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.
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
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