Introducing Data Grain and How it Unlocks Information Asymmetry in Specialty Insurance

For too long, specialty insurance has been defined by data asymmetry—a system where critical underwriting insights are fragmented, siloed, or outright missing. This lack of transparency has led to inefficiencies at every level, from suboptimal risk selection to capital misallocation.

Of course, once you dig into things, it becomes clear that there isn’t a shortage of data in insurance. At Accelerant, we believe that solving the challenge of information asymmetry isn’t just about collecting more data—it’s about ingesting, structuring, and sharing the right data, at the right level of granularity. Our ingestion engine ensures that every risk in our portfolio is analyzed with the right data grain—not too broad, not too cluttered—so that both MGAs and risk capital partners can make faster, smarter, and more profitable decisions.

The role of data grain in driving market-leading growth

For decades, insurers have struggled with either too little data—leading to conservative pricing and missed opportunities—or too much raw, unstructured data, which slows down decision-making and creates noise instead of insight. Accelerant’s approach is different, and we spend a lot of time thinking about data grain, which basically refers to the level of detail at which data is captured, structured, and analyzed to be most useful.

We think of data grain as the “Goldilocks” of the data strategy. It’s the level of detail that’s just right—not too much, not too little. It’s important because the granularity of your data determines how actionable it is. For example, if an MGA only collects total premium written for a program but doesn’t capture individual policy-level exposure details, they can’t spot trends, optimize pricing, or demonstrate performance to capital partners. On the other hand, if every policy interaction and adjustment is logged without structure, underwriting teams are stuck sifting through noise instead of acting on insights.

Data grain also matters beyond underwriting. If policyholder contact data is stored inconsistently—such as mixing structured fields with free-text notes—automated systems can’t correctly generate renewal offers, claims notifications, or risk alerts. That’s why at Accelerant, we ensure that every data point is captured at the right level of granularity—structured, clean, and immediately useful—so MGAs, insurers, and risk capital partners can operate efficiently and profitably.

Well-managed data isn’t just an asset—it’s a competitive advantage. As it turns out, getting data grain right touches every aspect of performance:

• Strategic Insights: Well-structured data reveals patterns across a portfolio, allowing for more accurate pricing, risk segmentation, and capital deployment.
• Operational Excellence: The right level of detail enables real-time underwriting adjustments, faster claims handling, and proactive risk management.
• Trust and Compliance: Capturing only essential data builds stronger relationships with capital providers, policyholders, and regulators, reducing friction and unnecessary risk.

How to get data grain right

Start with clear goals: What Are You Solving For? Before collecting a single data point, define why you need it. Are you looking to improve risk selection, streamline claims, or demonstrate value to capital providers? The goal will help you back into what to collect and how to store it.

• Example: If an MGA is focused on reducing loss ratio volatility, collecting detailed claims history at a policyholder level is essential. But capturing every historical claim adjustment down to the minute it was filed? That’s unnecessary complexity.

Remember more isn’t always more. Capturing every possible data point can slow down operations and overwhelm underwriting teams. Not all data is created equal. The right data grain means capturing the details that drive impact while avoiding clutter.

• Example: An MGA underwriting professional liability coverage doesn’t need to track every line item in a client’s contract, but they must capture high-level contractual obligations that impact claim liability.
• Tip: If a dataset isn’t actively used in reporting, analytics, or decision-making, it’s likely too detailed. Eliminate or aggregate where possible.

Build for the future: scalable data architecture. We’ve seen time and again how failure to invest in the right architecture tends to hamper data (it’s only as useful as it is usable).

• Tip: Invest in structured, cloud-based data systems that can ingest, normalize, and analyze risk data in real-time—this ensures flexibility and speed as the business grows.

At Accelerant, we handle the heavy lifting of data ingestion, structuring, and distribution so that MGAs and their capital partners can focus on growth, underwriting excellence, and capital efficiency.

• For MGAs, we ensure underwriting teams have clear, structured insights that improve risk selection and pricing.
• For Risk Capital Partners, our platform delivers the transparency they need to confidently allocate capital.

Our goal to make the insurance industry work better for everyone requires eliminating data asymmetry, creating a risk exchange where information flows freely, and improving efficiency across the ecosystem. And that starts with data grain.