Meridian Regional Airlines' predictive-maintenance team has moved from the Business Understanding phase into Data Understanding. The project lead reminds the team that this phase is not "just Business Understanding again with more data." What is the AI-specific focus that distinguishes the Data Understanding phase in CPMAI?

The CPMAI Data Understanding phase is where a team stops evaluating the business case and starts evaluating whether the data on hand can actually support it — dataset availability, quality, volume, and relevance to the stated business question. For Meridian's predictive-maintenance initiative, that means checking whether sensor telemetry and maintenance-log history are complete enough, span enough aircraft and failure types, and are trustworthy enough to train a model, not re-litigating whether predictive maintenance is worth doing (that's Business Understanding, already closed). Re-negotiating the project charter and capital budget is a Business Understanding activity revisited only if Data Understanding forces an iteration back — it is not the phase's own focus. Selecting a cloud vendor for production hosting is an Operationalization concern, far downstream of confirming the data even exists. Writing UAT acceptance criteria belongs to Model Evaluation and deployment planning, where success is measured against defined criteria — that comes after the team knows it has usable data. The distinguishing AI-specific risk in Data Understanding is that traditional software projects can usually assume the data exists in usable form; AI projects cannot make that assumption, which is exactly why CPMAI gives it its own phase rather than folding it into requirements-gathering.