Author Description

Big Data has created radical shifts, in less than a decade, with implications that are more subtle than they appear. While the technology is taking far major leaps ahead creating an unprecedented amount of data there is an urgent need to address Big Data' subtle implications and challenges related for instance to the establishment of mathematical theoretical frameworks to scale inferences and machine learning algorithms. Big Data has shown its tremendous potential to transform industries, such as healthcare and insurance industries, and to empower artificial intelligence and machine learning at an unequivocal scale, today. However, there are concerns that Big Data may lose much of its usefulness, potentially generating new unintended consequences if epistemological (knowledge generation) challenges are not addressed. Big Data has grown tremendously rapidly leading to data to outpace concepts. Conceptual investigations and mathematical frameworks are to theory formulation what methodology is to Big Data gathering and Big Data analytics. A lack of conceptual frameworks to address epistemological challenges of Big Data may slow progress in innovations and delay the development of Big Data's prospective applications according to recent reports and publications on Big Data. There is an urgent need to address Big Data's epistemological challenges along with technological challenges, in both public and private sectors, and to catch up with both shortages in skills and concepts to better leverage Big Data for our increasingly data-driven society.