🧠 AI Projects Start with DATA — Not the Model (CPMAI Data Literacy Breakdown)

Before you touch a model, review your data strategy first. I'll assume you've already nailed down a clear, strong business case for using AI — that's step one. But even a perfect use case will fail if your data foundation is weak.

Here's the core truth every AI/PM practitioner should tattoo on their brain:

This is the classic GIGO principle — Garbage In, Garbage Out. No amount of clever modeling fixes bad data.

To make this easier to teach (and to learn myself, since I'm currently studying for the PMI-CPMAI™ certification), I built a mind-map covering the full picture of AI data literacy. Here's what's in it:

🔹 Data Types - Quantitative vs. Qualitative, Real Data vs. Synthetic Data
🔹 Real-World Examples - mapped to each CPMAI phase (Business Understanding → Data Understanding → Data Preparation → Modeling → Evaluation → Deployment → Monitoring)
🔹 How to Select the Right Data for your specific AI project
🔹 7 Steps for AI Data Governance - practical guardrails you can apply immediately

If you're prepping for CPMAI certification or just want a solid mental model for AI project data management, this map should save you hours of scattered reading.

👉 Would love feedback from anyone else on the CPMAI journey - what tripped you up on the data literacy section?

#CPMAI #AIProjectManagement #GIGO #DataGovernance #PMI #AIStrategy #DataLiteracy

Data in AI project - By Tony