Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead....

Delivering information is not just about orderly distribution for the masses. Organizations need to maintain their competitive edge by making new discoveries.
Data Science is the discipline of extracting knowledge from the data landscape. This realm of discovery has a completely different set of requirements from the standard data-to-information life-cycle. What this often means is confusion on how to work with data. Intricity brings a best of breed framework for building harmony between Data Science and Business Intelligence.

This at times requires both a cultural shift as well as a better understanding of the interplay of how the data landscape comes together.

The Data Science and Machine Learning teams not only need unstructured access to data, they also need the ability to experiment with external data sets. Unlike aggregate analytics, often Data Science teams are attempting to model reality by iterating through raw situations, the more successful these teams are, the better they are at predicting the future and optimizing inputs.

Intricitys Data Moat Strategy is a springboard to help the organization develop a data landscape that produces a defensible position in the machine learning marketplace, and makes the most of the organizations data assets.