Never Mind Clean Data. Annotate As You Collect It.
CIO, Wednesday, August 5th, 2026
Capturing metadata at collection time prevents the loss of context that accurate AI models require.
Organizations pursuing AI face a paradox: overly cleaned data removes context essential for accurate predictions. Rather than sanitizing data after collection, experts recommend annotating and tagging information at the source to preserve lineage, provenance, and situational detail.
In-stream labeling captures what is free to capture and unrecoverable later, such as system origin, while allowing lost context to be reconstructed.
Companies including LinkedIn and Miro achieved substantial improvements in AI accuracy by implementing schema validation and metadata attribution earlier in data pipelines, turning governance into a proactive practice rather than reactive cleanup.