Toggle Main Menu Toggle Search

Open Access padlockePrints

A Review of Data Science in Business and Industry and a Future View

Lookup NU author(s): Dr Shirley Coleman

Downloads


Licence

This is the authors' accepted manuscript of an article that has been published in its final definitive form by John Wiley & Sons Ltd., 2020.

For re-use rights please refer to the publisher's terms and conditions.


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.


Publication metadata

Author(s): Vicario G, Coleman S

Publication type: Article

Publication status: Published

Journal: Applied Stochastic Models in Business and Industry

Year: 2020

Volume: 36

Issue: 1

Pages: 6-18, 43-48

Print publication date: 08/03/2020

Online publication date: 29/10/2019

Acceptance date: 28/08/2019

Date deposited: 31/07/2020

ISSN (print): 1524-1904

ISSN (electronic): 1526-4025

Publisher: John Wiley & Sons Ltd.

URL: https://doi.org/10.1002/asmb.2488

DOI: 10.1002/asmb.2488


Altmetrics

Altmetrics provided by Altmetric


Actions

Find at Newcastle University icon    Link to this publication


Share