Pattern matching is the practice of detecting specific structures, sequences, or connections within data. It involves searching for meaningful patterns — whether shapes, trends, or recurring relationships — within raw or semi-structured data. Pattern matching helps make sense of data by identifying recurring structures and uncovering insights by exploring unknown or emergent patterns. This technique requires knowing the patterns in advance and is typically exact, requiring a match to be either present or absent. It can be used to find answers in data that address business problems, such as optimizing supply chains, detecting fraud, personalizing product recommendations, and analyzing customer behavior. Pattern matching is particularly effective in graph databases, where it allows for real-time analytics directly on live operational data with minimal performance impact. Cypher, a declarative query language designed specifically for graph databases, makes it easier to express patterns that span multiple hops or involve optional paths, improving readability and performance compared to SQL. With pattern matching, you can quickly find the solution to complex business questions by defining and detecting structures directly in your data.