On August 28, 2012, we made history in the world of Big Data.
That’s when we publicly unveiled our flagship Flexible Analytics and Statistics Technology™ (FAST) product. At an event hosted by Amazon.com’s Machine Learning Group, Context Relevant CEO and Co-Founder Stephen Purpura discussed his decade-long effort to simplify Big Data analytics and how Context Relevant is making routine what today is typically available only to the largest of research and development organizations.
Using a widely known Knowledge Discovery in Databases (KDD) data mining data source, Stephen demonstrated that our patent pending FAST system completes complicated model building and execution tasks in just seconds, compared to the minutes required by Vowpal Wabbit, one of the best systems deployed in the commercial space.
We haven’t been working on this technology so hard and for so long merely to set new speed records. We did it to put the power of Big Data modeling and predictive analytics into the hands of analysts and front-line IT engineers. Until now, this capability required a large, highly skilled and expensive team of data scientists.
Our technologies achieve their huge time-to-insight advantage by using revolutionary processing techniques. We have changed the way companies interact with massive amounts of data, transforming them from collecting big data to profiting from it. Now analysts can perform interactive analysis of massive datasets—up to hundreds of billions of rows—while facilitating rapid predictive modeling and discovery. Our FAST system is incredibly simple to use and scale, and reduces the time it takes to build models from overnight to the time it takes to, say, take a sip of coffee.
In other words, not long at all.
We believe that Big Data represents an enormous opportunity for the enterprise. But its unrelenting volume and the acute shortage of data scientists have enabled only a few of the world’s largest companies to profit from it.
FAST changes all that.
By allowing users to do in seconds what before took hours or days, we’re changing the very nature of what questions can be asked, as well as the models and predictions that can arise from them.