Advancing beyond technicism when managing big data in companies’ decision-making

Design/methodology/approach – By adopting a research strategy based on case studies, the paper depicts the main phases and challenges that companies ‘live’ through in approaching big data analytics as a way to support their decision-making processes. The analysis of case studies has been chosen as the main research method because it offers the possibility for different data sources to describe a phenomenon and subsequently to develop and test theories. Purpose – In recognising the key role of business intelligence and big data analytics in influencing companies’ decision-making processes, the paper aims to codify the main phases through which companies can approach, develop, and manage big data analytics. Findings – The paper provides a possible depiction of the main phases and challenges through which the approach(es) to big data analytics can emerge and evolve over time with reference to companies’ decision-making processes. Research implications – The paper recalls the attention of researchers in defining clear patterns through which technology-based approaches should be developed. In its depiction of the main phases of the development of big data analytics in companies’ decision-making processes, the paper highlights the possible domains in which to define and renovate approaches to value. Research limitations – The proposed conceptual model derives from the adoption of an inductive approach. Despite its validity, it is discussed and questioned through multiple case studies. Additionally, its generalisability requires further discussion and analysis in the light of alternative interpretative perspectives. Practical implications – The reflections herein offer practitioners interested in company management the possibility to develop performance measurement tools that can evaluate how each phase can contribute to companies’ value creation processes. Originality/value – The paper contributes to the ongoing debate about the role of digital technologies in influencing managerial and social models; it provides a conceptual model that is able to support both researchers and practitioners in understanding through which phases big data analytics can be approached and managed to enhance value processes.