Big data isn’t new. There’s just more of it and it’s getting harder and harder to figure out how best to use it. I’d like to share a few thoughts about big data and what I learned from a talk about how Formula One racing teams use big data.
Let’s put today’s data in perspective. One study estimated that by 2024, the world’s enterprise servers will annually process the digital equivalent of a stack of books extending more than 4.37 light-years to Alpha Centauri, our closest neighboring star system in the Milky Way Galaxy. That’s a lot of data to analyze.
According to Gartner analyst Svetlana Sicular, “Big data is a way to preserve context that is missing in the refined structured data stores — this means a balance between intentionally “dirty” data and data cleaned from unnecessary digital exhaust, sampling or no sampling. A capability to combine multiple data sources creates new expectations for consistent quality; for example, to accurately account for differences in granularity, velocity of changes, lifespan, perishability and dependencies of participating datasets. Convergence of social, mobile, cloud and big data technologies presents new requirements — getting the right information to the consumer quickly, ensuring reliability of external data you don’t have control over, validating the relationships among data elements, looking for data synergies and gaps, creating provenance of the data you provide to others, spotting skewed and biased data.”
While I don’t have THE solution, I do have one suggestion. Find a way to analyze the disparate data coursing through your environment and give it meaning – business context.
For example, focus on the right information by asking what’s important to the business. In “The Data Driven Business of Winning” Managing Director of CMS Motor Sports Ltd. Mark Gallagher, shared how Formula One teams successfully analyze data to ensure the safety of drivers and win races.
Gallagher explained how a team of data engineers, analyzing reams of information in real time, can help make strategic decisions for the business during the race. “In 2014 Formula One, any one of these data engineers can call a halt to the race if they see a fundamental problem developing with the system like a catastrophic failure around the corner.”
It comes down to the data engineers looking for anomalies. “99% of the information we get, everything is fine,” Gallagher said. “We’re looking for the data that tells us there’s a problem or that tells us there’s an opportunity.”
TeamQuest Director of Market Development Dave Wagner likens this right data approach to a Moneyball concept he’s discussed in a recent webinar.
Much like the Formula One example above, Wagner believes IT must understand what’s important to the business in order to be successful and be able to deliver accurate, strategic advice – sometimes in a matter of seconds.
Teams can be successful if they’re able to look at the right data in combination with powerful analytics, according to Wagner. In fact he likens it to the equation:
Good data + powerful analytics = better results
Just ask Formula One about the power of good data and analytics. A Formula One driver’s steering wheel is basically a laptop, providing him with the data needed to make the best decision available. Drivers can scroll through a 10-point menu – while driving – and adjust parameters that affect the performance of the vehicle. This happens because the driver is able to get to the right data when needed to get a desired outcome.
Lots of data is collected by IT, which shares data that’s important to the customer (business), and together they use that data to gain an advantage and be successful in the marketplace.
That’s a nice checkered flag ending and a nice way to end the day.