Mar 04 2017

Sloan 2017: How Teams Are Making Sense of Data to Drive Improvements

The Oklahoma City Thunder integrates data from numerous streams to deliver insights and efficiency.

Sports teams know they have a lot of data, but it takes a smart strategy to get from owning data to using it effectively to improve operations.

The Oklahoma City Thunder were swimming in data coming from various streams, but the NBA team didn’t have a way to make sense of it all. The team had numerous Microsoft Excel spreadsheets, but they weren’t managed well, and the team couldn’t correlate the data in those spreadsheets to gain insights such as how weather affected ticket sales.

“We realized that we were capped,” said Karlis Kezbers, the team’s director of business intelligence and ticket strategy, during a presentation at the MIT Sloan Sports Analytics Conference in Boston. “We couldn’t grow as an organization in how we used data.”

Making Sense of a Deluge of Data

To address this situation, the Thunder implemented a data management system to improve how it used the data it had. The system included:

  • A customer relationship management solution that integrated data from numerous streams
  • A central data warehouse to store all this data
  • A data visualization engine to provide decision-makers throughout the organization with actionable insights based on the data

The system combined information from more than a dozen sources, including email, Excel and ticket data. It also created an executive dashboard that gave team personnel a comprehensive view of various operations, such as ticketing, television ratings, and sales of merchandise, food and beverages.

“This gives you a quick look at how our business is doing this season,” Kezbers said.

It also enables users to drill down into specific areas or even look at specific days. The system confirmed for team officials the things Thunder staff were doing right. It also generated questions about how the team could improve operations.

Using Data to Boost Customer Service

For example, the system created a seat map with data about season ticket holders aimed at helping members of the sales staff strategize their efforts. They can explore the data to see detailed information about individual season ticket holders and gain insights about how to provide them better service.

“It’s helping us make a customized experience for our customers,” Kezbers said. The team also uses data from its resale marketplace to get a better understanding of the market and provide leads to sales representatives.

In the future, Kezbers expects the system to enable the Thunder to shift from being a reactive organization to a proactive one, utilizing predictive analytics as well as post-analytics.

“We keep trying to move forward, opening new doors and trying new things,” Kezbers said. “We’ve been able to evolve and adapt.”

To learn more about how CDW can help your team make better use of its data, visit

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