The main difference between traditional data science and the demands for IoT are generated by the real-time factor. The high signal rates of discrete observations need to be aggregated to take on the spot decisions. The two dimensions that add complexity to this problem are the need to streamline data management and compress a massive amount of data coming from various sensors.
Gartner predicts that the number of IoT devices will surpass 11.2 billion this year, the majority of which are in the consumer sector. The same report forecasts that the endpoint spending will exceed $2 trillion, in hardware and software combined.