FSebastian Vettel at the 2012 FIA Formula One World Championship. Photo: Getty Images/Red Bull Content Pool

The Cost of Slow

Sebastian Vettel headed into the Brazilian Grand Prix, the final race of the 2012 Formula One World Championship, with the goal of become the youngest-ever three-time world champion.

The 25-year-old had won five races that year, finished in the top three of the last six, and was one of only two drivers with enough points to potentially take home the series title.

Then, in the fourth turn, with a light rain falling, the back of Vettel’s car met the front of Bruno Senna’s, sending Vettel spinning into oncoming traffic.

But that’s not where the story ends.

F1 cars wear thousands of sensors that collect petabytes of data that is continuously being analyzed.

By the time Vettel’s damaged car arrived at its tenth-lap pit stop, “engineers had run simulations using modeled data to determine the adjustments that would need to be made to the car to keep it running for another 70 laps,” Bernard Marr wrote in a July 2015 post on Forbes. The adjustments were made, Vettel reentered a high-action race and hours later won his third world championship.

F1 exemplifies speed in so many ways, and the sport has been a leader in creating competitive advantage through ever-faster reactions to data. Which is maybe an obvious approach when the competition is on your back tire at 200 mph.

F1 has been a leader in creating competitive advantage through ever-faster reactions to data. Which is maybe an obvious approach when the competition is on your back tire at 200 mph.

In business, where it’s common for companies to wait days if not weeks for action-advising reports, the pace is far slower, in part because our sense of the competition is so much less blunt. And that includes the changing pace of technology.

Futurist Ray Kurzweil believes that during the 21st century we won’t experience 100 years of progress but rather closer to 20,000 years of progress.

Put in conceivable terms, imagine Fenway Park at noon, dead quiet and empty, save for one of its 34,673 seats. In the very highest row, a single seat is occupied.

Now imagine a single drop of water is placed on the pitcher’s mound, and every minute the size of the water drop doubles. (Let’s assume Fenway Park is watertight and that fan is committed to his seat.) How long would it take for the water to reach him?

A week? A day?

Hardly. By 12:49 p.m. he’d be underwater. Scariest of all, he wouldn’t sense any danger until the last four minutes, because at 12:45, 93 percent of the stadium would still be dry.

This is what exponential growth looks like.

Imagine a single drop of water is placed on the pitcher’s mound at Fenway Park, and every minute the size of the water drop doubles. How long would it take for the water to reach him?

What, then, is the cost of slow to an organization? What’s the cost of being slow to adapt? Slow to respond? What’s the cost of suffering slow systems? Or of answering questions in a week instead of a day, a day instead of an hour, or an hour instead of minutes? What will those costs be in two years—and at what point might you wind up underwater, if you don’t start reacting now?

Some companies already consider these questions down to fractions of seconds.

One in four people will leave a site that takes more than 4 seconds to load, Kit Eaton reported in Fast Company, pointing to study in which Amazon calculated that a one-second slowdown of its page-load speed could cost it $1.6 billion in sales annually.

For Google, were its search results to render just four-tenths of a second slower, it could lose 8 million searches per day.

Today every enterprise should be engaging data in ways that enable analytics to behave like so many sensors, acting as early-warning systems, enabling agility and addressing problems as quickly as they arise.

Amazon calculated that a one-second slowdown of its page-load speed could cost it $1.6 billion in sales annually.

At Collective[i], our mission is to transform the ways companies build, manage and grow their revenue; we focused our initial offering on Sales because it’s the epicenter of the enterprise, with the closest ties to revenue. When products resonate, or don’t, Sales knows it first. Just like it’s the first to feel the earliest signs of a slow-down, or of a turn-around. It’s when Sales fails that companies turn to layoffs.

And still, most Sales teams are equipped with little more than PowerPoint and Excel — tools that pre-date the flip phone. And when they do have predictive analytics, they’re informed by too-little data, making them more like smartphones without apps; or like an F1 team trying to solve a problem, at race speeds, using megabytes, instead of petabytes, of data. The type and amount of data are as critical to the formula as brilliant analytics and a team of engineers that know how to quickly respond to them.

We believe that enterprises — responsible for not only keeping customers happy but for supporting families and in so many cases whole towns — are every bit as deserving of technology that’s as sophisticated in its ability to manufacture speed.

While size used to be the great differentiator (think Kodak, Borders, BlackBerry) today the most critical determinant of success is how fast you can move. •


Collective[i] provides real-time buyer intelligence, smart forecasting and other data-driven analyses based on one of the world’s largest networks of enterprise data, all to dramatically improve the performance of the world’s leading Sales teams. To learn more, visit
@Collectivei or www.collectivei.com.