A review of data science in business and industry and a future view

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.

A review of data science in business and industry and a future view by G. Vicario and S. Coleman

“…Vicario and Coleman provide in this section a thoughtful and comprehensive overview of knowledge discovery with an emphasis on the generation of information. In parallel with this description, Kenett and Shmueli present a framework for designing and assessing information quality.…”

“…The paper of Vicario and Coleman discusses the skills and background required by data scientists working in an organization. An important part of this consideration is the organizational aspect of data scientist employment.…”

“…The review of Vicario and Coleman provides us with a platform for discussing and further developing topics of major importance to applied statistics and analytics. This has several implications briefly annotated below, with this discussant's opinion and experience: How should we practice Statistics ?…”

“…This discussion follows the structure of the review paper by Vicario and Coleman . Let me first start with a vote of thanks to the authors who provide a comprehensive landscape of important challenges and opportunities to modern applied statistics.…”

Data science in organizations: Conceptualizing its breakthroughs and blind spots

“…organizational contexts (Medeiros et al, 2020;Steinberg and Aronovich, 2020;Vicario and Coleman, 2020). We thus adopt Provost and Fawcett's (2013: 53) conceptualization of data science in the organizational context: understanding phenomena via the analysis of data with the ultimate goal of improving decision-making.…”