Some of you may have noticed, as I’m going through different use cases for biotech software, that I spent a measly one week on analysis, but I’m about to spend multiple weeks on data (and metadata) collection. Meanwhile, if you look at what everyone else is writing about, it’s LLMs and AlphaFold and generative models and so on, with very little about data collection. And I get it - those things are all much more interesting and flashy, and fun to think about. But the fact is, when it comes to what’s most important to an AI/data-driven biotech, it’s almost never the models. So this week, before we dig into data (and metadata) collection in all its gory details, I want to explain why I think data matters more than models.
> But you can’t leave it all to the biologists because they don’t think about data the way data scientists do. You need someone who understands both what it means for data to be more information rich (even if they don’t use those exact words) and the practical requirements (such as metadata) that will make the data usable. In other words, you still need data scientists for strategy (3). But they should be spending their time designing datasets instead of models.
Exceptionally well said. Excited to see how you explore this is later posts and topics.
> But you can’t leave it all to the biologists because they don’t think about data the way data scientists do. You need someone who understands both what it means for data to be more information rich (even if they don’t use those exact words) and the practical requirements (such as metadata) that will make the data usable. In other words, you still need data scientists for strategy (3). But they should be spending their time designing datasets instead of models.
Exceptionally well said. Excited to see how you explore this is later posts and topics.