The term “big data” is ubiquitous. With exabytes of information flowing across broadband pipes, companies compete to claim the biggest, most audacious data sets. And businesses of all varieties — old and new, industrial and digital, big and small — are getting into the game.
Masses of social, weather, and government data are being leveraged topredict supply chain outages. Enormous amounts of user data are beingharnessed at scale to identify individualsamong a sea of website clicks. And companies are even starting to leverage hugequantities of text exchangesto build algorithms capable of having conversations with customers.
But the reality is that our relentless focus on the importance of big data is often misleading. Yes, in some situations, deriving value from data requires having an immense amount of that data. But the key for innovators across industries is that the size of the data isn’t the most critical factor — having the right data is.
Uber is often referred to as a big-data success story. There is no doubt that Uber captures a wealth of information. Using the applications it has running in both its drivers’ cars and its users’ pockets, it has mapped the real-time logistics flows of human transportation.
But Uber’s success isn’t a function of the big data it collects. That big data has enabled the company to enter new markets and fulfill new jobs in the lives of its customers. Uber’s success results from something very different: the small,rightdata it needed to do something very simple — dispatch cars.
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.
Sometimes the right data is big. Sometimes the right data is small. But for innovators the key is figuring out what those critical pieces of data are that drive competitive position. Those will be the pieces of right data that you should seek out fervently. To get there, I’d suggest asking the following three questions as a process for drilling down to the right data.
Question 1: What decisions drive waste in your business? Most businesses have large sources of waste. Consider the world of floral retailing. The average retail florist can sustain spoilage rates of more than 50% of their inventory. More than half of their flowers simply become refuse.