A department chair at my university emailed me last October: "Can you build me a dashboard that shows enrollment trends by program?" I said sure. I asked three follow-up questions. By the fourth question, we had a completely different project. She did not want enrollment trends. She wanted to know which programs to cut before the provost asked. The dashboard was a shield. The question behind the question was: help me make a decision I am afraid to make, and give me something to point to when someone challenges it. I have fielded roughly 1,200 data requests across 10 departments in the past three years. Financial aid, facilities, registrar, enrollment, advancement. People whose entire careers are built on institutional knowledge, not data literacy. The pattern is always the same: they ask for a thing (a report, a dashboard, a number), and the thing they ask for is almost never the thing they need. We teach SQL. We teach Tableau. We teach Python. Nobody teaches the 10-minute conversation that decides whether the next 40 hours of analyst work produces something useful or something that gets opened twice and never again.
A department chair at my university emailed me last October: "Can you build me a dashboard that shows enrollment trends by program?" I said sure. I asked three follow-up questions. By the fourth question, we had a completely different project. She did not want enrollment trends. She wanted to know which programs to cut before the provost asked. The dashboard was a shield. The question behind the question was: help me make a decision I am afraid to make, and give me something to point to when someone challenges it. I have fielded roughly 1,200 data requests across 10 departments in the past three years. Financial aid, facilities, registrar, enrollment, advancement. People whose entire careers are built on institutional knowledge, not data literacy. The pattern is always the same: they ask for a thing (a report, a dashboard, a number), and the thing they ask for is almost never the thing they need. We teach SQL. We teach Tableau. We teach Python. Nobody teaches the 10-minute conversation that decides whether the next 40 hours of analyst work produces something useful or something that gets opened twice and never again.

Sumaiya Shrabony is a Technical Program Manager and BI practitioner at the University of Colorado Denver, where she manages Enterprise Analytics Infrastructure and AI Adoption.Her work sits in the gap most AI talks skip: what happens after a tool looks good in a demo and has to survive real workflows, governance constraints, anxious stakeholders, broken metrics, and users who did not sign up for AI. She writes Ground Truth, a weekly newsletter that tests AI tools against practical enterprise data work and reports the adoption barriers stopping them from landing.She moved to the US from Bangladesh at 19 and built her technical career without inherited defaults, which now shapes how she teaches: specific, direct, and built for people who need the work to make sense by Monday morning.