Big Data Monetization: Platforms and Business Models

The main goal of our PhD project is to propose a methodology that can be widely applied to determine the monetary value of a Datum in a Big Data environment. In order to define our problem, we started by investigating how monetization in those environments have been suggested in academic literature. By observing the results, it was possible to conclude that little attention has been given to the dimension Value in academic studies related to Big Data when compared to researches directed to the other three classical dimensions (Volume, Velocity and Variety). More specifically, in terms of economic value, studies are even scarcer, and the existing ones do not share a common view on how to measure this value. Bearing that in mind, we have drawn our hypothesis that it would be possible to develop a data-driven methodology (quantitative methods based on artificial intelligence) that could highlight the monetary value of a data asset (value dimension) taking into account the different applications, contexts and scenarios related to those data. We applied a formal process of systematic literature review based on the methodology suggested by Kitchenham to find out what methods have been applied to determine the relevance and value of data in these environments and if these methods are based, in any way, on information theory. The results showed that, in spite of the progress made on the topics of Big Data and the application of analytical methodologies over the last decades, there is no method based on data that is widely used to determine the value of a datum in a Big Data environment. If, on the one hand, monetization in Big Data environments is still a field that needs to be better explored in academic literature, on the other hand, these intangible assets, i.e., data, grow exponentially and are more and more present in the corporate world. It highlights the opportunity to develop studies in search of standards that can be widely accepted and used to this end. We have now finished 12 moths on this research (1
^st
year).