Data Science and its Relationship to Big Data and Data-Driven Decision Making

By Foster J. Provost, Tom Fawcett. Big Data, 2013

1,410 citations recorded by the source corpus.

Abstract Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot—even “sexy”—career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

Subfields: Information Systems and Management, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/bd1c1d5540f246090e740c0d5a0fa7f2c64059d1

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Citations in the corpus

Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics (2016, methodology): “The analyses also show a striking result of particular relevance to the current discussions of using big data for 884 MIS Quarterly Vol. 40 No. 4/December 2016 improving business decision making (Provost and Fawcett 2013).”
Explaining machine learning models in sales predictions (2017, background): “Data-driven decision-making is the practice of basing decisions on the data analysis, rather than on intuition (Provost and Fawcett, 2013).”
Big data analytics capabilities: a systematic literature review and research agenda (2017, background): “The main argument for the importance of a data-driven culture is that although many companies implement big data projects, the vast majority rely not on the information extracted from data analysis, but rather on managerial experience or intuition (Provost and Fawcett 2013).”
Reviewing and exploring innovative ubiquitous learning tools in higher education (2019, background): “Learning sources with the big data analytic approach helps to empower the orientation to improve the practical strength (Provost and Fawcett 2013).”
Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management (2013, background): “As Provost and Fawcett (2013) write, “But data science involves much more than just data-mining algorithms.”
Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, neutral): “In sequence, strong BDAC’s can support, and in some cases even replace, human decision-making and automatize action in response to generated insight (Provost & Fawcett, 2013).”
Big Data Investment, Skills, and Firm Value (2014, neutral): “…data” is used to describe technologies enabling the collection, management, and analysis of datasets that are too large for conventional database systems (Dumbill 2012), and recent work discusses how big data tools are enabling new decision-making capabilities for firms (Provost and Fawcett 2013).”