People said predicting the future is like predicting how a drop of water rolls down a car window. Everyone knows the general direction of the water drop: Gravity inevitably pulls it downward. But to predict the specific path, such as how it interacts with other drops and how it reacts to microscopic imperfections in the glass, is next to impossible.
We are currently on the brink of new technological breakthrough that's easy to predict in general terms but impossible to predict in specific terms: internet of things (IoT).
If there is one question keeping CIOs—chief information officers—awake at night, it's this: How the hell am I going to integrate the IoT into my organization?
For CIOs especially, the promise of vast deluge of data spewed forth by the
IoT is both a blessing and a curse. On one side of the coin is unrivaled insight into your customers, your supply chain, your production operations etc.—all made possible by the IoT. The other side of the coin is this: "What if your competitor cracks it first?"
One thing is clear: IoT integration should be at the top of your agenda—and, in fact, at the top of your entire C-suite’s agenda. Because prying open the IoT treasure chest isn't a matter of technical prowess—it's a matter of organizational prowess.
Sure, the technical challenges are formidable in their own right. With the promise of near-endless data comes the requirement to store and analyze that near-endless data. And when our data lakes become so vast, how can we make sure that our data is secure and the quality is up to par. Tough nuts to crack, but with the help of advances in storage technology, machine learning and cybersecurity, we’re definitely making good progress. That’s why most of my conversations with the C-suite take on a different focus: “Hypothesize these challenges solved, how do you get it to actually work? And what does it mean for my organization?”
That’s where it becomes interesting and also where it becomes difficult to predict the path of the water drop.
Well-implemented IoT work environments allow organizations to learn just about anything about their supply chain and operations processes. If they apply these learnings to their sales, marketing and customer service departments, they are on their way to transforming their entire business model.
Consider John Deere, a U.S. manufacturer of agricultural equipment. Instead of thinking of itself as a manufacturer of tractors and harvesters, Deere now sees itself as a provider of soil quality. This transition was made possible by the many sensors that are placed on Deere's equipment. Those sensors constantly track and learn from the environment around them. But the truth is, that’s just the tip of the iceberg in terms of business model transformation.
So how do you get there? How do you get to a level of organizational learning where you are able to redefine your product into a service?