Data quality is how well data fits its intended use, judged on qualities such as accuracy, completeness, consistency, timeliness, validity and uniqueness. Teams track it with automated checks, for example flagging duplicate customer records or missing order dates before they reach a finance report.
Training data is the set of examples a machine learning model learns from. Its quality, coverage and labelling largely determine how well the model performs.
Data migration is moving data from one system, format or location to another while keeping it complete and correct. Typical cases are legacy database replacements and moves to the cloud.
Data observability is continuous monitoring of data pipelines and datasets so that problems are caught before users notice. It watches freshness, volume, schema, distribution and lineage.
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