Truth Before Meaning: Data, Storytelling & AI

Your data is worthless if you can’t tell the story. Particularly in the AI era. 

AI-powered analytics promise the world, but they’ll amplify errors as quickly as insights. In a marketing landscape where accuracy and speed are both non-negotiable, your team can't afford to scale mistakes.

Scott Taylor, a.ka. “the Data Whisperer” brings his drama-kid-energy-meets-enterprise-data-realism to warn marketers not just to trust data blindly—but to question the plumbing underneath.

“Agentic AI and vibe coding make data management even more imperative,” he says. “If your AI is trained on garbage, you can now make mistakes at scale in ways we never dreamed of before.”

"Truth before meaning," Scott Taylor warns, isn’t philosophy—it's survival, and a core concern for data leaders trying to build trustworthy systems in a world where speed trumps sanity. Focusing on truth before meaning highlights the critical importance of addressing data quality issues before implementing AI solutions.

You’re Not Bad at Data —You’re Losing the Story War

Data leaders learn SQL, not data storytelling. But when the meeting starts, execs aren't asking for code—they’re asking for clarity.

Scott regularly speaks to rooms full of data professionals. The most common response?

“This was therapeutic.”

That’s because data leaders are often outshouted by marketing, sales, finance—departments where storytelling is a prerequisite for success. “They’re better storytellers,” Scott explains. “And data leaders grow up on hard skills, not soft ones.”

But if you can’t communicate the why, your how won’t matter. And no, no one wants your life story—they want business value in 60 seconds or less.

What’s Your “One Number”? (Spoiler: You Don't Have One)

Scott shares the most common question executives ask before major client meetings: “How are we doing with them?”

Simple ask. Nearly impossible to answer.

Scott calls this the "one number" problem—executives want a clear metric, but inconsistent hierarchies and scattered identifiers turn that into a Swiss cheese situation. One product might have 50 different internal codes across 50 different systems. How do you roll that up?

These marketing data integration challenges require structured solutions.

Vocabulary, Voice, Vision: How to Make Data Make Sense

Scott's framework for turning chaos into clarity is essential for implementing successful data-driven marketing strategies.

• Vocabulary: Agree on what you call things. Customers? Partners? Accounts?
• Voice: Speak the why in a way business people can hear.
• Vision: Tie it all back to the strategy. No one cares how data gets managed—they care about how it gets them to the next milestone.

This isn’t fluffy consultant talk—it’s a survival tactic.

Sales Are Ruining Your CRM (And You Might Be Too)

“A lot of the bad data is coming from sales,” Scott notes. 

He’s not wrong. Whether it's salespeople fat-fingering new leads to hit quotas, or 23 different teams creating customers in 23 different ways, the result is the same: polluted systems and poor decisions.

Bonus confession: Scott deliberately corrupts badge data at conferences to get his title on his name tag. Guilty, but effective.

You Are Not Alone–Everybody’s Data is a Dumpster Fire

Scott says the most healing thing he offers isn’t a data solution. It’s empathy.

“Everyone thinks it’s just their company,” he says. “But enterprise data problems are more alike than different. Everyone’s got silos. Everyone’s got duplicates. Everyone’s got garbage.”

Scott’s message isn’t doom—it’s direction. Before AI transforms your marketing, you've got to transform your data. The payoff will be trustworthy insights at scale.

Because the only thing worse than garbage data is automated garbage data.

‍

Listen now: Spotify | YouTube | Apple

Connect with Scott: LinkedIn | Website

‍