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.
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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Serving customers by looking forward as well as back is a big promise, but the power of today’s new digital capabilities is vast and growing.
Let’s talk about how digital can work for your business.