Why do Machine Learning on Big Data?

Traditional analytics tools are not well suited to capturing the full value of big data.

The volume of data is too large for comprehensive analysis, and the range of potential correlations and relationships between disparate data sources — from back end customer databases to live web based clickstreams —are too great for any analyst to test all hypotheses and derive all the value buried in the data.

Basic analytical methods used in business intelligence and enterprise reporting tools reduce to reporting sums, counts, simple averages and running SQL queries. Online analytical processing is merely a systematized extension of these basic analytics that still rely on a human to direct activities specify what should be calculated.

Machine learning is ideal for exploiting the opportunities hidden in big data.

It delivers on the promise of extracting value from big and disparate data sources with far less reliance on human direction. It is data driven and runs at machine scale. It is well suited to the complexity of dealing with disparate data sources and the huge variety of variables and amounts of data involved. And unlike traditional analysis, machine learning thrives on growing datasets. The more data fed into a machine learning system, the more it can learn and apply the results to higher quality insights.

Freed from the limitations of human scale thinking and analysis, machine learning is able to discover and display the patterns buried in the data.

“Data analytics is about discovering knowledge from large volumes data and applying it to the business. In this process, the speed of the data scientist is actually a limiting factor. Skytree makes this much less of a limiting factor. It accelerates the work of the data scientist.



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