As we continue working backwards through the stages and use cases that software for biotech needs to support, we’re getting into the neighborhood of one of my favorite topics/borderline obsessions, namely metadata. In the last few posts, I covered finding and accessing data. And if this was any other industry, the step before that would be data collection. But in biotech, we find ourselves in a unique situation where metadata - the data about the data - not only plays a role as important as the data itself, but is collected separately from it. In the next few posts I’m going to cover how the two are collected. But first, we need to discuss how they’re merged back into a single, usable, dataset.
In biotech, data is only half the story.
In biotech, data is only half the story.
In biotech, data is only half the story.
As we continue working backwards through the stages and use cases that software for biotech needs to support, we’re getting into the neighborhood of one of my favorite topics/borderline obsessions, namely metadata. In the last few posts, I covered finding and accessing data. And if this was any other industry, the step before that would be data collection. But in biotech, we find ourselves in a unique situation where metadata - the data about the data - not only plays a role as important as the data itself, but is collected separately from it. In the next few posts I’m going to cover how the two are collected. But first, we need to discuss how they’re merged back into a single, usable, dataset.