Research directions for harvesting cross-sectorial correlations towards improved policy making

The current paper focuses on the emerging problem of the management of and the processing required for the massive amounts of interdisciplinary data produced nowadays. As it becomes apparent that “the truth and the useful information is drowned in a sea of irrelevance due to the vast amount of information available”1, there are strong indications that seemingly irrelevant co-occurrences of events and subtle links between them may form pieces of the same puzzle that complement each other towards the revealment of predictive or explanatory indicators for many sectors of the modern economy. To this direction, modern technologies like data mining, data and visual analytics, artificial intelligence, etc. can be of significant value, if offering a comprehensive communication of potentially useful information to the appropriate stake holders and/or policy-makers. In this context,a multi-purpose platform for data analytics is briefly exhibited in order to demonstrate the potential of such approaches to policy making.