EP12 Why Marketing Data Goes Wrong (And How to Fix It)

Chapters

• 00:00 Intro

• 01:10 Thierry’s unconventional path into data

• 03:48 Becoming a “data translator”

• 06:54 Why → How → What storytelling framework

• 11:03 Stop stereotyping stakeholders

• 18:14 Why Thierry did stand-up comedy

• 24:04 “Pursuit of imposter syndrome” mindset

• 25:24 How big brands misuse data

• 30:30 Removing bias from data

• 34:56 Fixing broken reporting in agencies

• 43:02 Moving from Specialist to Marketing Leader

• 47:00 Stakeholder storytelling and unlocking budget

• 51:40 Using trend data to shape product strategy

• 55:55 Why Six Chillies was founded

• 01:00:00 Buying a car entirely through ChatGPT

• 01:04:10 AI, trust, and the collapse of certainty

• 01:06:30 Why taste will outlast automation

Data isn’t the problem. The way marketing teams use it is.

Over the last decade, marketers have been told to collect as much data as possible. As Thierry explains:

“Over the last 10 years, we've been told that data is the king and queen of everything… collect as much data as you humanly can and it will objectively tell you exactly where you need to go.”

But the result has not been clarity. It has been fragmentation and overwhelm.

“What’s quickly happened is this gap has occurred where the individuals who are technically involved within the data are on one side and then you have the individuals who want to make decisions… on the other side and that gap's actually grown.”

This aligns with long-standing research. As Herbert Simon famously observed, “a wealth of information creates a poverty of attention.” When attention collapses, decisions stall.

The Real Issue: Data Translation

Thierry does not position himself as the best analyst or the best copywriter. He positions himself as the bridge.

“I sit in the middle and I bridge those two things and I call myself a data translator. Ultimately I help individuals cross that bridge regardless of which direction they're coming from.”

This concept mirrors what MIT Sloan Management Review describes as the rise of “analytics translators” — individuals who connect technical capability with commercial application.

Why Marketing Data Goes Wrong

1. Fragmented Teams, Fragmented Truths

As brands scale, silos emerge.

“The SEO team has a complete other understanding of their audience… paid social will see a completely different audience… and so then what you end up having is different strategies built for different audiences.”

Without shared interpretation, data becomes a set of competing narratives rather than a unified source of truth.

2. The Confidence Gap

Misuse is not always technical. It is psychological.

“Another thing I would say would be a confidence gap… ‘I’m not a data person’… that confidence bit is actually quite detrimental.”

Teams defer to analysts instead of interrogating insights. The result is passive acceptance rather than critical thinking.

3. Overloaded Reporting

Glen describes the common experience of bloated dashboards:

“It’s like 15 pages long and it takes like 5 minutes to load and then crashes.”

When reporting becomes performance theatre, stakeholders disengage.

The Fix: Why → How → What

Thierry adapts Simon Sinek’s Golden Circle framework into a practical reporting structure:

“Why should I care? How did we get here? What do you want me to do about it?”

This reframes reporting from descriptive to decision-led:

• Why does this matter commercially?

• How did we arrive at this conclusion?

• What action should be taken?

Research from Harvard Business Review supports this structured communication approach, highlighting that clarity and credibility drive executive trust in analytics.

AI, Trust, and Taste

The future complicates this further. AI accelerates output but introduces new risks.

“For me it’s trust and taste… we’re already in the realm now where trust is a massive factor when it comes to a data perspective when AI is also within the conversation.”

Fragmented privacy landscapes have already created blind spots:

“Our data has become extremely fragmented...”

As institutions question authenticity and synthetic content proliferates, the differentiator becomes judgment. Not automation. Not volume. Judgement.

The Leadership Shift

Progress requires discomfort. Thierry frames growth as intentional exposure:

“I’m always in the pursuit of imposter syndrome. The moment I don’t feel it, I think I’m in the wrong place.”

The transition from specialist to leader is not technical. It is communicative. Data becomes influence when it is structured, contextualised, and tied to action.

Conclusion

Marketing data goes wrong when:

• Teams operate in silos

• Confidence gaps suppress critique

• Reports overwhelm instead of guiding

• AI output replaces human judgment

It goes right when:

• Interpretation is centralised

• Stories are structured

• Decisions are explicit

• Trust and taste remain human responsibilities

If your dashboards are not influencing decisions, they are not working.

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