Logical Data Management and Data Products Deliver Data for Insurers

Taming the Transient Data Tide

The challenge today isn't just about data volume—it's about velocity. Insurers are inundated by a rising tide of transient data: short-lived, rapidly changing, high-frequency information that may not be stored permanently but is mission-critical in the moment.

Examples include telematics data for usage-based personal auto insurance, streaming weather and catastrophe feeds for commercial property risk, foreign exchange rates and capital markets data for IFRS 17 compliance, Internet of Things (IoT) sensor data from industrial equipment for commercial liability risk, and real-time behavioral signals to detect fraud in digital claims journeys.

This "delta data"—the small changes that matter—create immense pressure. Insurers must respond in real time, whether to prevent losses, adjust pricing, or fulfill compliance requirements. However, storing all of this in a central data lakehouse is often too slow, rigid, and expensive.

Why the "Last Mile" of Trusted Data Matters

This is where many insurers encounter a fundamental roadblock: Even if they've captured the data somewhere, they can't deliver it in the right format, with the right governance, at the right time, or to those who need it. This is known as the "last mile" problem, and it's particularly acute in the insurance industry.

Underwriters, risk teams, and claims handlers don't just need access; they need business-ready, explainable data, with context and control. That means knowing where the data came from, whether it's compliant, and how to act on it with confidence.

For this, insurers increasingly rely on logical data management (LDM) and data products, an architecture and delivery model that makes data both agile and accountable.

LDM + Data Products = Bridging the Gap

LDM creates a virtual, governed data layer that connects data across cloud platforms, on-premises systems, legacy cores, streaming sources, and external providers without duplicating or moving the data. Instead of batch ingesting every piece of data into a lakehouse or warehouse, LDM allows users and systems to query it in place.

Data products sit on top of this logical layer. These are curated, reusable, and explainable assets designed for specific use cases, including underwriting, Solvency II, IFRS 17, fraud detection, customer engagement, reinsurance optimization, and more.

Data products can be at the following levels.

• Bronze level. Raw, lightly curated data for data scientists or actuaries.
• Silver level. Aggregated and standardized datasets for internal risk reporting or compliance.
• Gold level. Fully governed, business‑consumable data products tailored for frontline decision‑makers, artificial intelligence (AI) agents, or external reporting.

Unlike dashboards or reports, these products can be delivered via application programming interfaces, generative AI (GenAI) interfaces, or real-time alerts, making them consumable by both humans and machines.

Use Cases Across Insurance Segments

Whether writing personal lines, commercial coverage, or specialty insurance, the benefits of LDM and data products are far-reaching. For personal insurers, they enable real-time pricing based on driving behavior, smart home sensors, or wearable devices. They streamline digital claims with explainable AI models using historical and real-time data, and power hyperpersonalized policy offers using household context, customer history, and third-party data.

Commercial insurers use them to combine IoT sensor data, maintenance logs, and external risk indicators for industrial risk scoring. They support instant underwriting assessments for complex portfolios by connecting legacy policy data with real-time exposures and automate environmental, social, and governance (ESG) risk scoring for commercial clients using third-party feeds and investment screening data.

Reinsurers and specialty insurers benefit by fusing catastrophe modelling data, policy-level exposures, and historical loss ratios into a unified reinsurance treaty assessment product. They gain near real-time claims aggregation views across cedents and regions and enhance exposure management by integrating live feeds from geospatial sources, climate analytics, and insurer submissions.

Role-Based Impact Across Insurance Functions

This isn't just about IT modernization; the ability to deliver trusted, transient data impacts every major insurance function.

• Underwriting. Receives gold‑level data products combining historical loss data, real-time exposure, risk scores, and policy history, speeding up decisions, improving pricing, and ensuring consistency across portfolios.
• Claims. Uses real-time feeds and unified views to detect anomalies, flag fraud patterns, and automate first notice of loss responses, reducing cycle times and increasing customer satisfaction.
• Risk and compliance. Accesses consistent, auditable data for Solvency II, Own Risk and Solvency Assessment, IFRS 17, and regulatory disclosures with full lineage and policy enforcement built in.
• Actuarial and finance. Leverages aggregated, governed datasets for modelling, reserving, and capital adequacy, while reducing manual data wrangling and improving model transparency.
• Product and innovation. Builds and tests new offerings, embedded insurance, usage-based pricing, and climate-linked policies, using dynamic data products instead of waiting for custom pipelines.
• AI, analytics, and GenAI teams. Feeds governed, explainable data into large language models, chatbots, and AI agents, ensuring that answers are consistent, compliant, and context‑aware.
• Real results, real impact.

In a recent IDC global study, insurers using logical data management and data products reduced time‑to‑insight by over 50 percent, while improving confidence in the data delivered to frontline systems. They're also unlocking new revenue streams through embedded insurance, accelerating ESG investment analysis, and reducing fraud losses through advanced behavioral modelling.

This is not just an efficiency play; it's about transforming data into a resilience driver, enabling faster and smarter decisions in a world of ever-changing risk.

Looking Ahead: Powering Agentic AI in Insurance

The next evolution is already underway: agentic AI, which are intelligent agents that not only answer queries but also take the initiative. Imagine an AI agent that continuously monitors catastrophe alerts and adjusts regional underwriting thresholds, a compliance bot that scans all transactions for regulatory breaches using real-time feeds, or a customer-facing GenAI assistant that adapts offers based on household risk, policy bundling, and claims sentiment.

These capabilities are only possible if the agent is fed the right data product: trusted, contextual, timely, and policy-compliant. Agentic AI will be data-hungry, and insurers who can't deliver governed data in real-time will struggle to operationalize it safely.

That's why LDM and data products are not just back-end enhancements; they're the operational foundation for safe and scalable AI adoption.

Turning Complexity into Confidence

If the last decade in insurance was about digital transformation, the next will be about data activation and AI enablement. In a volatile environment, insurers don't just need to store and analyze data; they need to deliver it where it matters most—to risk professionals, frontline systems, and intelligent agents.

With logical data management and data products, insurers can tame the data tide; deliver the last mile of trusted, transient data; and accelerate the adoption of AI automation and innovation. This shift moves the organizations from reactive risk management to proactive resilience, unlocking growth, building trust, and creating confidence at every level.