Three Data Problems Costing Carriers Billions
And why 2026 is the year to fix them
By Near Space Labs’ CRO, Matthew Tucker
2025 gave me and my sales team at Near Space Labs a front-row seat to the entire insurance industry. We walked the floors of ITC, sat in on partner strategy sessions, and took calls with prospects across the market. The sheer range of voices we heard at Near Space this year was staggering, yet the same three themes kept surfacing everywhere. While the overarching theme is that this is one of the most exciting and innovative times ever to be in the insurance industry, sub themes stemming from this idea are worth paying attention to. As we head into 2026, these patterns reveal fundamental shifts in how carriers approach property data, and they’re impossible to ignore.
1. The Hidden Cost of “Good Enough” Imagery
Here’s an uncomfortable truth: the insurance industry is suffering from an unintentional but collective case of complacency around imagery recency.
Carriers are making billion-dollar underwriting decisions based on imagery that’s typically six months old or older. When pressed, most will admit they know their data isn’t current, but here’s the catch: since nobody has a significant advantage in data recency, there’s an industry-wide acceptance that what we have is “good enough.”
Except it’s not.
Every three weeks, a billion-dollar climate disaster occurs. Loss ratios are higher than they’ve ever been. Carriers are struggling with profitability. Yet somehow, the connection between outdated property data and inadequate pricing isn’t being made. No one is being held accountable for ensuring that the imagery feeding their underwriting workflows, and increasingly their AI models, is actually current.
The real challenge is that making the jump to demand recency requires someone to stick their neck out first. Insurance is a risk-averse industry, and until a competitor gains an unfair advantage through better data, most carriers will continue operating in the “good enough” zone.
The problem? By the time that competitive pressure hits, it may be too late.
2. Coverage Blind Spots: The $36 Billion Opportunity
While the industry debates AI and digital transformation, millions of American homeowners are being systematically underserved due to coverage blind spots. The numbers are staggering: approximately $36 billion in missed underwriting opportunities, translating to roughly 6.1 million underserved quotations per year.
This represents both a business problem and a matter of equitable access to insurance coverage.
Here’s why these blind spots exist: The legacy airplane-based imagery model that insurers historically rely on simply doesn’t work economically for properties in less densely populated areas. Flight paths are optimized for dense urban and suburban zones where the per-property cost makes sense. Properties outside these zones are left with outdated data, limited coverage options, or both.
But these aren’t just “rural” areas that carriers can afford to ignore, which would already be a problematic calculation in itself. These are expanding risk zones at the urban-wildland interface, areas where climate exposure is growing exponentially. Wildfires, flooding, and severe weather don’t stop at city limits. In fact, some of the fastest-growing risk is in these traditionally underserved markets.
Major carriers and analytics providers all recognize that this gap exists. What they need is a data provider who can help them close it.
3. Imagery Licensing is Undergoing a Value Shift
This one catches most people off guard, but it may be the most important issue of the three.
As the industry races toward AI-powered underwriting, a significant shift is occurring in how imagery licensing works. The distinction between licensing rights and ownership rights matters more than ever.
Here’s what’s important to understand: in insurance land, no one owns imagery. Companies license it. And the terms of those licenses are evolving rapidly as the market changes.
Traditional imagery providers are pivoting their business models. Many have shifted from pure data collection to building their own analytics products, essentially moving up the value chain to compete in the same markets as their customers.
This full-stack approach makes business sense for those providers, but it creates implications for licensing terms. Want to build your own roof condition model using AI? The flexibility to do so may vary significantly depending on your image provider and contract terms.
For carriers and analytics companies, this creates an existential workflow consideration. Imagine you’re running underwriting processes that depend on specific image-derived data. As contracts evolve and providers’ business models shift, it’s worth understanding exactly what flexibility you have (and will continue to have) to use your licensed imagery data.
The industry needs great products to help mitigate and understand risk. Imagery is a critical part of the data workflow. Many businesses have built proprietary capabilities using imagery. As this market evolves, carriers should verify their continued ability to license imagery in the ways their workflows require, because the landscape is changing quickly.
The Path Forward
These three issues (recency complacency, blind spot coverage gaps, and licensing restrictions) aren’t going away. They’re accelerating as AI adoption forces the industry to confront the quality and flexibility of its foundational data.
The carriers and analytics companies who recognize these challenges now and take action will have a significant advantage over those waiting for the market to force their hand. Because in insurance, being reactive to data problems means missing key opportunities for optimal profitability.
At Near Space Labs, we’re providing the foundation for carriers and analytics companies to build proprietary advantages through better, fresher, more accessible data, with complete freedom to use it however their business demands.
The industry, led by Near Space Labs will inevitably address these issues. The critical question is who will move fast enough to turn them into competitive advantages.