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Orangetheory sees AI data governance as a ‘force multiplier’

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Ameen Kazerouni, chief analytics officer at Orangetheory Fitness, is well-versed in AI. His experience working as the head of AI and machine learning at Zappos has helped him successfully utilize AI and data at Orangetheory.

At VentureBeat’s virtual Transform 2021 conference, Kazerouni compared data to oil: Both are made valuable through a refining process.

“There’s this evolution that data goes through where it goes from data to knowledge and to insight,” Kazerouni said. “When you collect data, it’s serving a purpose. And then understanding what that data means and how it relates to other pieces of data is critical.”

In other words, data isn’t useful until it’s contextualized. Companies have realized that data is important and are very diligent about collecting every piece of information they can, Kazerouni said. When one piece of information is seen in conjunction with other pieces of information, it serves as a “force multiplier” that allows businesses to identify correlations and confirm hypotheses about what is happening within the ecosystem. Businesses can make informed decisions derived from data that can dramatically change how consumers experience products, Kazerouni said. But data requires additional work before it becomes extremely valuable.

The key to finding and making use of this information is what Kazerouni called “building your differentiator.” This means finding a niche that can set your company apart from its competitors and really investing in that. This could mean buying technology, building it internally, or some combination of the two. A company with in-house resources and a larger team would make a different decision from one entering machine learning and AI for the first time.

“If you’re trying to build something that somebody else is building as their core product, you will never be able to focus on it as much as a person [who] just does that and nothing else,” he said.

Data governance is critical

For Orangetheory, that effort is directed toward science and AI-informed fitness for its consumers. The company invests in analytics and data governance and places significant trust in the process. It’s important to understand how data is classified and to clearly define what each piece of data is used for and what level of protection is required. There are a lot of regulatory frameworks out there, and focusing on compliance with major data regulations puts the business in a “very strong position to say that the right types of data are protected with the right protocols and the right best practices,” Kazerouni said.

From a technical perspective, Orangetheory encrypts data in storage as well as in transit, keeps personally identifiable information in isolated environments, and makes sure the PII does not get replicated into analytical environments. The company recognizes the computational and resource costs of good data privacy and protection but sees these as critical to keeping the consumer’s trust. “It’s important to incur that cost early and not shortcut around that,” Kazerouni said.

This includes investing in data engineers who help with the extract, transform, and load (ETL) pipeline and maintain the technical chain of custody so the data becomes available in a “very usable and secure fashion,” Kazerouni said. Data analysts, statisticians, and business intelligence analysts focus on actually deriving insight and telling stories from the data in a way that’s consumable and actionable. Automated machine learning comes after that, and companies need to invest in the foundational building blocks.

The work of contextualizing data into actionable goals isn’t just for data analysts and statisticians. Orangetheory works with kinesiologists and a Medical Advisory Board — professionals in the fitness field — and includes them in every decision around template design and fitness and workout-related claims.

“We’ll always have a human in the loop,” Kazerouni said, noting that it was important to involve experts in decisions about how wearable data and workout data should be used. “[There’s value] in having an exercise physiologist or [kinesiologist], someone who’s well trained in that, to review what’s coming out of the data before you push it out and not trying to just automate that.”

Early in the AI journey

Kazerouni described Orangetheory as a greenfield environment — developing from a clean slate, with no legacy code — so the company is in the process of making decisions about whether to build or buy its data infrastructure, where to invest resources, and how to use its data. The team also has to decide whether prescriptive or predictive analytics would be most valuable. They are considering whether it would be possible to prescribe action and strategy based off insights available from the data, whether the data can be used to predict schedule optimizations and demand, and how to tackle the wearable supply chain. “The lessons are really the prioritization of what you build, what you buy, how you bring these assets online, and in what order,” Kazerouni said. “I’m sure we will make mistakes along the way. And probably next year, we will tell you all about them.”

The technological building blocks Orangetheory set up for itself have led to strong gains, even during the pandemic. The company’s interactive (and remote) Orangetheory Live workout, “a child of innovation [and] circumstance,” was a success, thanks to its robust digital platform.

Kazerouni is confident that the shift to virtual presence and remote accessibility is here to stay. “The focus is more on life, which is what we try to create … the ability to do more when you’re staying healthy. The hybrid approach that’s emerging is great because it all results in more life.” Kazerouni’s statement rings true for both the technological innovation and the AI-powered learning that comes with it.

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