
Four separate dashboards became one platform with five tabs, and we did it without changing the software underneath. Getting an answer went from about six minutes to two.
Before figures from the audit of the four legacy dashboards; after figures measured across six validation rounds. No abandonment, error-rate or satisfaction numbers were collected, so none are claimed.
Meridian University ran its analytics across four separate Power BI dashboards, one each for undergraduate admissions, graduate admissions, research and HR. If a dean wanted to know how the university was doing overall, they had to open all four, compare the numbers by hand, and spend about six minutes finding the answer.
I brought the four together into one Power BI platform. The software stayed the same, so all the work went into the information architecture and the rules for how things are charted. That took the time to an answer down to two minutes, and 94% of university leadership were using it within the first term.
This was not a redesign where you get to pick a new stack. The old dashboards were built in Power BI and the new ones had to ship in Power BI too. The tool was fine, the way it had been put together was the problem. Three things shaped the approach:
Before I sketched anything I ran a heuristic audit across all four of the old dashboards. What came out of it explained why people had stopped using them:
The structural pivot was moving from four isolated tools to one platform with five unified tabs: Overview, Undergraduate, Graduate, Research and HR.
I mapped the new information architecture and user flows in hand-drawn wireframes, keeping the focus on hierarchy before any visual polish.
Once the wireframes were settled I built the UI system on top of Meridian's official brand typography, with a palette chosen for reading data: navy, crimson, teal and purple.
University leaders are accountable for these numbers, so automation had to be handled with care.
Ask Meridian AI is not a separate product and not the default dashboard. It is a user-invoked modal, triggered by an Ask AI button, layered over the active canvas. An optional analytical assistant that surfaces background signals and next-best actions. It explains; it does not decide.
I ran six rounds of validation with real university stakeholders and changed the UI on what came back. One example: the Overview tab was trying to answer four separate questions, so I cut it down to one.
| Design change | Measured outcome |
|---|---|
| Overview cockpit, one question answered in a glance | 6 min → 2 min to a leadership answer |
| Global persistent filters across tabs | 7 → 2 clicks to a segmented view |
| Multi-year views by default | 1 → 5 years of context on screen |
| Official definition, source and owner on every metric | 9 → 0 disputed definitions |
| Fit-for-purpose charting rules | 11 → 0 pie charts |
Most of the work here was information architecture, and that is what turned it from something people avoided into something they open every day.
Thank you
Happy to walk through any part of this in more detail, including the decisions that did not make it.