 John Kerry Rue's book, Bad Blood, describes the rise and fall of Theranos, a startup that was supposed to be making some sort of innovative blood test. But what exactly were they trying to invent? Theranos said they were making a new way of doing blood tests that used a fraction of the blood normally used. But no one really had a viable plan for how they were going to do that. What was the special secret sauce their product had that would make it better than existing products? Theranos' CEO was very secretive. So secretive, she didn't want to release product descriptions, saying that others would steal Theranos' intellectual property. But that's really no excuse. In fact, that's a red flag. You should be able to have a description in the public domain about your innovative product that basically makes sense to scientists and lay people without giving away IP. If any startup wants to actually do data science about their product, they need to have plain language descriptions of what the product is supposed to do so scientists and researchers can operationalize these ideas into data concepts. Then, we can create measurements in the data related to those concepts. I'm Monica Wahee, and I'm an author on LinkedIn Learning. Want guidance on operationalizing variables from a product description to actual data? Take my LinkedIn Learning two-part course series, Designing Big Data Healthcare Studies. The link is in the description.