Distress Signal: How Bad Data Costs Airlines Billions—And What They’re Doing About it

It’s no secret that airlines are in a profitability crisis. While some of that deficit can be attributed to market factors like oil prices or consumer demand, we can’t underestimate the profound  impact of operational headwinds.

Each day, airlines across the globe lose approximately $203 million to operational inefficiencies. But while the leakage may be operational, the root cause is not. Beneath the surface of all those operational snafus lies a far more insidious offender—bad data. And it’s not going to leave quietly.

In this article, we’ll take a look at some of the most common operational issues impacting aviation and how major airlines are leveraging new data quality practices to solve them. But first—a look at the numbers.

Revenue Leakage by the Numbers

From booking to billing, fragmented and inaccurate data is quietly draining the aviation industry of billions in the form of revenue leakage. In fact, revenue leakage affects roughly 3 to 5 percent of airline revenue globally. For major carriers, this translates into some pretty significant financial impact.

Here’s a look at the data from four of the world’s most prolific airlines (anonymized for compliance):

• Airline 1: $1.6 billion – $2.7 billion
• Airline 2: $432 million – $720 million
• Airline 3: $278 million – $463 million
• Airline 4: $66 million – $110 million

At the heart of this profitability struggle is a growing data quality crisis that’s been quietly undermining the reliability of some of the airline industry’s most foundational operations.

This isn’t a peripheral issue. It’s a structural flaw in how data is monitored, managed, and governed within data + AI ecosystems—and it’s wreaking havoc on bottomlines.

Let’s consider some of the most common operational pitfalls. 

How Does Bad Data Impact Airlines?

When airline data goes bad, it can manifest in all kinds of expensive downstream issues. Some of the most common ways bad data will impact revenue for airlines include:

Booking Errors—incorrect fare codes, passenger names, or flight details that can result in lost revenue or added operational costs in the form of compensation or rebooking.

Ticketing Errors—inaccurate applications of taxes, fees, or discounts that would lead to a customer being undercharged or overcharged. 

Billing Errors—delayed or erroneous invoices, incorrect partner settlements, or currency conversion mistakes.

Fare Discrepancies—inconsistent pricing across distribution channels or agent mistakes result in customers being undercharged or overcompensated.

No-shows and Overbooking
Poor forecasting and unreliable data on bookings can lead to costly overbookings or missed revenue on empty seats.

Loyalty Program Issues
Faulty data in loyalty systems—such as over-redemption or duplicate records—undermines customer trust and creates accounting liabilities.

Any one of these issues could wreak havoc on your margins—to say nothing of the loss of customer trust that’s undoubtedly to follow.

These aren’t isolated issues. They’re persistent errors rooted in a chronically fragmented data estate that’s underpinned by a lack of sufficient visibility across domains. 

Understanding the Data Trust Deficit

For the last 15 years or so, data quality has been a discipline within data teams to one degree or another. But as airline leaders have begun to embrace more automation within their operational workflows, the scope of the data quality problem is coming into view—and it doesn’t look good.

Data and AI leaders need to solve for three primary challenges: 

1. Facilitate data + AI trust
Customers won’t use what they don’t trust—and if customers won’t use it, stakeholders can’t deliver value from it. So, the first hurdle to creating trust is creating transparency. 

Despite generating vast amounts of data, airlines often struggle with siloed departments and disjointed data infrastructure. 

What’s more, AI/ML investments require an even stronger data foundation before they’ll be adopted for tasks like predictive maintenance, route optimization, or forecasting. 

2. Mitigate financial and reputational risks
When data goes bad, it isn’t just an operational risk—it has real financial and reputational consequences. Revenue leakage of just 3–5% annually can mean billions for major airlines; while operational disruptions add up to ~8% of total global revenue.

And that’s not even counting the massive reputational and regulatory risks of something like a  customer data breach. This is particularly common for things like loyalty programs where point tracking and customer records require precise data management. 

3. Improve productivity for data + AI teams
A team’s productivity will always be determined by its most manual processes. While airlines invest heavily in analytics, data teams remain siloed with tedious and limited communication with their consumers and a lack of real-time insights during disruptions. 

What’s worse, manual data correction and exception handling sap needed resources to build and fortify those critical operational workflows.

While traditional manual data quality practices (like testing or more recently model output evaluations in the case of AI tooling) can serve to address a small subset of the challenges above, they’re woefully inadequate to address the systemic challenges that make them possible in the first place—or to scale those solutions with the growth of an enterprise. 

Rethinking Data Management with Monte Carlo

As the scope and complexity of the data quality challenge expands, aviation leaders have begun looking to modern end-to-end solutions that embrace scalable automation to address these challenges. 

Unlike the reactive data quality processes of old, data + AI observability solutions like Monte Carlo empower data leaders and teams within aviation to proactively detect, manage, and resolve data quality issues before they impact operations.

So, what does that look like? Here’s how some of the biggest names in aviation are leveraging data + AI observability for their stakeholders: 

Revenue Protection & Assurance
The most common, critical, and instantly justifiable use case for a modern approach to data quality is in the management and maintenance of revenue operations. Monte Carlo use data + AI observability to:

• Prevent revenue leakage
• Ensure accurate revenue recognition
• Validating partner settlement data
• Monitoring ticket data integrity
• And maximizing ancillary data through data quality monitoring

Customer Retention and Loyalty
Like we said, how a loyalty program is managed can have a profound impact on customer sentiment—and that sentiment destruction will have longtail effects on your bottomline. By eliminating erroneous data like duplicate records, incorrect tracking data, or inconsistent PI, Monte Carlo ensures loyalty programs operate as intended to preserve customer trust and maximize retention.

Compliance and Risk Management
In a highly regulated industry like aviation, managing compliance is non-negotiable. Modern data quality management within data + AI observability accelerates safety, regulatory compliance, audit readiness, and interoperability with external vendors and regulators.

Operational Efficiency
With powerful scalable monitoring, data + AI observability is being used by airlines to track on-time performance, prevent crew and scheduling errors, and reduce the need for manual data correction and backfilling.

Monte Carlo’s new monitoring agent will also suggest and create new monitors  for critical assets to scale health beyond previously identified business rules. 

Customer Experience
Airlines are also improving their customer experiences by creating monitors to ensure integrity for critical customer data, deliver complete voice-of-the-customer data, and manage customer consent and preferences.

A Path Forward

The aviation industry has long operated under tight margins and high complexity. But in a landscape increasingly shaped by automation, personalization, and global demand volatility, data quality is no longer a backend concern—it’s a strategic imperative.By shifting from reactive firefighting to proactive data monitoring, airlines can recover lost revenue, improve operations, and elevate the customer experience for travelers around the world.

Bad data might be unavoidable—but with data + AI observability covering your most critical assets, the consequences can be.

Our promise: we will show you the product.