Heathrow — Helping airport operations make faster, more confident decisions.
/Role
Lead Product Designer — covering Product Strategy, Enterprise UX and Data Visualisation.
/Focus
Product Strategy · Enterprise UX · Data Visualisation
/Impact
Operational concept · Executive alignment · Product vision
Heathrow wanted to explore how operational data could become easier to understand and act upon. Rather than designing another reporting dashboard, the ambition was to rethink how Power BI could support operational decision making across one of the world's busiest airports.
Working closely with stakeholders, I explored how complex operational data could be transformed into a clearer, more intuitive experience that helped teams move from interpreting information to taking action.
Airport operations generate enormous amounts of live information.
Passenger flow, baggage, security, stand allocation and terminal activity all exist across multiple systems, making it difficult for operational teams to build a complete picture of what is happening.
Traditional dashboards presented large amounts of data, but still relied on people connecting the dots themselves.
The opportunity wasn't simply to visualise more information. It was to help people make better operational decisions with greater confidence.
Pre-exploration system revealing cognitive load and limited spatial context for operational decision-making.
Reduced operational effort — less time required to understand what's happening.
Faster problem identification — teams spot issues before they escalate.
Confident decisions — quicker, more assured operational decision-making.
Beyond traditional reporting — demonstrating what enterprise dashboards could become.
Enterprise dashboards shouldn't just report the past. They should help people decide what to do next.
Operational teams weren't thinking in databases or reports. They were thinking about flights, passengers, baggage and disruption.
I restructured the experience around the real workflows people followed every day, helping users understand operational context rather than navigate disconnected datasets.
Operational workflow: alert to action. Designing around how teams actually think, not how the data is structured.
Early workflow mapping used to align operational journeys before exploring interface concepts.
Raw data doesn't create situational awareness. Hierarchy, spatial context and visual emphasis do.
I designed concepts that helped teams see what mattered most at a glance — moving from reporting what happened to showing what needed attention right now.
Concepts exploring how operational data could become easier to interpret through clearer hierarchy and spatial awareness.
Rather than writing specifications, I built lightweight concept prototypes to make the future feel tangible.
This gave stakeholders something to react to, helped surface assumptions early, and created a shared reference point for what the product could become.
3D geospatial prototype embedded within Power BI, visualising stand-level baggage performance in real time.
Operational teams had a clearer, more intuitive way to engage with live data — reducing the mental effort required to understand what was happening and what needed attention.
Heathrow gained a tangible product vision — one they could take to leadership, align around, and use to inform future investment decisions in their data and analytics capabilities.
The process demonstrated the value of working from operational reality outward — and gave the broader team a reusable pattern for approaching complex enterprise data challenges.
Open discussion on the process of translating operational complexity into product clarity — and what it takes to align stakeholders around a future vision.
What made this engagement different wasn't the technology — it was the framing. Starting from how people actually think and work, rather than what data is available, changed everything about the design direction.
Enterprise BI is often treated as a reporting problem. This project was a reminder that it's really a decision-making problem. The goal isn't to surface more information — it's to reduce the work required to act on it.
Good enterprise software doesn't show everything. It highlights what matters most.