Traditional BI requires human input to decide what correlated factors to query. As predictive data analytics gets increasingly powerful, the algorithms do the deciding. That spells the end of BI as columnist Bernard Golden knows it - and he doesn't feel fine about it.
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IT executives are starting to realize that there's little value in big data without robust analytics systems that can crunch the numbers and give key decision makers (read: their bosses) easy-to-digest information. With so few real solutions on the market, though, this is easier said than done.
A second technology making a significant impact on solving Big Data problems is in-memory computing, which takes workloads that were traditionally resident on disk-based storage and moves them into main memory. This delivers a performance improvement many times above that which has been possible previously.
According to IDC’s Digital Universe report the data created globally on an annual basis will leap from 1.2 zettabytes this year to 35 zettabytes in 2020 (one zettabyte is equal to one billion terabytes).