Real-world data

What is real-world data?

Quick answer

Real-world data is information generated by real activity — customer conversations, transactions, field reports, machine logs, documents — rather than created artificially for a model. AI developers use it to train and test systems on the messy, varied situations they will meet in practice. It is often proprietary, so it is usually obtained by licensing it from the organisation that holds it.

Why it matters

Models trained only on curated or synthetic examples can fail on edge cases. Real-world data captures the variation, errors and context of real operations.

In healthcare, the term also has a specific regulatory meaning: data relating to patient health status collected outside clinical trials.

Examples

Support tickets and chat logs
Sales and service call recordings
Insurance claims files
Construction daily logs and RFIs
Equipment maintenance logs and sensor readings

Real-world vs synthetic data

	Real-world	Synthetic
Origin	Actual activity	Generated by a model or simulation
Edge cases	Naturally present	Only if designed in
Privacy burden	Higher	Lower
Typical role	Ground truth, fine-tuning, evaluation	Augmentation, scaling

Rights and privacy

Usually contains personal or confidential information
Requires de-identification and contractual review
Licensed with defined permitted uses

Human-generated data (/human-generated-data)
Operational data (/operational-data)
Synthetic vs real business data (/insights/synthetic-data-vs-real-business-data)

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