Entity resolution is a critical capability that enables organizations to understand when different data points refer to the same real-world entity, going beyond simple deduplication by considering context and relationships between data points. It's essential for large, complex datasets and can help uncover hidden patterns and relationships in data. Entity resolution matters because poor data quality costs organizations significantly, leading to missed fraud patterns, incomplete customer views, redundant marketing efforts, compliance risks, and supply chain blind spots. Successful entity resolution enables organizations to gain a deeper understanding of their data relationships and make better decisions. Real-world use cases include healthcare, where it solves the challenge of determining when different medical records represent the same patient, and retail, where it resolves product information discrepancies across e-commerce sites, inventory systems, and supplier catalogs. Core entity resolution techniques include deterministic matching, probabilistic matching, and graph-based methods, each with its strengths and limitations. To implement entity resolution effectively, organizations should focus on data quality, scale and performance, privacy and compliance, and emerging technologies that transform how we connect related entities.