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Last year, we talked about how advanced analytics is changing insurance—how it’s giving companies a deeper look into risks, customer behavior, and claims management. But as anyone in this field knows, having the tools and data isn’t the same as making them work.

That’s where the real challenge begins.

The Reality of Implementation

A lot of insurance companies have jumped on the analytics bandwagon, investing in sophisticated platforms and hiring data scientists. But here’s the thing—just because you buy the best tools doesn’t mean you’ll get the best results. It’s like buying a high-end espresso machine and expecting it to make great coffee without knowing how to use it.

One of the biggest struggles is data quality. Insurance data is messy. Policies written decades ago may have different formats than those written today. Claims notes might be filled with shorthand, misspellings, and inconsistent terminology, the inconsistency oftentimes favoring the insurer. Before analytics can deliver any value, someone has to clean up that mess.

Then there’s data integration. Most insurers have multiple systems—one for policies, another for claims, another for customer interactions. If those systems don’t talk to each other, good luck getting a complete picture. Companies spend millions on analytics, but without proper data integration, it’s like trying to put together a puzzle with missing pieces.

And let’s talk about expectations. Senior executives often expect analytics to be a magic bullet. They want instant insights and automated decision-making, but they don’t always understand the complexity behind it. Analysts get asked, “Tell us what we don’t know” or “Predict next year’s claims trends” without the right data to back it up.

We’ve seen situations where businesses push for machine learning models before they even have reliable historical data to train them on.

It’s Not Just a Tech Problem

Even when the technology works, the human factor comes into play. Underwriters, adjusters, and agents have years—sometimes decades—of experience making decisions based on gut instinct. Introducing analytics can feel like stepping on their toes.

Analytics models can flag high-risk customers, but experienced agents can override them based on their intuition. Sometimes, they can be right. Other times, they aren’t. The key challenge is getting teams to trust the numbers without making them feel like their expertise is being replaced.

Another big challenge? Vendor hype. Much like those who rushed to adopt the printing press without understanding its full potential, many companies buy into analytics without fully understanding how to use it. Vendors promise the world—automated fraud detection, real-time risk analysis, AI-driven underwriting—but they don’t always explain what it takes to get there. Companies end up with expensive tools that sit idle because they don’t have the right internal expertise to use them effectively.

So, What’s the Way Forward?

The good news is that none of these problems are unsolvable. Companies that succeed in advanced analytics don’t just invest in technology; they invest in people and processes.

First, get the data right. Before jumping into predictive modeling, companies need a solid data foundation. That means cleaning up legacy data, standardizing formats, and making sure systems are properly integrated. Think of it like building a house—you wouldn’t install fancy windows before laying a solid foundation.

Second, set realistic expectations. Advanced analytics isn’t a magic wand. It takes time to build reliable models, test them, and refine them. Leadership needs to understand that insights don’t come overnight, and data science teams need to communicate clearly about what’s possible and what’s not.

Third, bring people along for the ride. Analytics should support human decision-making, not replace it. The best companies invest in training so their teams understand how to use data-driven insights effectively.

Lastly, don’t buy into the hype without a plan. Advanced analytics works best when companies take a thoughtful, step-by-step approach. Start with smaller, high-impact projects—like improving claims fraud detection or streamlining underwriting processes—before scaling up to more complex initiatives.

The Bottom Line

Insurance companies have more data at their fingertips than ever before, but turning that data into real business value takes work.

The challenges are real—messy data, disconnected systems, cultural resistance, and unrealistic expectations. But with the right strategy, those challenges aren’t roadblocks; they’re just part of the journey.

Last year, we talked about the promise of advanced analytics in insurance. This year, we’re acknowledging the roadblocks—but also showing that with the right approach, those roadblocks can be overcome.

The companies that figure this out won’t just have better models; they’ll have better businesses. And in an industry built on managing risk, that’s what really matters.