Data Engineering Podcast

This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.

Support the show!

09 October 2023

Using Data To Illuminate The Intentionally Opaque Insurance Industry - E395

Rewind 10 seconds
Skip 30 seconds ahead

Share on social media:


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.


  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at
  • This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting
  • As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at
  • You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to today to get 2 weeks free!
  • 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


  • Introduction
  • 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?

Contact Info

Parting Question

  • From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

  • 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.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • If you've learned something or tried out a project from the show then tell us about it! Email with your story.
  • To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers


The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Sponsored By:

Support Data Engineering Podcast

Share on social media:

Listen in your favorite app:

More options

Here are shows you might like

See show recommendations
AI Engineering Podcast
Tobias Macey
The Python Podcast.__init__
Tobias Macey