Be data informed not data driven.

The answer to what you should do next is not in the data. It is in your experience and in your understanding of your business and in your processes when informed by the available data.

Improve your business decision making by taking explainability, causality and uncertainty into account.

Explainability in machine learning means stakeholders can understand the machine learning process and what goes into making the predictions.

Causality in machine learning allows you to move beyond simple correlations to make better business decisions and estimate their business value even in changing environments such as economic conditions, supply chain disruptions, and consumer preferences.

Uncertainty in machine learning allows you to quantify how confident you and the model are in the relationships it has discovered, the predictions it has made and decisions you're considering.

"The next revolution will be even more impactful upon realizing that data science is the science of interpreting reality, not of summarizing data." -Judea Pearl, UCLA

Together we work to better understand the appropriate uses and limitations of AI and ML techniques and methodologies.

Together we'll evaluate your data and processes to understand:

  • Your data, how it is collected and what it means.
  • The target metric you'd like to improve.
  • The relationship between the data and your target metrics.
  • What affects the predictions of the ML model and how confident you can be in them.
  • What can be done that will most likely give the largest improvement (uplift) to the target metric.

Every business can get more value from their domain knowledge, processes and data to make better decisions.

Let set up a quick call to:

  • Discuss your business goals
  • Explore the current challenges
  • Create a custom plan
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