Data Multiverse: The Uncertainty Challenge of Future Big Data Analytics

With the explosion of data sizes, extracting valuable insight out of big data becomes increasingly difficult. New challenges begin to emerge that complement traditional, long-standing challenges related to building scalable infrastructure and runtime systems that can deliver the desired level of performance and resource efficiency. This vision paper focuses on one such challenge, which we refer to as the analytics uncertainty: with so much data available from so many sources, it is difficult to anticipate what the data can be useful for, if at all. As a consequence, it is difficult to anticipate what data processing algorithms and methods are the most appropriate to extract value and insight. In this context, we contribute with a study on current big data analytics state-of-art, the use cases where the analytics uncertainty is emerging as a problem and future research directions to address them.