A lot of people like to talk about data driven organizations. A lot of these people don’t seem to know what it really means. In my experience at a tech company like Google, the default way to make decisions is to do an experiment and objectively assess the results. They do this in a small way with A/B tests on things like font color. But they also do it in a massive way like having multiple teams working on different approaches to the same type of product, and letting the best one win.
They can’t do this for every decision, but the ideal is deeply ingrained in the culture. Engineers are encouraged to start building their ideas before a decision is made because showing a working prototype is the most effective way to make a decision. Leaders try to give equal weight to ideas that come from interns or from the CEO because the only way to truly know what’s best is to try it.
Being data driven means that experience, intuition and authority take a backseat to direct observation.
Tech companies can get away with this because response times are fast. A/B testing the font color takes days. Launching a new feature takes weeks or months. Collecting data is cheap, so you can collect enough to trump experience and intuition. Plus the tech world is moving fast enough that experience and intuition are much less valuable.
Biotech organizations use experiments to make decisions about the progress of their drug programs, the designs of their medical devices, etc. But testing the pipeline itself is much harder. You won’t truly know if a new approach to drug discovery works until the phase 3 trial is complete. Five to ten years (or more) is far too long a cycle time to be data driven.
When you can’t collect enough data to make a data driven decision, intuition and experience are the next best thing. So maybe it’s better if biotechs aren’t data driven about absolutely everything. They just need to find the right balance between data and experience/intuition/authority.