Jonathan Carrigan manages one of Maple Leaf Sports and Entertainment’s most important teams.
Although properties owned by Canada’s largest sports conglomerate include the Maple Leafs, Raptors and Toronto FC franchises, Carrigan oversees the company’s efforts in a whole other playing arena: big data.
In a keynote at Tuesday’s Big Data Toronto conference, Carrigan drew parallels between managing a sports team and building an effective analytics unit at a business.
“I’m not a data scientist. I’m more of a coach. My job is to provide a strategic framework for the (analytics) team to unite them in a common purpose,” said Carrigan, MLSE’s senior director of business intelligence (BI) and platforms.
He said his team’s goal involves “leveraging advanced analytics to revolutionize the fan experience and our business.”
As we recently reported , IBM has built a digital War Room for MLSE so the Raptors can use analytics to create statistical profiles of opposing players and scout talent for the National Basketball Association (NBA) draft.
In the most recent NBA season, the Raptors put up some impressive numbers while making their first ever trip to the third round of the playoffs. MLSE’s analytics team, however, has its own stats worth boasting about, too.
“It’s to the point where we can predict within an accuracy of one per cent who’s likely to renew (their season’s ticket subscription) and who isn’t,” Carrigan said.
Now that MLSE is four years into its analytics program, Carrigan told the conference crowd his company is able to hit replay and review how well its data strategy is going so far. Based on some of the shots that didn’t quite land in the net, here are his tips for what not to do when crafting your big data playbook.
Overlooking key players
It’s tempting to focus on the data superstars within your organization who already ‘get’ analytics, like data scientists and senior IT people. But that doesn’t help the players in your business units who need more coaching in order to understand how analytics works and why it’s useful to them. It’s a mistake MLSE made early on, Carrigan acknowledged.
“HR was the number one stakeholder we failed to engage in the process,” he said.
As time went on, MLSE realized it needed HR’s buy-in and collaboration to spot and fill key talent gaps in its analytics program. Without the participation of HR and other divisions from day one, MLSE couldn’t meet the goal of becoming data-driven throughout its entire organization.
“This is not an IT project or BI project. It’s a corporate cultural transformation project that has to involve the entire corporation,” said Carrigan.
Skimping on equipment
If you want your business to be data-driven at every level, you have to equip staff with tools to make that happen. MLSE has started creating self-serve dashboards so users in various business units can quickly access data that really matters to them.
“These are easy to use, non-technical tools so they can start to integrate data insights into their part of the business every day,” Carrigan said.
Neglecting people and processes
That being said, simply giving your players the best skates and sticks won’t turn them into scoring machines. After investing in analytics technology, support the people and the processes in a “holistic way,” Carrigan recommended.
“As my wife will tell you, you can fill your garage with tools but unless you have somebody who knows how to use them with a plan for what to do with them, not much happens. So think about what the people in the organization need to put some of these new technologies into action.”
According to Carrigan, MLSE probably should have made sure its data was a little more prepared for analytics before it jumped into the game.
“We got so caught up in the vision and putting in technologies and really didn’t have a full understanding of the scope that was required to get our data ready to plug in those tools and make them work.”
To fix that, MLSE had to do a “massive data cleanse” of “years of data,” he recalled. “I’m telling you, it was painful. Take the time to understand the current state of your data.” He said that includes figuring out where your data resides, deciding how you want to integrate it and putting some sort of data governance framework in place.
Watching the clock
Don’t focus on one game without also taking the long view of your entire season. Carrigan said his team has similarly realized that being data-driven “is not a project. It’s never going to end. It’s an ongoing process.”
As he explained, MLSE spent the first couple of years of its analytics program “just kicking the tires” and talking to various vendors to understand the technology and how it could fit into their business model and industry.
The past few years have been more focused on cleansing and readying data, building the BI team and identifying opportunities to start applying data insights to various parts of MLSE. That’s a tall order considering that MLSE’s empire also includes Rogers Centre, Air Canada Centre, Leafs TV, NBA TV Canada, plus various restaurants and retail stores.
Missing chances to score
Carrigan gave examples of how MLSE constantly looks for places to extract value from data, even in what seem like the tiniest corners of its business. To predict things like ticket and merchandise sales for an upcoming home game, the company takes into account data such as its own team’s performance, the opposing team it will play, historical sales figures, the day of the week and starting time of the game.
They’ve actually run the numbers to decide which concession items to offer and where to list them on the menu.
“It was actually beer and popcorn that turned out to be more relevant to our fans,” Carrigan revealed.
Food for thought, whether you’re crunching numbers or popcorn.