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a16z Podcast: Data Network Effects


March 08, 2016 00:51

00:00 / 31:02

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. Read more
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