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Manifold Blog

Your Project Needs a Data Readiness Audit

Posted by Vinay Seth Mohta on Mar 21, 2019 6:00:00 AM
Vinay Seth Mohta
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In the early phase of a new project, we dive into the “Understand” step of our Lean AI framework. There are two main forms of understanding we aim for — business understanding and data understanding.

In our work, we have found our “AI Uncertainty Principle” to be a useful heuristic to keep in mind. Namely, the value that you get out of an AI project is bounded by the value of the business problem multiplied by the data quality multiplied by the predictive signal in the data. If either the value of the business problem or the data quality is too low, then your project won't be successful. The last factor, predictive signal, we have no control over — but the first two we do. That's why it's critically important to de-risk these factors early with good data auditing.
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Before starting big projects, we strongly recommend protecting your organization from costly missteps by performing some form of AI Data Readiness audit. This audit should look at:

  • identifying the data you have available, including assessing quality and quantity
  • integrating the data you have
  • addressing data engineering needs, including building pipelines and monitoring the system

At Manifold, we have created a 15-page audit document incorporating decades of collective experience. We review this document with clients in the early phase of an engagement to ensure we have a good handle on data quality and risks.

Interested in learning more? Get in touch at

Topics: Data engineering

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