PurposeThis paper aims to analyze how decision support systems manage Big data to obtain value.Design/methodology/approachA systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.FindingsThe findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.Originality/valueAs it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

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This article aims to present a framework for structuring Big Data projects. The methodological procedures were divided into two phases: in-depth interview and focus group. The first phase embraces 12 in-depth individual interviews. In the second phase, three sessions of interviews with focus groups were applied. Both phases had as research subjects professionals with experience in Big Data projects. The analysis process was based on categorization through theory-driven and data-driven codes. Based on our analysis, it was possible to present a definition of a Big Data project and explore the beginning and its phases. We also identified 17 different critical factors in Big Data projects and proposed a discussion on technical and behavioral skills and decision-making issues in Big Data projects. We also developed and validated a framework to help structure a Big Data project. As the main theoretical contribution, our study aligns with a growing body of researchers who are proposing to debate Big Data projects, and with that increasing maturity on the topic. As a practical contribution, we present a framework that we hope will contribute to professionals working in the area, helping to conduct a Big Data project through a systemic view.

This article aimed to identify relationships between Big Data and Decision Support Systems. For this, we conducted a search in the Scopus database and as a result, we identified a report according to the increased frequency of publications, frequency of publications in journals and, using the VOSviewer software, we performed an analysis of words co-citation. We identified 5 groups of keywords that suggest different areas of study (e.g., logistics, health and social media), as well as a more recent focus on studies aimed at sustainable development, machine learning, analytical techniques and decision-making processes decision. An important contribution that should also be highlighted was the strong relationship between the keywords Big Data, artificial intelligence and decision making, suggesting studies involving the three terms in a large number of works.