INBOX INSIGHTS, August 6, 2025: T-Shaped Marketers, Dataset Validation

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INBOX INSIGHTS: T-Shaped Marketers, Dataset Validation (2025-08-06)

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Why Being a T-Shaped Person Matters More Than Ever

Last week marked exactly 10 years since I started working with Chris. Ten years. Yikes, where does the time go?

I remember being intimidated by pretty much everything in those early days. I was coming from this weird academic-wannabe-commercial space where I understood theory but had zero practical experience in the real business world. Terms that everyone else threw around casually? Completely foreign to me. And one of my first responsibilities was writing for the company blog on a regular basis.

As the person responsible for the output, Chris’s standards for what was “acceptable” for the company blog were really high. I was about a decade out of practice for writing anything original other than a project plan. The ironic part? I’d always wanted to write. In undergrad, I took every writing class I could get my hands on and absolutely loved the process. But once I started working full-time, those opportunities disappeared. Until I landed at the agency.

All of a sudden, my opinion mattered. I needed to have something to say.

So here I am, 10 years later, wanting to revisit one of my very first posts: the idea of a T-shaped person. The original post has long since disappeared from that company’s website (lesson learned about keeping offline copies of your work!), so I’m working mostly from memory here.

What I Thought I Knew About T-Shaped People

Back then, I probably wrote something along the lines of: “A T-shaped person is well-rounded in a lot of areas and deeply focused in one or two.”

Classic textbook definition, right? The horizontal stroke of the T represents broad knowledge across multiple disciplines, while the vertical stroke represents deep expertise in a specific area.

It turns out, I wasn’t wrong. But I wasn’t thinking big enough.

Why the Stakes Are Higher Now

Here’s what’s changed in the past decade: our industry has evolved dramatically, and the biggest shift is that being well-rounded isn’t just nice to have anymore – it’s absolutely crucial for survival.

With AI dominating conversations, tasks, processes, and the overall job market, flexibility has become your most valuable asset. You can’t just be the “social media person” or the “analytics person” anymore. Those narrowly defined roles are exactly what AI is coming for first.

The New T-Shaped Definition

If I had to redefine what it means to be T-shaped in 2025, here’s how I would break it down:

The vertical stroke (your deep expertise): Critical thinking
The horizontal stroke (your broad knowledge): Understanding all the areas that comprise a business

Let me explain why this matters.

Critical Thinking as Your Superpower

AI can generate content, analyze data, and even write code. But it can’t think critically about whether the output makes sense in your specific context. It can’t question assumptions, identify blind spots, or make judgment calls based on nuance and experience.

Critical thinking means:

Asking “why” and “what if” instead of just accepting information
Connecting dots across seemingly unrelated data points
Identifying patterns and anomalies that others miss
Making strategic decisions when you don’t have complete information

This is your insurance policy against automation.

Business Literacy as Your Foundation

The horizontal stroke isn’t about being mediocre at everything – it’s about understanding how all the pieces fit together. You need to grasp:

How marketing impacts sales (and vice versa)
Why legal considerations affect creative decisions
How customer service insights inform product development
Why financial constraints shape strategic priorities

When you understand the whole business ecosystem, you become the person who can translate between departments, spot opportunities, and solve problems that span multiple functions.

What This Looks Like in Practice

I’ve watched this play out countless times over the past decade. The people who thrive aren’t necessarily the most technically skilled in their specific area. They’re the ones who can:

Take a data analysis and explain what it means for the sales team
Look at a marketing campaign and immediately spot potential operational challenges
Understand why a “simple” website change requires input from five different departments
See connections between customer complaints and product roadmap decisions

These are the people who become indispensable – not because they’re irreplaceable in their technical skills, but because they’re irreplaceable in their ability to think across the entire business.

Your Action Plan

So how do you become (or stay) T-shaped in this new reality?

Strengthen your critical thinking muscle: Question everything. When someone presents data or makes a recommendation, dig deeper. What assumptions are we making? What aren’t we considering?
Get curious about other departments: Sit in on sales calls. Read customer service tickets. Attend finance meetings. Ask questions about how other teams measure success.
Practice translation: When you’re in cross-functional meetings, make it your job to help different departments understand each other. Become the bridge.
Stay learning: Not just about your specific area, but about business in general. Read about industries outside your own. Understand macro trends that could impact your company.

The beautiful thing about this approach? The more you understand about how businesses work, the better you become at your own specialized area. It’s not about spreading yourself thin—it’s about giving yourself context that makes your expertise more valuable.

And honestly? It makes work much more interesting when you understand how all the pieces connect.

What does being T-shaped look like in your role? Reply to this email or join our free Slack group, Analytics for Marketers.

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how the nonstop AI hype has buried crucial marketing updates and what you can do about it. You’ll learn why even Google quietly launched a powerful free tool that almost no one noticed. You’ll discover how much of your analytics data is silently vanishing every day. You’ll see why press releases and algorithms no longer reach anyone—and what actually works instead. You’ll find out how to build a human network that keeps you ahead of the noise. Watch the episode now to reclaim your edge.

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In this week’s Data Diaries, let’s talk about data validation.

One of the most important skills you can build in processing data is to understand correlation and pattern matching. Being able to put data sets side by side and see how they march in lockstep or drift apart from each other is an incredibly valuable and underrated skill.

This becomes even more important once you experience a data quality change. You’ve probably had the experience of seeing a data source decline in quality over time. If you’ve ever done any work in social media marketing, you’ve seen the quality of social media reporting go down. If you’ve ever done any work in web analytics, you have almost certainly seen the quality of your Google Analytics data decline.

However, understanding the scope and severity of a data decline is impossible if you don’t have something to measure alongside it. For example, if you’ve ever wondered about the impact of ad blockers on your website traffic, you would need to correlate that with actual page hits measured by your server itself. People can suppress ad and analytics tracking all they want – they still can’t suppress the fact that they are using resources on your server, and that is something you can measure.

This became a hot topic in the last week as the US federal government published employment data that made some headline news. Without getting into politics, one of the most important things you can do with that sort of data is cross match it against other data sources to see if the data source’s quality is changing over time. You would take unemployment data from the government and cross-match it against ADP payroll data or indeed.com job postings and look for correlations to see if the data source in question is beginning to drift away from established patterns.

In the past, this required some knowledge of statistics and good statistical software, but today you can do this with a prompt in a tool like Google Colab. You upload your individual data sets, write a detailed prompt in Google Colab, which is powered by Gemini, and it will do all the code writing to perform the statistical analysis and give you an answer.

The most difficult part is knowing what to ask for. And you can use regular Google Gemini or ChatGPT or any generative AI platform of your choice to develop the list of questions and statistical methods that would give you the greatest insight.

The key takeaway is to never trust a data set by itself. Always have some means of validating it against other similar data, so that you can tell if and when the data set in question starts to decline in quality or diminish. In terms of where to start? As a marketer, you should be looking at the correlation of all of the KPIs that you have in your customer journey, to spot drift and where data sources might be going awry.

Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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