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 irreplaceably valuable, made so because of 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. Data requires additional work before it becomes extremely valuable.

The key to finding and making use of this information requires what Kazerouni called “building your differentiator.” This means finding a niche that can set a company apart from its competitors, and really investing in that. That could mean buying technology or building it internally. A company with power in-house and a larger team would make a different decision than one entering machine learning and AI for the first time. If the company has a limited set of resources, build what is going to be the differentiator — spend resources on something that other companies are not spending their resources on. That is the best way for a company to focus their efforts because that is why is going to elevate the user experience, drive efficiency, and be different from the rest of the market.

“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 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 being compliant with the 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 there is a computational and resource cost to good data privacy and protection, and is willing to spend because it is critical to keep the consumer’s trust. “It’s important to incur that cost early, and not shortcut around that,” Kazerouni said.

This includes investing early in data engineers who help with the extract, transform, and load (ETL) pipeline and maintain the technical chain of custody, so that 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. Investing in the foundational, building blocks is necessary.

However, contextualizing that data into actionable goals isn’t just for the data analysts and statisticians. Orangetheory works with kinesiologists and a Medical Advisory Board — professionals in the fitness field — and includes them in every decision relating to template design and any fitness and workout related claims.

“We’ll always have a human in the loop,” Kazerouni said, noting that it was important to have someone involved in making decisions on how wearable data and workout data should be used. “[There’s value] in having an exercise physiologist or kinesiology, 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 — a totally new environment with no legacy code and developing from a clean slate — so the company is in the process of making decisions about whether to build or buy its data infrastructure, where to invest the resources, and how to use the data. There are questions to decide whether prescriptive or predictive analytics would be the most valuable. The team is considering whether it would be possible to prescribe action and strategy based off of the insights available from the data, whether the data can be used to predict schedule optimizations and demand, or even how to tackle the the supply chain for the wearables themselves. “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 that Orangetheory set up for itself has 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 this emerging change- of virtual presence and remote accessibility is here to stay. “The focus is more 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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