At Accelerant, we often say we have a “no data left behind” mentality.
That mindset comes from how we’ve seen data create value over time, as we’ve scaled from just a few Members to hundreds. When information is preserved and shared across participants, patterns emerge that no single dataset can reveal on its own. Sure, a bar in London may have a higher risk, but if the owner lives above it, those risks meaningfully improve, something you can only really appreciate once you have a broad dataset. At Accelerant, we’ve seen firsthand how, as more data enters the system, network effects begin to do some of the heavy lifting. Insight improves.
Today, we have 57,000 unique risks represented in our system. A year ago, there were 21,000. That growth is geometric, more than doubling in a year. It materially changes what questions the data can answer.
Of course, that mantra of “no data left behind” simplifies the reality. Holding onto data without the ability to learn from it can quickly become a liability, or simply chaos.
Why this is hard for MGAs
Most MGAs aren’t short on data. They’re short on clarity.
Information lives in multiple systems. Data gets collected “just in case.” Old records linger because deleting them feels risky. Over time, teams spend more energy maintaining data than using it.
The result is a familiar frustration: plenty of information, very little insight.
For MGAs, the goal shouldn’t be to become data experts overnight.
A few practical starting points:
- Focus on decision-critical data. What information actually affects underwriting, pricing, or portfolio management today?
- Stop trying to predict future use. Much of the data worth preserving won’t look valuable at the moment it’s collected.
- Share where scale matters. Some insights only emerge when data is combined across participants and over time.
- Reduce internal data debt. Less time spent cleaning, storing, and governing data internally means more time spent making decisions.
The bottom line
It’s a privilege to have “no data left behind” as our strategy, because it only really works when data can compound and context can accumulate.
Our counsel to Members can therefore seem counterintuitive: You can be data centric without having to hold it all. Look for systems that let data do more work than your team has to. Shared infrastructure exists for a reason (and that’s why we’re building it at Accelerant).
Reach out to us to learn more.