The demand for real-time data in insurance is growing. We explore why it matters, how it’s reshaping the industry, and simple steps you can take to transform your data into real-time data. We’ll also discuss data enrichment and why data lakes are critical to modern insurance data solutions.
In brief:
- Real-time insurance data is crucial for insurers to make faster, more informed decisions and remain competitive in a rapidly evolving market.
- Insurers face challenges such as lagging insights, disconnected data silos, and rigid legacy systems that prevent timely access to critical information.
- Breaking down organizational silos and aligning business and IT around a single source of truth help your organization effectively use real-time data.
- Modern technology platforms make real-time dashboards and data feeds attainable, but the biggest obstacles are related to people, processes and company culture.
- Transitioning from legacy warehouses to data lakehouse architectures is necessary to support real-time analytics at scale in the insurance industry.
Real-time data has moved from buzzword to baseline in insurance. When claims spike after a catastrophe or commercial buyers are shopping for coverage, speed determines who wins. New products like cyber insurance also depend on live data to deliver both protection and service.
“Real-time data isn’t a luxury anymore — it’s how insurers stay competitive and responsive,” says Centric Consulting National Data and Analytics Practice Co-Lead Jeremy Gruenwald.
Luckily, the technology to enable real-time insights is no longer out of reach. With today’s platforms, you can stand up dashboards and feeds in hours. The bigger challenge lies with people and processes: breaking down silos, establishing governance, and aligning business and information technology (IT) around a single source of truth.
The Challenge With Today’s Insurance Data
You already collect vast amounts of data — policy records, claims logs, customer interactions, and more — but in many cases, you’re still trapped in yesterday’s cadence. Insurers are data rich but insight poor, held back by batch updates, siloed systems, and infrastructure built for stability, not agility. That mismatch creates three key roadblocks:
1. Lagging Insights
By the time batch-processed data reaches decision-makers, the opportunity may have passed. Consider what happens when a storm hits or when a large commercial risk is about to go to market: Every hour of delay reduces your reaction window.
2. Disconnected Silos
Underwriting, claims, billing, and product teams often maintain separate datasets and vocabularies. One unit may refer to a field as a “loss location,” while another may call it an “incident site.” Unless you align those concepts, streaming data won’t help you correlate meaningful patterns.
In fact, research shows that many insurers store critical data across 10 or more different systems, such as legacy policy administration platforms, customer relationship management (CRM) systems, rating engines, claims systems, and third-party providers, making real-time access difficult.
3. Rigid Systems and Culture
Many carriers operate on infrastructure designed for auditability rather than speed. A 2025 CIO article notes that insurers are moving from legacy warehouses to lakehouse architectures because the legacy warehouses cannot support real-time analytics at scale.
In fact, according to Pranay Shyam, Centric Consulting senior architect for our national data & analytics team, “The blocker isn’t technology. It’s culture, ownership and alignment.”
In short, it’s no longer enough to just have data. You must ensure your systems are wired to use it in real time and your organization is aligned to act accordingly. Without that alignment, you risk accelerating noise, not value.
Overcome Real-Time Data Barriers With the Right Foundation
Before you can deliver insights in real time, you need to ensure the data you’re streaming is accurate, consistent and meaningful. But laying this foundation isn’t always straightforward. Legacy systems, siloed processes, and cultural resistance can slow you down if you’re not prepared.
Here’s what you’ll need — and what often gets in the way:
1. Establish a Single Source of Truth
A single source of truth (SSOT) means your teams are working from unified records instead of fragmented versions scattered across policy, claims, and billing systems. When each system maintains its own version of “customer address” or “loss location,” it creates confusion and slows decision-making.
The challenge is most insurers still depend on decades-old core systems that weren’t designed for integration. In fact, 74 percent of insurance companies continue to rely on outdated legacy technology for core functions like pricing and underwriting. Without integration layers or cloud overlays, aligning data across systems can feel nearly impossible.
2. Strengthen Data Governance
Governance ensures your data is trusted, auditable, and consistently defined across the enterprise. It covers ownership, access, controls, and clarity of definitions — for example, making sure “loss location” means the same thing to both underwriting and claims.
However, governance efforts often stall because siloed business units — such as claims, underwriting, product and billing teams — keep their own separate vocabularies and rules. Without a shared framework, streaming data may amplify inconsistencies rather than resolve them.
3. Develop Your Data Lake Backbone
A modern data lake — or its newer evolution, the lakehouse — provides the scale and flexibility needed to support real-time data flows. A data lake centralizes structured, semistructured, and unstructured data, then validates it and distributes governed data streams across the business. Data lakes also set the stage for advanced capabilities like machine learning (ML) and process automation.
While cloud-native platforms make this more affordable than ever, leaders still hesitate to invest without a clear path to return on investment (ROI). Ongoing infrastructure and talent costs raise concerns, which is why starting with high-impact use cases is key.
4. Move From Raw Data to Enriched Data
“The difference between raw and enriched data is the difference between noise and insight. Enrichment is what makes data usable at real-time speed,” says Kris Moniz, data and analytics leader for Centric Consulting.
Raw data is only the starting point — it tells you what happened, but not always why. Enriched data adds context, such as linking claims with weather alerts, supplementing policies with external risk scores, or flagging fraud signals before they become payouts. Enrichment makes data more usable, whether you’re running batch reports or streaming in real time.
