Insurers have a ton of insurance analytics at their disposal: policy records, claims histories, provider notes and more. Still, insurance leaders struggle to make confident, data-driven decisions in real time. Sixty-eight percent of enterprise data goes completely untapped, wasting resources on cloud storage and missing out on the gold mine of information.
The problem isn’t a lack of data. It’s turning data into actionable insights that drive business results. Instead of letting data collect dust in the cloud, turn insurance data into your most strategic advantage.
While many insurers have invested significant money into data analytics, they’re overwhelmed rather than empowered by it. Disconnected tools, clunky legacy systems, and reactive data make it difficult to turn massive amounts of information into actionable insights.
Let’s unpack those two issues further.
The average insurance company uses multiple analytics platforms, business intelligence tools, reporting systems, client portals and other tools. Unfortunately, many of them don’t integrate at all, and information technology (IT) or business operations teams spend months creating reports that are already outdated. Reports offer no insights into future improvements, and they focus completely on what happened in the past.
Data quality and hygiene are also issues.
“The biggest challenge that I see with predictive analytics is data quality as an input,” Centric Consulting National Insurance Practice Lead Sean Neben says. “If you’ve got bad data quality and maybe bad algorithms, you can go off track quickly.”
A 2024 study found that 67 percent of North American insurance companies still rely on 20–30-year-old legacy systems to compile data. Originally, these systems worked because data volumes were manageable.
However, in a world that generates 402 million terabytes of data daily, outdated technology quickly creates silos between departments, slows down decision-making processes, and makes it nearly impossible to gain a comprehensive view of risks or efficiencies.
Companies are smart to invest in data analytics, but it’s tough to prove the return on investment (ROI) when data goes unused because of disconnected tools or legacy systems. Below are some of the biggest areas where usable insurance analytics makes a dramatic impact immediately.
Analytics can improve many aspects of insurance operations, but just three areas offer the most immediate and measurable returns: underwriting, claims processing and operations.
Predictive analytics help automate low-risk policies but also flag high-risk cases. Instead of manually reviewing every application, predictive analytics can:
For example, imagine a regional property insurer implements predictive underwriting models. By looking at historical claims, weather patterns, and properties, they can reduce application processing time from five days to under two hours.
Claims processing has enormous potential for analytics-driven upgrades. Modern platforms can analyze different insurance areas (medical records, invoices and claim patterns) to:
“You need to look at every medical record and invoice as they come in the door and compare them against trends to identify what’s out of whack,” Neben says. “One of the real breakthroughs has been around analyzing medical records because there wasn’t a lot of this technology available five years ago.”
Using analytics in operations enables insurance companies to optimize staffing, identify workflow inefficiencies, and enhance customer service.
Key applications include:
Understanding what analytics can accomplish is one thing, but implementing systems that drive daily decisions is another. Here’s how successful insurers make the transition.
The most effective platforms are simple to use, house everything needed without needing to switch between multiple systems, and offer user support. They plug into existing workflows and provide real-time insights.
“Modern technology allows you to be agile,” Neben says. “If you look at Microsoft Fabric, as an example, it allows me to be agile because the data engineer can roll up all of those skills into a single individual or a small team that can get things done much quicker.”
Traditional reporting shows what happened after the fact. Smart analytics platforms send alerts when action is needed. Examples include:
Many analytics initiatives fail because they focus on measuring IT metrics rather than business outcomes. Focus on key performance indicators (KPIs) that directly impact profitability and growth, such as:
Successful implementations start with a focused pilot project that delivers measurable results within 90 days. Choose a high-impact but low-complexity project that can provide results quickly. Starting small doesn’t mean thinking small — it’s a way to bring big-picture thinking into focus.
Now that you understand how to get started, let’s look at common mistakes to avoid when implementing a data analytics solution in the insurance industry.
Neben encourages companies to focus on what matters in the pursuit of using smarter data analytics: “Don’t overthink it. It doesn’t need to be perfect. The more time you spend planning things, the further behind you’re going to get.”
Many organizations become caught up in wanting to capture and analyze every possible data point or every possible scenario. Instead, focus on the specific insights needed to improve key business outcomes.
Take a look at these three common mistakes:
Don’t be tempted to build or buy a comprehensive platform that attempts to solve every possible analytics need.
“There are some shrink-wrapped data solutions out there, but they don’t work because data reflects the architecture and terminology of the business,” Neben says.
Instead, look for solutions that provide proven components while allowing customization for your specific needs.
When it comes to implementing new technology, the platform is just one piece of the puzzle.
“Change management around getting people to use models is critical,” Neben says. “You can tell an underwriter that the model is 90 percent accurate, better than most underwriters, but every underwriter is going to think they’re the 10 percent where they know better than the model.”
Technology alone doesn’t drive results. Instead, it helps people do just that. Invest in team training that creates a pathway to making the data work for them, and address change management early and often.
Insurance companies that successfully turn data into decisions share several characteristics. They start with clear business needs, choose technology that works with their existing workflows, and focus on how to use the information rather than seeking IT perfection.
Unlike large vendors or off-the-shelf platforms, Centric Consulting is rooted in industry expertise, flexibility and partnership. Centric’s Insurance Analytics Platform brings balance with its prebuilt, proven components that don’t limit your business into a one-size-fits-all tool.
Ready to transform your insurance analytics from reporting to decision-making? The time is now to take hold of your data, audit your current analytics, and proactively plan for the future.
Our Insurance team is uniquely positioned to provide you with a combination of pragmatic solutions and services focusing on achieving your objectives. We’ll put it all together so you can avoid the common pitfalls, such as disjointed, unconnected solutions. Contact us
