AI & Data

Start by defining what one example represents. A row might describe a customer, a transaction, a photograph, or one moment in a time series. Those units determine how duplicates, labels, and evaluation splits should work. Ten measurements from one device are not necessarily ten independent devices.

Features are inputs available to the model. Labels are target outcomes used in supervised learning. Check when each feature becomes available: a cancellation reason recorded after a customer leaves cannot fairly predict that departure beforehand. This is a form of leakage even when the field looks highly predictive.

Inspect missing values, annotation disagreements, unusual ranges, and changes in collection methods. Missing information can carry meaning; replacing every missing value with zero can conflate an unknown quantity with a real zero. Document the treatment and test it on representative examples.

Record provenance and access rules alongside the dataset. A public URL alone does not establish permission to reuse every item for every purpose. Collect only information needed for the task and define retention and deletion procedures. Evaluate separately on groups or conditions where errors would otherwise disappear inside an overall average.