improvements and the improvement of their organization. Some early adopter SMEs use predictive and prescriptive techniques in their SDMs. The usage of more sophisticated predictive and prescriptive techniques in SDM is not widespread yet under SMEs.

Despite these usages within the SDM of SMEs, several barriers were observed to hinder the implementation of DA in SDM. Technical barriers to adoption are lacking analysis quality and missing data. The costs and insufficient benefits of DA in SDM are both technological and organizational barriers. The organizational barriers further include the sentiment of the decision-maker, the knowledge in the SME, the SME size, internal resistance, and having no time or set priority for DA.

The research also highlights several opportunities for SMEs to further implement DA in their SDM. These opportunities include possible collaborations among SMEs or governmental institutions, and the cost of DA going down with technological advancements.

Given the potential benefits, it is recommended for decision-makers in SME to explore partnerships with other businesses or public institutions. This could initiate shared resources, reduced costs, and enhanced DA capabilities. Collaborative efforts can facilitate access to higher-quality data and advanced analytics tools that might be otherwise inaccessible.

Furthermore, as technological advancements make DA tools more affordable, SMEs should prioritize investments in scalable solutions that align with their growth objectives. A phased approach, starting with basic analytics and gradually integrating more advanced techniques, can help manage costs while building internal expertise.

To overcome organizational barriers, SMEs should invest in training and development to build internal DA expertise. Upskilling existing employees and hiring DA professionals can bridge knowledge gaps and foster a data-driven culture.

It is recommended that the SMEs try to utilize these opportunities. Considering the added value of DA to SDM, it is also recommended for governing bodies to investigate the possibilities for supporting the further adoption of DA in the SDM of SMEs.