Probabilistic record linkage still matters because identity data is messy and match decisions carry real financial and compliance risk. This article explains the intuition behind Fellegi–Sunter and Bayesian record linkage, shows how they control false merges and splits across noisy customer and product records, and points to modern tools and books that help you put these ideas into practice.

Pairwise matching treats each comparison as a one-off. A persistent knowledge graph turns product mentions, manufacturers, model numbers, attributes, and price bins into typed nodes and relations. Matching becomes neighborhood comparison: multi-hop paths (convergent evidence) can beat any single similarity score.

While most organizations chase the latest AI trends, data leaders are building something different: reliable foundations. This isn’t about deploying more agents faster—it’s about assets with lineage, harmonization rules, and semantic definitions that make every AI decision trustworthy. Discover why speed without discipline turns into liability, and how to fund the running back while the quarterback steals the headlines.