In Computer Science, we measure runtime and memory with precision - yet when it comes to course and program quality, our data is often scattered and incomplete. Grade distributions, student feedback, enrollment data, and academic integrity cases live in separate systems, leaving teaching teams without a holistic picture of how a course or program is performing or where it needs attention.

To address this, we developed an Education Quality Dashboard for CS educators. Unlike generic dashboards, it integrates data directly relevant to CS teaching: course change history, student feedback, grade distributions, special consideration data and academic integrity patterns. This comprehensive view enables teaching teams to make data-informed decisions across both courses and programs.

We designed the dashboard for three key use cases:

• Course (re)design: ensuring curriculum and assessment changes are informed by a comprehensive view of course data.
• End-of-semester reviews: focusing discussions on trends, gaps, and the actions or resources required for improvement.
• Course handovers: reducing reliance on informal knowledge transfer when teaching teams change.

Over the past year, the dashboard has been used in end-of-semester review meetings across 100 CS courses. Staff reported that discussions became more focused, led to clearer actions, and required less preparation time. At the same time, challenges emerged around interpreting diverse data sources.

In this lightning talk, we will share our design decisions, lessons learned, and how we streamlined scattered data into actionable insights. We will also consider the broader potential of dashboards as “compilers” of course quality in computing education.