Big data as a value generator in decision support systems: a literature review

Abstract

Purpose – This paper aims to analyze how decision support systems manage Big data to obtain value.
Design/methodology/approach – A systematic literature review was performed with screening and
analysis of 72 articles published between 2012 and 2019.
Findings – The 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/value – As 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.