Full Answer

AI training works by finding patterns across many consistent examples. That only works if the examples are actually comparable. When event names, parameters, or value formats drift over time, the model cannot tell that purchase in January and order in March describe the same behaviour. Instead of one strong signal built from a year of data, it sees several weak, disconnected ones, and the historical depth you worked to accumulate loses most of its value.

This is where capture method matters more than people expect. Schema enforced at the point of collection, the way a purpose-built tracking layer applies it automatically, keeps every event in the same shape without anyone policing it by hand. Custom or ad-hoc implementations tend to drift precisely because consistency depends on memory and discipline rather than the system itself.

Informatica's 2025 survey, where 43% of data leaders named data quality and readiness their biggest AI obstacle, reflects this directly. The blocker is rarely a shortage of data; it is data that cannot be trusted to mean the same thing twice. Fixing the schema once, at the source, is cheaper than cleaning years of mismatched records later, and it is the difference between history that trains a model and history that just takes up storage.