Data Science encompasses various tools and techniques from statistics and computer science to solve specific business problems. Organizations are often overburdened with the choices of deciding among the vast number of available tools and techniques for enhancing business value, increasing revenues, reducing costs and improving overall efficiency. Despite the advances in existing body of literature there is a significant gap in providing a holistic multi-perspective framework that can systematically guide tool selection based on the nature of problem in a specific business context with a desired outcome. Therefore, the purpose of this paper is to address this gap and the pressing issues of selecting the right data science tool for solving specific business problems.
The paper presents a systematic literature review based on the guidelines provided by Tranfield et al. (2003), focusing on widely used data science tools for solving business problems across different business functions and industry verticals. The study yielded 71 research articles deemed relevant after a thorough search strategy and included for the final analysis.
The contribution of this paper is that it presents a multi-perspective synthesis and categorization of a myriad of data science tools: a conceptual framework based on (a) business problems, namely descriptive, predictive, and prescriptive, (b) business outcomes, namely operational, tactical and strategic and (c) business context or industry verticals. The findings will be helpful for academicians and practitioners across various disciplines with a practical guiding framework to solve business problems with the right data science tools from the toolkit effectively, thereby improving decision-making and solution efficacy.
The novel conceptual framework uniquely integrates multiple perspectives to map data science tools to specific business problems, outcomes and industry contexts. This integration offers a tailored solution with significant theoretical and practical value by enabling a more structured, industry-specific context and outcome-driven approach to tool selection for business value creation.
