The insurance industry is notoriously opaque and hard to navigate. Max Cho found that fact frustrating enough that he decided to build a business of making policy selection more navigable. In this episode he shares his journey of data collection and analysis and the challenges of automating an intentionally manual industry.
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- Your host is Tobias Macey and today I'm interviewing Max Cho about the wild world of insurance companies and the challenges of collecting quality data for this opaque industry
- How did you get involved in the area of data management?
- Can you describe what CoverageCat is and the story behind it?
- What are the different sources of data that you work with?
- What are the most challenging aspects of collecting that data?
- Can you describe the formats and characteristics (3 Vs) of that data?
- What are some of the ways that the operational model of insurance companies have contributed to its opacity as an industry from a data perspective?
- Can you describe how you have architected your data platform?
- How have the design and goals changed since you first started working on it?
- What are you optimizing for in your selection and implementation process?
- What are the sharp edges/weak points that you worry about in your existing data flows?
- How do you guard against those flaws in your day-to-day operations?
- What are the most interesting, innovative, or unexpected ways that you have seen your data sets used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on insurance industry data?
- When is a purely statistical view of insurance the wrong approach?
- What do you have planned for the future of CoverageCat's data stack?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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