Good piece by Tina Groves. I have discovered that many technologists, especially in the small to medium sized businesses, do not understand the value Hadoop or Big Data. Or how the methods work together with predictive analytics. Here is a perspective from IBM, using their suite of tools. Not universal, but useful to review.
It is also key to understand that analytics methods are most purposefully applied against decision processes. They do not always need complex methodologies, but start with the simplest techniques, like visualizing data that is created by your methods. Groves writes:
" .. For many people, big data is synonymous with Hadoop. Certainly, the ability to store and processing vast amounts of data on commodity hardware has fueled a new generation of applications. The synergies of Hadoop, fast and reliable internet and the increasing love for all things mobile have channeled heady investments in software as a service (SaaS) startups.
IBM understands the opportunity with Hadoop and addressing big data challenges. Early and continued investment in this area is why IBM has been consistently rated as a leader in numerous analyst reports, including the recent Forrester Wave on Big Data Hadoop Solutions. ...
In the big data realm "data” means obtaining data as close as possible at the source, and then analyzing it for immediate action. IBM® InfoSphere Streams enables tapping into streaming data or data in motion where the throughput rates are from the thousands to millions per second. Whether that data originates from medical devices to monitor neonatal infants or from sensors to predict manufacturing yields, InfoSphere Streams has opened the door to exciting new applications of analytics. ... "
This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions. #MidsizeIBM
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