Strategies for Managing Data Freshness in Dovetail Research
Curious to learn how people tackle the challenge of data "freshness" when working with Dovetail over months/years, especially when mixing both generative/foundational research (which may have a longer shelf life) and evaluative product research (likely a short shelf life). Does anyone have a setup that allows them to distinguish between "archival" data/insights connected to a past product decision and "current" data/insights that should be considered moving forward? What rituals/tasks do you have in place to evaluate data and insight relevance and what do you do with out-of-date/irrelevant materials?