The challenge is balancing speed with accuracy. Every enrichment layer must be designed to add value without creating unacceptable lag. If enrichment slows your pipelines too much, you lose the advantage of timeliness.
5. Prioritize People and Culture
Technology isn’t the main obstacle anymore. People are.
Insurance has always been a cautious industry, and many carriers still assume real-time adoption is too complex or costly. Add to that a shortage of engineers and streaming architects, and the result is slow adoption even when the tools are ready.
Industry research shows that one in three insurers cite shortages in data and analytics skills as a major challenge to scaling their analytics use. Without the right talent and leadership support, real-time data programs stall before they start.
Change has to start at the top, with executives sponsoring initiatives and encouraging new ways of thinking. Success depends on empowering business leaders to own data decisions, aligning IT and operations, and creating space for teams to experiment with new capabilities. With leadership buy-in, cultural barriers become opportunities for transformation.
Even with a strong foundation, integrating real-time data takes time. Legacy systems, siloed teams, cautious culture, and ROI concerns don’t disappear overnight. That’s why leadership commitment and a clear plan for early wins are so important. The key is to prioritize use cases where real-time data delivers measurable value fast.
From Preparation to Payoff: Implementing Real-Time Data in Insurance
With the foundation in place and the biggest barriers addressed, the next step is deciding where to focus first and how to turn early wins into long-term business impact. Don’t launch an enterprisewide overhaul. Instead, you’ll see the best results by prioritizing high-impact use cases, setting realistic expectations, and tying every initiative back to business value.
Focus on the Right Use Cases
Not every process needs to run in real time. Focus first on areas where speed directly reduces cost or improves customer satisfaction by reducing friction in high-stakes moments, which can help you with policyholder retention. For most carriers, that means:
- Claims Triage: Routing adjusters faster during catastrophe events lowers loss adjustment expense and improves policyholder trust.
- Quoting: Providing quick, accurate quotes gives you a competitive edge, especially in commercial insurance where the first mover often wins. “In commercial lines, the first accurate quote often wins,” Gruenwald says. “Real-time access to risk signals and history helps teams respond quickly and confidently.”
- Fraud Detection: Real-time data helps you flag suspicious claims or activity before payouts occur.
Set Realistic ROI Expectations
Real-time initiatives can deliver wins in weeks, but cultural and organizational adoption takes longer. Think of ROI on two levels:
- Short-Term Wins: Faster claim assignment or improved quoting speed, measured in days or weeks.
- Long-Term Gains: Reduced leakage, higher retention, better underwriting accuracy — benefits that build over months or years.
Cloud-native platforms make these projects far more affordable than before. It’s also important to account for ongoing operational costs, such as infrastructure, monitoring, and skilled talent, so ROI models reflect the full life cycle of real-time systems, not just the initial build. Modern data and analytics platforms are built to scale, allowing insurers to start small and expand without having to rebuild from scratch.
Integrate With What You Already Have
You don’t need to rip and replace your policy administration system to use real-time data. In fact, most insurers find more success by layering new capabilities around their legacy cores rather than attempting a full system overhaul.
Practical approaches include:
- Dashboards and Alerts: Streaming enriched claims data into a real-time dashboard allows adjusters to spot spikes immediately, even if the claims platform itself isn’t modernized.
- APIs and Overlays: Application programming interfaces (APIs) or cloud-native overlays can connect legacy systems with real-time feeds, giving underwriters access to updated data without forcing them to switch tools.
- Decision Support Engines: Instead of rewriting policy workflows, you can feed real-time risk scores or fraud indicators directly into existing decision engines, enhancing accuracy without disruption.
The goal is to enhance, not disrupt, the processes your teams already rely on.
Build Momentum Through Measurement
The best way to win support is to show impact. Document improvements in cycle time, claims handling costs, or quote conversion rates and share them broadly. When executives and teams see measurable progress, adoption accelerates.
One real-world example comes from outside the insurance industry but illustrates the same principle. Centric Consulting worked with a biogas producer facing strict regulatory requirements to report emissions data every minute. Missing even short intervals created compliance risks and financial penalties.
By shifting to real-time monitoring and alerting, the company could detect gaps instantly and take corrective action, turning what was once a liability into a measurable business advantage. Insurers face a similar challenge: When risk signals like weather alerts or claim spikes go unmonitored, costs can grow quickly. With real-time data, you can intervene before small issues become major losses.
Real-time success comes from starting small, focusing on clear business value, and scaling what works. With the right use cases and expectations, you’ll move from proof of concept to measurable payoff.
What’s Next for Real-Time Insurance Data
As customer expectations rise and new risks emerge, insurers that continue to rely solely on batch processes will struggle to keep pace. The next frontier isn’t just faster data, but smarter data: real-time streams enriched by artificial intelligence (AI), governed by strong business rules, and embedded seamlessly into decision-making.
Artificial intelligence will accelerate this shift. As models mature, AI can help insurers spot correlations across claims, policy, and external risk data that humans would miss. Early adopters are already using AI to strengthen fraud detection and underwriting precision. Paired with real-time feeds, these tools make decisions faster, fairer, and more accurate.
The future won’t be about if insurers adopt real-time data. Instead, it will be about how quickly they can scale it and which use cases deliver the most business value. With a solid foundation, clear governance, and targeted use cases, you’ll be positioned to lead in the insurance industry.
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