Data enrichment is the process of improving a dataset by adding information it lacks, either from other internal sources or from external data. It is also called data enhancement. Typical additions include filling missing attributes, appending demographic or firmographic fields, standardizing categories, and joining reference data such as region codes.
Enrichment differs from cleansing. Cleansing corrects what is already in the table, while enrichment brings in what is not there yet. A customer table with only an email column, for example, can be enriched with company size and industry to make segmentation possible.
For AI work, enrichment often decides whether a dataset is usable at all. Sparse or incomplete records limit what a model can learn, so teams enrich data to restore missing context before training. Recording what was added, and from where, keeps the enriched dataset traceable and reproducible.
Related terms: Data Cleansing · Data Augmentation · Data Quality · AI-Ready Data