Data Cognition Engine for Big Data Analytics

SisenseHunch

What is Sisense Hunch™?

A Data Cognition Engine that rewrites the rules of Big Data analytics, condensing immense amounts of data into lightweight neural networks for quick and easy analysis at the edge.

Sisense Redefines Big Data Analytics

Leverage insights from terabytes of data and process hundreds of thousands of queries with sub-millisecond response time using the latest AI technologies.


Make Big Data
Interactive

Enjoy interactive data exploration over terabytes of data with sub-millisecond response time.


Bring the Value of
Big Data to the Edge

Compress terabytes of data and put the power of your data anywhere you need it.


Eliminate Big Data
“Storage”

Completely replace Big Data repositories with a nearly accurate solution that will satisfy most analytic needs.


100% Data Privacy Guaranteed
Does not retain any level of detail adhering to policies and regulatory compliance.

The Secret Behind Sisense Hunch™ 

 

  1. First, Sisense Hunch creates a huge set of possible queries that cover many angles of the Big Data store.
  2. Then, Sisense Hunch uses this information to train a type of machine learning algorithm called a deep neural network (DNN).
  3. Now, Sisense Hunch can be placed anywhere at a tiny fraction of the original data size, and answers queries directly with sub-millisecond response time and 99% accuracy or better for most datasets.

Celestica – Innovation Design Partner

Celestica uses Sisense Hunch on terabytes of data to reduce query response time from 30-minutes to milliseconds.


Sisense Hunch is the first of its kind

Sisense Hunch can be applied to a number of revolutionary applications. For starters, Sisense Hunch put the insights of billions of rows of data into a sensor, a phone, or a wearable device, offering the power of big data in a small, portable, cost-effective, and secure IoT package – bringing Big Data analytics straight to the edge.

If you’re ready to tackle your Big Data questions in a whole new way, reach out to us here:

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