CPMAI™ Phase 2: Data Understanding

Assess the data landscape and figure out what you’re really working with. Because...bad data is worse than no data at all.

✨ If your data is garbage, your AI will just deliver expensive, automated garbage faster.

This is where project managers help the team stop and ask: Do we even have the data we need? Is it clean? Is it relevant? Or are we about to feed the model a buffet of nonsense?

In Phase 2 of the CPMAI Framework, the focus is on:

🧹 Evaluating data sources for quality, quantity, and relevance.

🔍 Identifying gaps in the data and whether it actually supports the business problem.

🧪 Understanding biases, inconsistencies, or ethical issues lurking in the data.

Here’s a real-world example of an Epic AI Project Management Fail.

Case Study: Target’s Predictive Analytics Program. Target famously used customer data to predict which shoppers were pregnant — and sent coupons for baby products to a teen girl before her father even knew. The data was technically accurate, but Target hadn’t considered the ethical and privacy implications. The company had to rethink how they used data to avoid alienating customers while still driving sales. The lesson? Just because you can predict something with your data doesn’t mean you should — and understanding your data deeply before acting on it is crucial.

Before you unleash an AI to solve your business problem, remember: If your data’s a mess, your project is just a beautifully wrapped disaster waiting to happen.