“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Yannick Koger, principal consultant, big data and business intelligence, at Infinitive.
Yogi Berra once said that it's tough to make predictions, especially about the future.
He could just have easily been talking about business intelligence as baseball when he uttered those immortal words.
I’ve previously discussed the importance of the film “Moneyball” in exploring how big data increasingly factors into business decision-making, even in sports. When Oakland Athletics manager Billy Beane started using big data analysis to drive front-office decisions, he didn’t win instant admiration. But today Beane is not only revered as a baseball guru; he has also found success off the field as a board member of NetSuite, a position he’s held since 2007.
Like it or not, big data is here to stay. In business, the question is how you can you best apply big data to various realms. In other words, what are the big ideas when it comes to big data?
Let’s take an example from the 1999 MLB draft, where you would have had a choice between two players. The first was the Arizona high school player of the year and had just put up a .560 average, 22 home runs, 14 doubles and 81 RBIs in his senior year.
The second player was putting up decent numbers in the minors, but a scouting report worried that he had a “heavy, bulky body” and “tends to be a hacker” at the plate. The data clearly tells you to pick the first player, right?
But the second player, who was selected in the 13th round of the 1999 draft, turned out to be three-time National League MVP and future Hall of Famer Albert Pujols. The first player – and fourth overall pick from that year – was Corey Myers, who never made it to the show.
These errant draft picks were based on a too-limited selection of stats. Had social media existed back then, we might have had some “unstructured” insights about Pujols’ ability to hit in the clutch or make adjustments in between at-bats.
You’ve also got to ensure you have the right people analyzing the data and using it to generate relevant insights for the business. That means skilled analysts who have solid technical knowledge and clear business perspectives, who understand the areas where data-driven insights are actionable and can provide tangible business improvements.
Optimally, a combination of people, process, technology and strategy is necessary to address big data challenges and seize the big data opportunity. In baseball, that means having a general manager with the skills and knowledge to identify the right metrics for players who can help deliver the best return in terms of generating the most wins per resources invested. Increasingly, every Major League Baseball franchise collects ample data, but it’s the teams that best use the data that succeed.
Finally, you’ve got to adapt and look for more new ways of generating actionable insights. As recently reported, MLB will implement revolutionary camera technology to capture every action at the plate and on the field, allowing front offices to analyze brand new data streams in search of a competitive edge.
As once-overlooked metrics like on-base percentage have now become widely used, Beane says it’s critical to seek value elsewhere.
“The idea that you can create a template that will work forever doesn’t happen in any business,” he said.
To truly thrive, you may have to look to the metrics your competition is ignoring. In other words, big data is an ongoing pursuit, not a one-time destination.
Big data isn’t just a quirk of America’s favorite pastime – it’s an essential component of the best business intelligence practices across industries. So even if you choose to just sit back and watch the action unfold this season, without going deep into the box scores and SABRmetrics, you can apply some of baseball’s business intelligence and big data innovations within your organization.
After all, there’s no such thing as fantasy business.