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

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.

Big data for policymaking: fad or fasttrack?

“…While there is no widely accepted definition of the term, the most basic description is that big data means datasets that are too large for traditional processing systems and require new technologies (Provost and Fawcett 2013). This not only refers to the size of the data, but also to its variety, velocity and veracity.…”

Big Data in the Industry - Overview of Selected Issues

“…Companies have recognized this potential and are currently using it extensively to improve their offerings and more accurate adjust to consumers needs. Similarly, after mastering the technology of flexible data mass processing, organizations in the Big Data 2.0 era will take full advantage of the information potential they will be able to achieve [9].…”

Exploring Qualitative Data: the use of Big Data technology as support in strategic decision-making

“…Although such integration has already been put in practice for operational decision-making, it is still a new issue for strategic decision-making (Wladawsky-Berger, 2013, Provost and. Authors as Brynjolfsson et al (2011), McAfee and Brynjolfsson, (2012) and Provost and Fawcett (2013) have stated that data-driven decision-making processes are better because they are based on evidence rather than intuition.…”

“…According to Russom (2011), -Volume‖ is considered the primary attribute of Big Data. Organisations are dealing with terabytes and petabytes of data and this outstrips the traditional databases (Thusoo et al,2010;Madden, 2012;Shah et al, 2012;Provost and Fawcett, 2013;Chen et al, 2014).…”