News in today’s US print edition of the Wall Street Journal calls out the growing power of data. Uber, in its IPO document filed with the Securities and Exchange Commission last week, details how the company is collecting data about cab demand as well as food demand (for its UberEats business). In reality the company is profiling the demand for its market like no other transportation service ever has.
The WSJ article reports that Uber has amassed more than 10 billion passenger trips for use in predicting demand for its services. This is of course no different than a public sector agency tracking the requests it gets for service from its citizens or a manufacturer of soap tracking demand at store level. All such situations call for data and predicting demand. The difference is that with AI such business processes are getting better; and newer business processes are becoming data value chains or value networks.
Local can services might have focused on forecasting peak demand, say for airport travel around peak arrival slots. But Uber looks more broadly than this and may in fact be able to organize far greater number of assets to meet overall market demand. That might explain why the services seems so popular with users and also threatening to its competitor services. But really the need to forecast demand is not new. Its the scale that is different; and the accuracy that comes with a learning algorithm that improves the accuracy.
Beyond the data you know about, what about the data or value you don’t? This is called dark data. The idea is that you probably want to collect as much data as you can afford, for as long as you can, while avoiding or mitigating risk. At some point that data might prove valuable but you just can’t tell when. As AI techniques continue to mature, that time when that dark data becomes useful may be sooner than we thought.
So are you amassing your data war chest?
See also The War on Data Continues.