🧬 Why Wet-Lab Data Needs More Than ETL | Nathan Clark Rerelease (3/4)

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“What matters is that you say, okay, this sample is part of this program and that program is part of this area.”

Nathan Clark walks through his work at Benchling, where he managed the Insights business intelligence product and helped develop an early machine learning effort. He explains why operational wet-lab data is fundamentally different from omics data, and why the relationships between samples, programs, instruments, locations, and people matter as much as the experimental results.

The conversation then moves into product discovery in scientific software. Nathan contrasts high-volume experimentation at Affirm with the slower, more qualitative process of working with research labs, where product teams need to study customer behavior, understand hidden needs, and show users what structured data can make possible. He also discusses why adoption requires tangible demonstrations when a category has no established name or buying process.

Nathan traces the thinking that led to Ganymede, including the problem of biotech companies rebuilding the same AWS infrastructure, instrument parsers, and data connectors. He describes the cost of data decay, the value of turning unstable experimental information into structured design-of-experiments data, and the need to make scientific knowledge easier to share instead of repeatedly recreating the wheel.

Key Topics Covered:

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Find Nathan Clark at these links:

LinkedIn: https://www.linkedin.com/in/nathan-clark-4b850134/

Website: https://ganymede.bio/

Find our host, Jon Chee, at these links:

LinkedIn: https://www.linkedin.com/in/jonchee/

Learn more about Excedr:

LinkedIn: https://www.linkedin.com/company/excedr/

Website: https://www.excedr.com/

Intro/Outro music:

OkKyojin: https://flow.page/kyojin

Resources & Articles:

NIST, What is design of experiments?: https://itl.nist.gov/div898/handbook/pmd/section3/pmd31.htm

NIST, What are the steps of DOE?: https://itl.nist.gov/div898/handbook/pri/section1/pri13.htm

Journal of Biomedical Semantics, ELN data provenance: https://link.springer.com/article/10.1186/s13326-021-00257-x

Snowflake, Modern data stack: https://www.snowflake.com/guides/modern-data-stack

Companies, Universities, & People Mentioned:

Salesforce: https://www.salesforce.com/

Snowflake: https://www.snowflake.com/

Databricks: https://www.databricks.com/

Fivetran: https://www.fivetran.com/

AWS: https://aws.amazon.com/

Ginkgo Bioworks: https://www.ginkgobioworks.com/

00:00 Intro

01:43 Benchling Insights and Machine Learning

03:55 Why Operational Data Matters

04:26 Benchling as a Relational System

05:10 The Modern Data Stack for Wet Labs

07:27 Finding a Business in Data Integration

07:46 Product Management in the Lab

10:31 Learning From Customer Behavior

11:26 Getting Scientists to Adopt New Tools

13:39 Showing the Value of a New Category

16:56 The Spark Behind Ganymede

18:22 Capturing Hidden Variables

20:22 Turning Experiments Into DOE Data

24:01 The Problem of Recreating Infrastructure

26:49 Data Decay in Biotech

28:16 Outro

The Biotech Startups Podcast gives you a front-row seat to the business and science of building a biotech. Hosted by Jon Chee, CEO of Excedr, the show features honest conversations with founders, execs, and investors about their work, their companies, and how they got there. From scientific breakthroughs to startup lessons, each episode explores what it really takes to grow a life science company—from pre-seed to IPO.