We all talk about being data-driven - but how often do we ask who’s driving the data? Every research brief, survey design, and interpretation involves judgement calls. Even “objective” analysis is shaped by the questions we choose to ask (and the ones we don’t). The real craft of insight lies in how we choose to frame and interpret it - acknowledging our own filters, not pretending they don’t exist. In the end, good insight isn’t free from bias. It’s just bias that’s been made visible.
The Bias in Data-Driven Insights: A Craft of Framing and Interpreting
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In research I see several reasons why doing the surveys: test hypothesis to learn and distribute new insights, phrase questions so that the results prove your point of view (or your clients view). This presumes an understanding of the context and we humans still have the edge here. However, when models will better contextualize situations and specific environments, then we are doomed. Or what is your view?