Grouping related findings relies on sorting many observations into natural clusters based on similarity, so patterns and themes emerge rather than focusing on individual items. An affinity diagram is designed precisely for this: you write each finding on a separate note and work with others to place notes into groups that share a common theme, without imposing predefined categories. Once the clusters form, you label each group to capture its essence, which helps reveal overarching patterns, reduce duplication, and align stakeholders on the main insights. This approach is especially helpful during requirements gathering and user research to surface themes from raw data. Other diagrams serve different purposes. A fishbone diagram organizes potential causes into branches for root-cause analysis. A data flow diagram maps how information moves through a system, focusing on processes and data stores. A mind map organizes ideas around a central concept with branches showing relationships, which can help explore connections but isn’t primarily about systematically clustering a large set of findings into unlabeled groups.