How airports should prioritise data projects: A tried-and-tested framework

For airports at different stages of data maturity, one of the most challenging questions is: where do we start? Should you focus on the areas with the biggest commercial impact, the easiest technical wins, or the data that’s causing the most operational pain? 

In our webinar, The Data Strategy Blueprint: Laying the Foundations for Commercial Growth, Paris Bielby, Data Director at CAVU, and Jamal Karim, Lead Data Engineer at CAVU, shared a practical prioritisation framework geared towards helping airports make smarter decisions about which data silos to tackle first, and how to sequence work for maximum impact. 

CAVU: For airports at different stages of maturity, how do you prioritise which data silos to tackle first? 

Paris: 

“When you work with airports at very different stages of data maturity, the key is not to chase a single ‘right’ starting point; it’s to apply a consistent prioritisation framework that balances value, feasibility, and urgency. 

I usually look at three dimensions: 

1. Commercial Value

Where can data unlock the biggest financial impact — whether that’s improving retail conversion, optimising parking yield, or reducing leakage in airline billing? High‑value domains deserve attention, but only if the data foundations are strong enough to support meaningful insight. 

2. Operational Pain

Some silos create daily friction: inaccurate flight data, inconsistent passenger numbers, or unreliable asset information. These are the areas where fixing the data unlocks immediate operational stability and builds trust in the wider programme. 

3. Technical Readiness

Sometimes the smartest move is to start where the data is cleanest, most accessible, or easiest to integrate. Early wins build momentum, demonstrate credibility, and help secure buy‑in for the harder work that follows. 

The art is in balancing all three, not treating them as competing priorities. A high‑value domain that is technically impossible to fix today shouldn’t be first. Equally, an easy win with no business impact doesn’tmove the needle. 

A framework that works well is a Value–Feasibility Matrix: 

Value: commercial impact + operational impact 

Feasibility: data quality + system accessibility + stakeholder readiness 

Plot each data domain on that matrix. The top‑right quadrant (high value, high feasibility) becomes your starting point. The bottom‑right quadrant (high value, low feasibility) becomes your roadmap for investment. And the low‑value areas fall away naturally. 

To illustrate this with an example: one product that we have recently built and integrated into our software platform, Propel, is Travel Insurance, which is sold for a suite of client sizes, from very small to multinational. 

This means that we can help all our clients grow whilst considering the ways that we can report this into their data infrastructure safely and securely.” 

CAVU: Can you explain how this prioritisation framework was used to integrate travel insurance into Propel? 

Jamal: 

“From a delivery and engineering perspective, our focus was very much on prioritisation and sequencing, not trying to solve everything at once, but breaking the work into clear phases and creating a roadmap that would get the product live as quickly and safely as possible. 

We started by mapping exactly where the data needed for the Travel Insurance product was coming from: customer platforms, order systems, payment flows, and partner integrations. This helped us to understand what data was essential for the product to function versus what data was important for longer-term reporting and optimisation. That allowed us to split the work into clear streams and prioritise what was critical to bring the product online. 

The priority was enabling the core product flow: ingesting customer orders, validating transactions, and making sure we could provide the financial and operational data required by our insurance partner. That data had to be accurate, timely, and structured in a way that met partner requirements, because without that, the product couldn’t operate. 

Quality and governance were built into every step of this process. We implemented automated checks on the data being sent to partners to ensure completeness, accuracy, and consistency. We also designed governance processes so that every order is auditable, meaning we can track, trace, and reconcile all transactions end-to-end. This gives both internal teams and partners confidence in the integrity of the data being exchanged. 

Once that foundation was in place, we layered in reporting, ensuring our stakeholders from teams such as finance had access to reports that they required – giving them details of the performance of the product. 

So the prioritisation wasn’t abstract, it was very practical: first, enable the product to operate; second, ensure partner trust and financial integrity; and third, build the reporting and optimisation layers. That sequencing allowed us to deliver value quickly, while still building a robust, governed data foundation that the product can scale on.” 

CAVU: What happens when data prioritisation goes wrong? 

Paris: 

“When data prioritisation goes wrong, it usually shows up in one of three ways. You spend months working on something technically complex that delivers little business value. Or you chase a high-value opportunity, but the data quality or systems aren’t ready, so the project stalls or delivers inaccurate results. 

In the worst case, you try to tackle everything simultaneously, spread your team too thin, and nothing gets finished properly. 

The framework we use prevents that by forcing honest conversations upfront. It makes trade-offs visible and helps everyone understand why certain work is happening now versus later. That shared understanding is just as important as the technical execution.” 

Watch the full webinar and gain invaluable data expertise

This blog shares just part of Paris and Jamal’s conversation on how airports can take the first steps towards becoming data-driven. 

In the full webinar, they also discuss: 

• How to build data governance frameworks that enable faster decision-making
• Navigating commercial sensitivities when sharing partner data
• What creates a truly data-driven culture in airport environments
• The most common pitfalls mature airports should avoid as they scale