Emphasize Data Suitability over Data Quantity
November 30, 2016
It seems obvious to us, but apparently, some folks need a reminder. Harvard Business Review proclaims, “You Don’t Need Big Data, You Need the Right Data.” Perhaps that distinction has gotten lost in the Big Data hype. Writer Maxwell Wessel points to Uber as an example. Though the company does collect a lot of data, the key is in which data it collects, and which it does not. Wessel explains:
In an era before we could summon a vehicle with the push of a button on our smartphones, humans required a thing called taxis. Taxis, while largely unconnected to the internet or any form of formal computer infrastructure, were actually the big data players in rider identification. Why? The taxi system required a network of eyeballs moving around the city scanning for human-shaped figures with their arms outstretched. While it wasn’t Intel and Hewlett-Packard infrastructure crunching the data, the amount of information processed to get the job done was massive. The fact that the computation happened inside of human brains doesn’t change the quantity of data captured and analyzed. Uber’s elegant solution was to stop running a biological anomaly detection algorithm on visual data — and just ask for the right data to get the job done. Who in the city needs a ride and where are they? That critical piece of information let the likes of Uber, Lyft, and Didi Chuxing revolutionize an industry.
In order for businesses to decide which data is worth their attention, the article suggests three guiding questions: “What decisions drive waste in your business?” “Which decisions could you automate to reduce waste?” (Example—Amazon’s pricing algorithms) and “What data would you need to do so?” (Example—Uber requires data on potential riders’ locations to efficiently send out drivers.) See the article for more notes on each of these guidelines.