This article was originally posted on BigThink.com
Nikolaus Otto invented the basic design of the 4-stroke internal combustion engine – the power plant that today moves most of the vehicles on the world’s roads – back in 1862. The engines of Otto’s day were clattering, weak, and inefficient smoke bombs that drank in rivers of gasoline in return for trickles of power.
Big Data is in a similar position today. Data, we have learned, is a potent energy source for moving businesses, but we have only just figured out how to turn that energy into the work that will power sales. Our methods have been inefficient, sucking data in through a fire hose and giving back only modest insights.
We may have wrung all the efficiency we can get out of the internal combustion engine, but we have only begun to ramp up the efficacy of our data processing. Fortunately, the efficiency of our data engines is growing at the same moment as the supply of data is booming. This same happy confluence greeted Otto’s new motor, and the age of the automobile was upon us. Can the same happen for data? Of course.
Companies big and small have begun to see how data – usually data they already have – can help their businesses grow dramatically with only minimal investment. The supply of data they are sitting on is, like the oil fields of Texas, enormous and waiting to be tapped. What’s been missing is the need for the data and the means to use it.
The need stems from the increasing competitive pressures facing modern business. Many, if not most, of the physical revolutions in business have already taken place. Companies must settle for incremental improvements that move them just a step ahead, a lead that vanishes quickly. All the while, they pour investment into finding the next breakthrough idea. It’s a frustrating wait since paradigm shifting products or services are once in a decade events. Increased scrutiny and greater accountability pushes business leaders to search for any advantage they can find. Product or service innovation may not supply it, but data innovation almost certainly will.
The means comes from the burgeoning supply of data that we exhale with every move we make in the modern economy. As I show in my book, Sexy Little Numbers, one simple business trip from London back home produces a flood of data, most of it still ignored by business, which can deliver bottom line results – profit! – if only it were used efficiently. The means is also supplied by dramatically increased computing power, which can unearth countless hidden, profitable patterns that are opaque to human analysts.
Big Data – a smug storehouse of unanalyzed information – is giving way to Dynamic Data. Dynamic Data is what happens when we learn how to use data wisely. Here are the three developments heralding the coming of Dynamic Data:
1. Data sourcing: The biggest breakthroughs of the next few years will be in gaining access to different types of data. Many companies produce vast quantities of data as a by-product of what they do. It’s been dubbed “data exhaust.” Most of that data just sits on servers collecting dust, but it could be turned into immensely valuable information if only it were put in the hands of the right people. Here’s an example. A software company developed the accounting software used by 80% of the US car maintenance shops. But there’s a hidden gem in the program. The software throws off a database of millions of cars with their entire car maintenance history as a by product of the application’s cloud-based solution. Imagine the value of that data to an automaker like Ford or Toyota! Big Data believes that size matters most, but future competitive advantage may very well go to the companies gain access to the Dynamic Data their competitors don’t have.
2. Analytics: Finding the right data is only the first step. All that data still needs to be analyzed, and, increasingly, it will be machines, not humans, that do it. In the past, we employed big teams of statisticians to crunch all the data. This limited the amount of data we could process and the depth of insight we could expect. Analytics talent is rare, and even the best of it can never match a computer at tasks like this. The massive, cheap processing power available to us enables us to use brute force computing to crank out the algorithms that give Dynamic Data its brains.
3. Application: This is the biggest challenge. And the biggest opportunity. Finding and analyzing data will invariably produce insights that lead to profit. Or they will if they are used properly. Far too many marketing decisions are made without taking into account the insights derived from data. Even when data-driven insights are available, companies often fail to take advantage of them. The insights that Dynamic Data produces must be made easily accessible to all decision makers and presented in a language they can understand. Dynamic Data, when done right, tailors its insights to the challenges business leaders face, and it does so in real time. But Dynamic Data, no matter how good, will have no effect if decision makers ignore it. They need to be educated on what data can do for their businesses.
Thought of this way, numbers really are sexy. They are what will power our world for the foreseeable future. They are available like never before, and we know now how to use them wisely. Once we develop the habit of using Dynamic Data to inform our business decisions, data will transform our landscape. The businesses that ignore Dynamic Data? Well, they might as well be selling buggy whips.
Dimitri Maex is the Managing Director of OgilvyOne New York.
Follow him on Twitter: @dimitrimaex