The University of Deusto needed a redesigned enrollment dashboard to support decision-making across the institution. The project scope was limited to the enrollment process covering reservations and matriculation across four phases (ordinary and extraordinary). The design was developed based on input from the team responsible for the existing dashboards, without direct research with end users, and proposed a model for transforming raw enrollment data into clear, actionable diagnostics for different university roles.

01 Challenge

How might we adapt an enrollment dashboard to serve the different decision-making needs of multiple user profiles across the university, while balancing sophistication with ease of use?

The goal was to create clear rules that would allow the dashboard to grow sustainably — in structure, look and feel, and navigation — without becoming overly complex or hard to maintain.

02 Core Problem

The university has several profiles with different relationships to the enrollment process. Some focused on managing day-to-day registration, others on academic planning and strategy. Each group needed different diagnostics from the same underlying data, but there was no shared framework for deciding what information to show, to whom, and at what level of detail (university, faculty, or degree program).

03 My Role

Service Designer

I led the definition of the model and principles behind the new dashboard, working alongside another researcher and a UX/UI designer. This included mapping user archetypes and their information needs, establishing a Data - Controller - View model to separate data governance from interface decisions, defining navigation rules across two main views (in-process tracking and post-enrollment analysis) and three depth levels (University, Faculty, Degree), and creating a decision tree to guide where any new piece of content should live within the dashboard. Together, we produced low-fidelity wireframes and a navigable prototype to bring these rules to life.

04 Impact

The engagement established a clear foundation for the dashboard's design and growth:

Shared model Introduced a Data-Controller-View framework that separates raw data from how it's diagnosed and displayed, making the system easier to maintain and extend.

User-aligned structure Mapped university roles to their specific diagnostic needs, ensuring the dashboard speaks to academic planning and enrollment management audiences differently.

Navigation framework Defined two core views with three levels of depth (University, Faculty, Degree) and consistent filtering by student attributes.

Decision-making tool Delivered a decision tree that gives the team a repeatable method for placing future content correctly within the dashboard.

05 Key Takeaways

Diagnosis over data Showing numbers isn't enough. Every piece of data needs to be translated into something that helps someone make a decision.

Consistency builds trust Applying the same patterns to the same types of problems throughout the dashboard reduces the learning curve and increases user confidence.

Separating data from display Decoupling how data is governed from how it's visualized makes the system more scalable and easier to evolve over time.

06 Research & Framing

We structured the work around understanding three interconnected elements: the people who would consult the dashboard, the structure of the enrollment process itself, and the university's academic offering and organizational hierarchy. This led to a layered model — University, Faculty, Degree, Group — that mirrors how the institution is actually organized, and a two-view structure that separates real-time process tracking from deeper post-enrollment analysis.

07 Key Insights

Diagnosis needs a question Every piece of data on the dashboard should answer a specific need (who needs it, when, and what decision it supports) rather than being shown just because it's available.

Two rhythms of analysis During enrollment, users need fast, operational answers (are we on pace?). After enrollment, the focus shifts to slower, strategic reflection (how did we do, and what should change?).

Diagnosis has layers A useful diagnosis often requires comparing a result against itself (trend over time), against the institution's broader goals, and against the external market.