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Optimising data science and big data Posted on : May 23 - 2015

This year we can expect to see the data storage and data science landscapes mature, break our trust, and surprise us.

Last year was a big year for big data, and enterprises are increasingly showcasing more production uses of Hadoop, demonstrating more successes with a broader array of big data technologies, and solidifying innovative ways of using data science to improve business outcomes.

At the same time, innovators are continuing to push the machine learning boundaries, introducing new techniques that require less human intervention, like deep learning.

With that in mind, below are our data science and data-related predictions for the C-Suite to be aware of this year and in years to come.

Taking action on data, not just storing it: Hadoop adoption and growth continues

We will see increasing numbers of companies doing more with the data they have in Hadoop. They will be performing more analytics and running more applications – adding greater utility to the data from which they already derive value.

This will be partially enabled by further and faster penetration of SQL on Hadoop, as these environments continue to allow more and more data to shift from traditional stores, co-locate into one environment, and become accessible to downstream apps and learning algorithms.

The shift from “One Algorithm Wonders” (Point Solutions) to data science platforms

 

Within data science, there is a complex market evolving, and one analyst recently delivered a snapshot of this “machine intelligence” landscape. There will still be an emphasis on hiring data scientists, and a number of specialised new apps will power unique projects in the market, while introducing some challenges.  View more