How to make sense of your product data with an evidence map

Product teams often struggle to make sense of vast amounts of qualitative and quantitative data collected during assumption tests, hindering clear decision-making and resource allocation. Evidence maps, though commonly used in academic research and public policy, offer a valuable solution for product management by consolidating disparate data into a cohesive overview, enabling teams to better understand what they've learned and decide on next steps. Evidence maps organize assumptions into dedicated spaces for "soft" evidence from qualitative tests and "hard" evidence from real-world data, providing a structured approach to analyzing assumptions such as user interface usability and product market fit. The process involves distinguishing between types of evidence, linking findings to business metrics, and extracting key insights, sometimes with the aid of large language models, to inform decisions about product iterations or further data collection. This method ultimately helps teams discern between what they know and what remains uncertain, guiding strategic planning and product development.

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