Raw scraped data is rarely complete on its own. A product record may include a name and price but lack a standardised category, brand identifier, or competitor comparison. Data enrichment adds this context by joining the scraped record against one or more secondary data sources.
Common enrichment steps in scraping pipelines include: geocoding physical addresses to latitude/longitude using a mapping API, resolving company names to standardised identifiers (Dun & Bradstreet DUNS, LEI), appending demographic data to a geographic region, expanding product SKUs to full catalogue entries, or classifying text into a taxonomy using an LLM.
Enrichment introduces dependencies on external APIs, which must be accounted for in rate limiting (many enrichment APIs have strict quotas), error handling (enrichment failure should not block the primary record from being stored), and cost management (enrichment calls add variable cost proportional to record volume).