The Convergence of Machine Learning, Big Data and Supercomputing
Traditional analytics tools are not well suited to capturing the full value of big data. The volume of data is too large for comprehensive analysis, and the range of potential correlations and relationships between disparate data sources, from back end customer databases to live web based clickstreams, are too great for any analyst to test all hypotheses and derive all the value buried in the data. According to Peter Ungaro, Machine learning is ideal for exploiting the opportunities hidden in big data.
Freed from the limitations of human scale thinking and analysis, machine learning is able to discover and display the patterns buried in the data. Peter says that advances in supercomputing and high-performance storage are also important in getting real value form Big Data. He examines whether the technologies and systems used to solve these problems are diverging or converging over time and how changes in system architecture and a more holistic approach can change the game for how companies process data and discover new insights in the future.