&gt; but we&#x27;re actually swimming in data ...<p>I think this is a &quot;looking under the lamppost for your keys because that&#x27;s where the light is&quot; situation. We hear about tons-of-data and deep learning because teams like Google&#x2F;FB have problems that fit those situations well. This is not representative of the vast sea of learning applications most organizations face.<p>I work on learning applications day-in-day-out. In a typical year I work with over 50 different organizations&#x2F;projects. Almost none of them have data anywhere near what deep learning requires. Here are just a few examples from the past year:<p>1) optimizing recruiting pipeline. Maybe you have ~1000s of data points per year<p>2) medical billing applications: typically hundreds of data points per year<p>3) novel but slow-moving financial instruments, maybe 1 data point per day<p>4) automated sensor calibration where each run of the hardware costs you a few hundred dollars: depends on your budget, but thousands of data points in a year are representative<p>I think Google&#x2F;FB and the &quot;web+mobile is everything hivemind&quot; (I&#x27;m not saying that applies to you) have deeply distorted people&#x27;s expectations for how much data is available to solve a typical problem.