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Causality
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Understanding predictive data

Most businesses work with automated machine learning platforms that only take existing known model structures and attempt to fit data into them. These results are based on correlation, not causation, and lack the right actionable insights and models to explain the predictions being made. The results don’t identify the quality of behaviors in the data that are predictive in nature.
The Cognizant Causality Service uses predictive analytics and its assumption-free engine to learn, understand and adapt its conclusions. This allows our clients to uncover bias and harness predictive signals in their data to quickly zero in on what matters most: identifying the best actions to achieve business outcomes.
Cloud-based analytics solution reduces capital cost by $30M

Our AI analytics solution also helped mine operators monitor throughput and efficiency by viewing the root cause of lower yields on a near-real time basis.

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An Earth-moving vehicle on duty in a mining site

Our approach

Understanding data is often complex. Dealing with bias and causality requires a practical, proven mathematical approach. Our causality engine simplifies the process, reduces bias and provides strategic and tactical actions that your business can take in response to change.
You can identify relationships in the variables and build a customized model. That model then refines, trains and corrects itself, providing true causal factors.
The engine discovers that variables are the best predictive drivers for the user-defined business objective from thousands of variables. It powerfully discovers combination effects where factors that are weak predictors individually are strong in combination. This system automatically provides multiple recommendations to achieve the targeted goal.

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Our clients turn to us to help them reimagine ways of working with technology.

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