Data Enrichment: How to Add Value to Raw Scraped Data

Imagine you've spent hours scraping data from a website—product names, prices, and descriptions. But when you look at the dataset, it feels incomplete. This is the reality of raw scraped data : useful, but limited. To unlock its full potential, you need data enrichment . In this tutorial, we'll walk through data enrichment techniques using Python. What Is Data Enrichment? Data enrichment is the process of enhancing raw data by adding relevant information from external sources: Geocoding addresses to get latitude/longitude Appending product categories using a database Validating email formats with regex or external tools Enhancing user profiles with demographic data from APIs Why Enrich Data? Improves decision-making : Enriched data provides context Reduces errors : Cleaning and validating data early prevents downstream issues Boosts ML models : Feature-rich datasets yield better predictions Unlocks new insights : Merging datasets reveals hidden patterns Practical Example: Enriching Scr