If network effects are one of the most important concepts for software-based businesses, then that may be especially true of data network effects -- a network effect that results from data. Particularly given the prevalence of machine learning and deep learning in startups today. But simply having a huge corpus of data does not a network effect make! So how can startups ensure they don't get a lot of data exhaust but get insight out of and add value to that data and the network? How can they make sure that the (arguably inevitable) data aspect of their business isn't just a sideshow or accident? How should founders strike the balance between not overbuilding/ building a data team vs. having enough data for those data scientists to work with in the first place? And finally, what are the ethical considerations of all this? The a16z general partners most focused on bio and fintech -- Vijay Pande and Alex Rampell -- join this episode of the a16z Podcast to share their observations and advice on all things data network effects.