Big data drives the modern enterprise. It is now routinely collected, shaped, and deployed to power applications spanning all aspects of operation—from production to ERP to supply chain to CRM, the list can go on and on. And the speed at which all of this transpires is astounding: Fast Data now drives near real-time customer interactions, instantaneously adjusts processes for optimization, and curbs security breaches automatically. The scope of just how great an impact our collective shift to big data-based operations has made on the nature of business can best be illustrated by looking to Formula One (F1) racing.

In the interest of full disclosure, I grew in Monaco, so I was exposed to F1 from an early age and am a serious fan. And while that may color my views on Fangio and Vettel, it doesn’t negate the fact that the apex of international auto racing is actually analogous to data-driven enterprise operation in a number of ways:

  • Some enterprise functions require strategic real-time data to take immediate action, akin to the split-second decisiveness required of skilled F1 drivers during the course of a race.
  • Other functions also require precision, but permit more time to identify patterns based upon recurring data and make decisions informed by both past experience and the demands of present circumstance, akin to the efforts of the pit lane crew.
  • And all those involved in managing enterprise operations are kindred to the F1 engineers who leverage disparate information and expertise to make and continually improve those beautiful F1 chassis and engines. Likewise, their enterprise counterparts leverage disparate data to improve the way a business runs.

The biggest common denominators for both F1 and the modern enterprise are ever-advancing technical complexity and speed. For smooth function and any chance of success in both realms, the right data needs to get to the right people at the right time—and it needs to do so with consistency and great speed.

Think about enterprise managers and how they parallel F1 engineers. They rely on data to provide context on how the business or team is doing internally, as well as context on how the market space or particular race is functioning. They have a game plan, but are continually examining conditions and looking for the best set-up—the one that is going to produce the best results given the particular situation. Increasingly, what is needed is the ability to make quick contextual decisions as well as the flexibility to adapt based on all the data coming in.

Today, you need the data fast, as well as the ability to align those decisions and execute rapidly. In business, the Netflix model is the obvious example: leveraging analytics and speed with a massive data-management effort to create a solid and scalable operation. And the velocity at which that particular organization is adapting is quite impressive: they exemplify a philosophy that embraces agility. This doesn’t mean every enterprise has to do exactly what Netflix does. But just as no two F1 cars are identical, all F1 cars do have to function under the same “formula” equipment and rules. So while the “Netflix way” won’t work for every organization, it does present an indication of how enterprise tools and techniques are rapidly evolving, and enterprises will have to address those facts. Innovation in the effective management of big data, particularly in areas such as real-time analytics, has redefined performance capabilities both on and off the track.

Because the speed of innovation has spiked so rapidly where data collection, processing, and actionability are concerned, one emergent way of addressing it is through partnerships, which have become very important both in F1 and in enterprise operations. In F1, you’ll see tire-manufacturer Pirelli supplying mountains of important diagnostic information about performance under various conditions to the various F1 engineering teams, which they incorporate when testing and preparing their cars for competition. Likewise, you’ll often see design entities collaborating to create a team’s cars; hence the difference between entrants and constructors in F1 (for example, team Ferrari has a Ferrari engine this year, but so do team Sauber and Toro Rosso). The same is true in industry, where more and more enterprises integrate a variety of third-party technologies to best enhance the performance of their core operations, and ecosystem-building has become increasingly important across the board.

Formula One as a sport has evolved over the last 50 years from relying entirely on tinkering, instinct, recollection, and hand-written signs to the use of advanced computer assisted design, telemetry, event processing, and deep analytics as a matter of course. The rapid rate of innovation advances the capabilities of the cars and their drivers, and race fans get to witness the breathtaking marvel of speed. Data in the enterprise is fueling a similar course for business, where innovation has advanced our capabilities exponentially over a few short years, and again we’re witnessing the marvel of speed. The 2016 Formula One season kicked off on March 20 with the Australian Grand Prix in Melbourne, and will culminate in November with the final Grand Prix in Abu Dhabi. The Fast Data race in enterprise is already well under way and shows no sign of stopping. The chequered flag awaits!

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