#118: The Quest for Usable Data
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Whether you're heading to Cannes or tuning in from afar, this webinar is your backstage pass to the mind of one of the industry’s most creative data thinkers. With that in mind, here’s a Quo Vadis “Freakonomics” story about activating advertising data. Read Justin’s book for the real deal.
The Quest for Usable Data
A Quo Vadis Short Story: What happened at ACME wasn’t just a tech upgrade. It was a mindset shift and then a cultural shift.
If data is the new oil, most advertisers are still drilling with soup spoons. At least, that was the case at ACME, a household-name CPG conglomerate that sells everything from paper towels to pet food. Its marketing chief, a seasoned executive named Pat, knew that digital transformation had become a mandatory checkbox in the boardroom. But when it came to actually using data to improve marketing, things got… messy.
Pat was always thinking about how to squeeze more juice out of ACME’s data. On her early morning daily jog in Central Park, she had philosophical thoughts like:
“If we have access to millions of customer records, behavioral signals, email addresses, product preferences, loyalty card scans, survey responses, and tons of past campaign data… what do we actually do with it?” If we had a magic wand, what would we do with it?
Most marketing executives say something like, “Let’s just activate it,” but what they really mean is: “I hope someone else figures that out.”
Pat was different. Her first attempt at data-driven marketing was to hire a cleanroom vendor. When asked by her superiors — “What the hell is a cleanroom?” — she’d give the clinical definition, saying it’s a privacy-safe software platform designed to match first-party data with media and measurement partners without leaking PII. After getting blank looks back, she usually leaned on analogies because that’s how marketing people like to understand their fast-changing world. “Think of it like a dinner party where no one knows each other’s names, but the host still makes perfect introductions.”
So, Pat did what many marketing executives do. She uploaded ACME’s “best” customer data into her cleanroom vendor’s tech and waited for magic. But magic, as it turns out, requires fuel. It felt like a moon landing — full of anticipation, ambition, and the quiet fear that maybe, just maybe, the rocket wouldn’t launch.
If her cleanroom could talk to Pat, it would say something like: “Your data isn’t a meal, it’s a little snack and mostly bland. And I don’t clean your data, I just match it. You want better activation? Then feed me better signals.”
That got Pat thinking, “Why do companies like ACME collect oceans of data, only to activate a puddle?” The answer lies in what economists call data friction. That’s the gap between data’s existence and its usability. In ACME’s case, they had millions upon millions of data points sitting in back office systems: legacy CRMs, customer complaint logs, product reviews, shop-keeping units in multiple languages, retail feedback loops, and campaign briefs written by interns three summers ago. And none of it is structured, so most of it went ignored and unused.
Enter the idea that changed everything: Semantic AI.
One of Pat’s data scientists—an intern, ironically—suggested applying machine learning to “read” unstructured data and extract marketing-relevant insights. The clever intern had come across a semantic AI tool that could crawl through ACME’s forgotten data and turn it into structured, meaningful attributes from attitudes and behaviors to affinities and intent signals. Suddenly, something a mundane as email marketing data wasn’t just a pile of emails. It was a map of household behaviors. The rocket had finally launched!
In a matter of weeks, her useless cleanroom went from famine to feast. The platform began matching audiences with higher precision, driving smarter targeting and more granular measurement. And like music to a marketer’s ears, ROAS doubled.
The goodness kept flowing in. It did not take long for ACME’s retailers to take notice, causing a flywheel effect of more incoming data. Even her CFO, who initially thought “semantic” was a skincare brand, began asking for dashboards and now wanted to know what else Pat had in her data pantry.
She told him, “Our new approach isn’t about software. It’s about the underused value sitting right on our table all along. It’s about closing the gap between what we have and what we use. It’s about an economic truth that says most companies like ACME don’t need more data, they need more meaning.”
As Pat would later tell audiences at AdTech Economic Forum and events across the adtech circuit, “What we did at ACME is the difference between data strategy and data theater.”
THE END
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