This paper is a short introduction on (Big) Data Science and Intelligence for the RDA educational corner. Its purpose is to motivate a greater discussion of what is Big Data, how it is transforming the future of finance and what are the essential opportunities and concerns when using Big Data. "Intelligence" in Big Data is used to emphasize that mathematics is an essential part of the algorithmic and the statistical approaches we use when searching, estimating or seeking answers to our problems. When we use the power of IT, Mathematical and Statistical Intelligence embedded in numerous applications and studies seek to bridge theoretical constructs and their computational realizations. Their integration is a complete system of automatic and learning know how (we may call AI, Learning Machines or what not and by any other name). It is now expanded by systemic computing, data analytics and management to do much more with a lot less. However, in the long run, doing more without Intelligence, replacing intentionality by machine rationality, lead to an evolution where choices are no longer made but instead, are imposed by a data complexity and expert systems that may embed far greater risks than we can expect. In this case, without the power of a human intelligence and a mathematical (objective) rationality, our use of BIG data without science are similar to seeking to go from one place to another without a map.
- Data Science
ASJC Scopus subject areas
- Statistics and Probability
- Economics and Econometrics
- Statistics, Probability and Uncertainty