Data Analytics Implications
This special theme issue of IEEE Potentials introduces the rapidly evolving area of data analytics, which encompasses data analysis, data fusion, data storage, data sources, infrastructure and technology, screening and filtering algorithms, machine learning, and complexity.
Abstract
This special theme issue of IEEE Potentials introduces the rapidly evolving area of data analytics, which encompasses data analysis, data fusion, data storage, data sources, infrastructure and technology, screening and filtering algorithms, machine learning, and complexity. An emergent technology from data analytics is big-data analytics, defined by the Gartner IT Glossary as "high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation." Big data definitely changes the way we approach data analytics. For example, we favor very large volumes of data over smaller volumes, even if the smaller volumes are more accurate. Also, our big-data analytics is more focused on probabilities and correlations over causality and certainty. In fact, The Fourth Paradigm: Data-Intensive Scientific Discovery states that "increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets."