Web data is only useful if you can trust it. But on a feed that refreshes daily across thousands of sources, quality is not a one-time check - it is a system that has to run continuously.
This whitepaper sets out a practical framework for web data quality: the dimensions that matter, the checks that catch errors before they ship, and how to monitor quality over time.
Key takeaways
Why quality is the real differentiator
Most providers can get data. Far fewer can prove it is accurate, complete, consistent and current. As data feeds pricing decisions and AI systems, the cost of a silent error rises sharply - a wrong price or a missing field propagates everywhere downstream.
This framework treats quality as a set of explicit, measurable rules rather than a vague promise.
Who this whitepaper is for
This whitepaper is for the teams building AI features that depend on external, real-world data.
How we apply it
The framework reflects how we run our own feeds. Our Data Cleaning & Enrichment service applies these checks to raw web data so what you receive is validated and analysis-ready, and it underpins every Data as a Service feed we deliver.
- A working definition of quality for web data
- Validation rules that catch errors before they ship
- Deduplication and accurate product matching
- Freshness - measuring and guaranteeing how current data is
- How to monitor quality continuously, not just at launch
Frequently asked questions
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Data, analytics and engineering teams that depend on web data and need it to be reliable.
A practical, repeatable framework for defining, measuring and enforcing quality on continuously refreshed web data.