Believe it or not, analyzing seemingly unrelated data can reveal hidden truths. Take the Pentagon, the nerve center of the U.S. military. While classified briefings and high-level meetings happen behind closed doors, open-source data can offer clues about what might be brewing. Here’s where things get interesting. We can use Google Trends data to track searches for “Pentagon pizza delivery” and nearby “gay bars.” Why pizza and bars? Increased late-night activity might indicate longer work hours for Pentagon staff, potentially signifying preparation for a major event.

This is SenseMaker in its most simple form, usually structured to have an open (non-hypothesis) question (commonly referred to as a ‘prompting question’) to collect a micro-narrative at the start. This is then followed by a range of triads (triangles), dyads (sliders), stones canvases, free text questions and multiple choice questions. The reason or value for using Sensemaker: Open free text questions are used at the beginning as a way of scanning for diversity of narratives and experiences. This is a way to remain open to ‘unknown unknowns’. The narrative is then followed by signifier questions that allow the respondent to add layers of meaning and codification to the narrative (or experience) in order to allow for mixed methods analysis, to map and explore patterns.

Every research paper tells a story, but the pressure to provide ‘clean’ narratives is harmful for the scientific endeavour.

Summary: Overreliance on narrative details and assumptions about cause-and-effect explanations can lead to errors in judgment by UX practitioners.