Received: 14 November 2018 Revised: 4 August 2019 Accepted: 28 August 2019 DOI: 10.1002/asmb.2488 DISCUSSION PAPER A review of data science in business and industry and a future view Grazia Vicario 1 Shirley Coleman 2 1 Department of Mathematical Sciences, Politecnico di Torino, Torino, Italy 2 School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK Correspondence Grazia Vicario, Politecnico di Torino, Department of Mathematical Sciences, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy. Email: grazia.vicario@polito.it Abstract The aim of this paper is to frame Data Science, a fashion and emerging topic nowadays in the context of business and industry. We open with a discussion about the origin of Data Science and its requirement for a challenging mix of capability in data analytics, information technology, and business know-how. The mission of Data Science is to provide new or revised computational theory able to extract useful information from the massive volumes of data collected at an accelerating pace. In fact, besides the traditional measurements, digital data obtained from images, text, audio, sensors, etc complement the survey. Then, we review the different and most popular methodologies among the practitioners of Data Science research and applications. In addition, because the emerging field requires personnel with new competences, we attempt to describe the Data Scientist profile, one of the sexiest jobs of the 21st Century according to Davenport and Patil. Most people are aware of the need to embrace Data Science, but they feel intimidated that they do not understand it and they worry that their jobs will disappear. We want to encourage them: Data Science is more likely to add value to jobs and enrich the lives of working people by helping them make better, more informed business decisions. We conclude this paper by presenting examples of Data Science in action in business and industry, to demonstrate the collection of specialist skills that must come together for this new science to be effective. KEYWORDS business improvement, Data Scientist profile, Industry 4.0, knowledge discovery, SME 1 INTRODUCTION AND RATIONALE OF A DATA SCIENCE REVIEW This paper has been motivated by the need to frame the research area of Data Science in the context of business and industry. In the last three decades, Data Science has grown up and expanded its research area attracting interests and researchers from many neighboring scientific fields. This increasing interest in Data Science arises both from public and private organizations. For example, an internet shop can exhibit custom-made products and focused advertisements if it is possible to interpret the data of the customers' web surfing, or it can forecast demand and improve its logistics management if the sold-out data are properly analyzed. In the area of health care, where therapies and diagnoses are more and more digitized and registered, the use of Data Science methodologies can prevent faulty diagnoses, better detect what are the most appropriate care plans for the patients, and improve the quality of treatments. In addition, in industrial production, data coming from different work phases are of primary importance to improve product quality and raise awareness of failures, speed, and performance. 6 © 2019 John Wiley & Sons, Ltd. wileyonlinelibrary.com/journal/asmb Appl Stochastic Models Bus Ind. 2020;36:6–18.