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Data Science Introduction
/Data Preparation
/Data Integration

Data Integration

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Data Integration

Data integration is like assembling puzzle pieces from different boxes to create one complete picture. It's when you combine data from various sources—CRM systems, website analytics, financial databases—into a single, coherent dataset. The challenge? These systems weren't designed to work together. They use different formats, naming conventions, and structures, making it like trying to fit Lego and Duplo blocks together. The first step is mapping these differences and finding the right 'keys' that connect related records across systems.

One of the trickiest parts is entity resolution—figuring out when 'John Smith' in one database and 'J. Smith' in another are actually the same person. Without perfect IDs, you need smart matching algorithms that weigh different clues to make these connections. When sources disagree (like different addresses for the same customer), you need rules to decide which one to trust. Modern ETL (Extract-Transform-Load) tools help automate this process, creating repeatable workflows that keep your data fresh and consistent. When done right, integration reveals insights that would stay hidden in isolated data silos—like how website behavior connects to in-store purchases.