Transdisciplinary Benefits of Convergence in Big Data Analytics

Big Data applications can benefit from transdisciplinary convergence spanning multiple domains and opportunity areas. These big data applications in areas as diverse as healthcare, energy, and business must now process a tremendous volume of data and from a variety of data sources. New ways to process and store information beyond the standard three V’s of information characterization defined by volume, variety, and velocity are needed, to also include data variability and veracity. As such, volume and variety of data types alone pose significant challenges to timely and accurate analysis by human as well as machine operators. Although big data applications can instantiate human logic into executable code, process the volume of data quickly, and make correlations across the variety of data, leading to better analysis and predictive capabilities, the resulting conclusions are delimited by other key factors imposed by data velocity that limits immediate inferencing, data variability that limits data consistency, and data veracity that limits data quality. As these challenges are resolved in other disciplinary domains, the spillover benefits can be profound when transdisciplinary advances in one particular domain impact other domain application areas by way of convergence.