Hi all — I’m Gavin Powell. I’m a very experienced data engineer with a background in data warehousing, analytics, automation, and messy real-world datasets.
I work with property, location, climate, claims, demographic, and geographic data, and I’m especially interested in property insurance, flood, weather, catastrophe response, roof/property condition data, and property-related risk.
I’m here to learn from people who are closer to the actual insurance work than I am.
I can build the data side, but I do not want to guess at the insurance side. What I do not have is enough direct field experience to know which problems are genuinely painful versus merely interesting from the outside.
For example:
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Underwriting data that is missing, stale, or unreliable
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Roof/property updates that are hard to verify
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Permit, inspection, or public-record gaps
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Flood, hail, wind, wildfire, or catastrophe-response analysis
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Claims triage or prioritization
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Broker/agent risk or territory analysis
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Manual spreadsheet work that should probably be automated
For those of you working in claims, adjusting, underwriting, brokerage, catastrophe response, or risk management:
What data problems keep coming up?
Where is information missing, stale, hard to trust, or too manual?
I’m not posting an advertisement or pretending I already know the answer. I’m trying to understand where data engineering and analytics could actually be useful to people doing the work.
Thanks.