Statistics.Data Mining.Data Science.Machine Learning.AI.


No matter what you call it, the goal is to turn data into insights and improve decision-making.

Proponents suggest that removing the human element from the equation can result in more efficient and transparent outcomes, but what happens when the models or training data are incomplete, poorly documented, flawed, or even unlawful?

Compliance and Documentation = IP Value

When valuation relies heavily on intellectual property, software, algorithms, and data should be compliant and well-documented. Non-compliance and IP uncertainty pave a path to lost deal value, lawsuits, regulatory fines, or even collapse of the company.

Data Science Maturity Assessment

There are two types of organizations:

those executing on a data strategy and those that will be acquired by the former.

Let’s make sure you’re in control of the outcome.

Data Privacy Maturity Assessment

Data privacy has gone from rare to required in less than a decade; are you prepared for what comes next?

Machine Learning Model Assessment

Machine learning has the potential to create – or destroy – tremendous value.

Take your pick.


Most data science and machine learning projects require large amounts of data, and it’s essential that this data is obtained and used lawfully and ethically. When data is sourced or used illegitimately, your “AI” can become fruit of the poisonous tree, destroying company value and reputation. Our team can help your organization identify and mitigate the impact of risks related to data compliance.

  • Data with contractually-restricted usage such as non-commercial or no-derivatives
  • Data with regulatorily-restricted usage such as health or financial information
  • Data with uncertain origin or usage rights such as health or financial information


Even if your company’s data is not intrinsically non-compliant or unlawful, its usage in certain contexts may create risks. Certain modeling techniques may be either prohibited or required in certain contexts, either due to transparency requirements or IP restrictions. Our team can help your organization identify and manage risks related to machine learning models so that you can maximize your enterprise value and efficiency.

  • “Black box” models in situations requiring explainability

  • Contractually-restricted model applications

  • Regulatorily-restricted model applications

  • Patented or protected algorithms or modeling processes


Data science is hard. Figuring out how to integrate data science into your risk management and compliance frameworks is even harder. Employees, customers, investors, and regulators, however, don’t care if it’s not easy; they just want to know that you have a responsible plan to make it happen. That’s why we’ve open-sourced a Responsible Data Science Policy framework to help organizations get started on this journey.

You can use the framework independently, or we can work with you to:

  • Integrate internal and external systems to meet procedural requirements.
  • Develop bespoke prescriptive procedures that address your company’s specific data science activities.
  • Train personnel on privacy requirements and ethical best practices within machine learning, AI, and other activities.

Let us know how we can help.

Whether you’re looking for more information about one of our products or need to talk about custom services, don’t be afraid to ask.