Scott McKinley’s journey from professional cyclist and captain of the 1988 US Olympic Road Cycling Team in Seoul, Korea, to CEO and founder of data validation provider Truthset was more linear than one might think.
Cycling is “the ultimate test of truth,” McKinley says on this week’s episode of AdExchanger Talks. “You race 120, 130 miles and the first one to the other side across the line is the winner. It’s very pure, it’s very accountable, it’s very individual.”
But as doping became more prevalent in cycling in the 1990s, McKinley took it as his cue to quit.
“The cheaters arrived, and the drugs arrived, and I didn’t want to participate in that,” he says.
After retiring, he worked as a website manager for Cox-owned TV stations in the late ’90s before co-founding and running several measurement software and analytics companies and later joining Nielsen as EVP in charge of product innovation.
But by that point, roughly 18 years into his career, something had become abundantly clear to him: Digital advertising has a serious data quality problem.
“And I decided, basically, I’m either going to leave this industry, because I was so tired of the snake oil salesmen, the obfuscation and BS,” he says, “or I was going to create a company that tried to clean up the mess a little bit and give everybody a better shot at using data to predict who someone might be on the other end of a device.”
In 2019, McKinley founded Truthset, a startup that validates data sets for accuracy and quality.
According to Truthset’s analysis of public data marketplaces, the average accuracy of age data is 32%, meaning the majority of age-related data is wrong. Meanwhile, the average accuracy of gender data in publicly available segments is 61%, only a smidge better than a coin toss.
So why do advertisers keep buying this data?
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The ad industry’s “addiction to scale” at the expense of precision is one reason, McKinley says.
But there’s also a lot of “pretending” happening in the supply chain, he says, “what we love to call probabilistic modeling.”
“It’s basically a euphemism for pure guessing, right, with an incentive to maximize scale at any cost,” McKinley says. “Once you have those two variables, how can you possibly trust what comes out the other end?
Also in this episode: What happens when marketers use inaccurate data, McKinley’s unvarnished view on ID bridging (he’s, uh, not a fan) and why cycling wasn’t the best way to get girls in high school.
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