The Data Team That Teaches Together
I Gave My First Webinar—And Something Clicked
Not long ago, I gave my first webinar. It was a session on introducing R to Excel users—a light technical demo, some live examples, a few “aha” moments.
I expected it to feel like a tutorial. What I didn’t expect was how much it would shift the way I think about working in data.
Something happens when you stop just doing the work and start explaining it—especially to people outside your usual toolset. In this case, I was presenting R to a group more comfortable in Excel. Different language, different rhythm. But once we connected a few dots—from tidyverse to pivot tables, from reproducibility to automation—I could see it land.
What started as a knowledge transfer turned into a mindset shift—for them and for me.
That’s when it hit me:
In today’s data teams, sharing what you know is no longer a side project—it’s part of the job. And more than that, it’s part of how teams grow, adapt, and evolve.
This article is about that realization—how teaching, mentoring, and building knowledge-sharing habits can turn individuals into communities, and work into movement.
The Myth of the Lone Genius (and the Cost of Hoarding Knowledge)
In the early days of any career in data, it’s easy to believe that success comes from knowing more than others. Becoming the one person who understands that messy model, cracked that SQL puzzle, or finally got the BI tool to cooperate.
It’s a seductive idea: the lone genius—headphones on, solving the hard stuff solo, irreplaceable because no one else can do what they do.
But here’s the quiet truth most experienced teams know:
That kind of knowledge is powerful… until it’s a liability.
Because when knowledge is hoarded, it becomes fragile.
The person who built the pipeline leaves, and no one knows how to maintain it.
The model gets better, but no one understands why it changed.
The script works, but only its author knows what all those cryptic variable names mean.
Silos don’t scale.
They slow teams down, break onboarding, and turn every project into a guessing game. And in a field where tools evolve monthly and teams shift constantly, what’s not shared gets lost fast.
Even worse? It breeds fear. People hesitate to experiment or contribute because they’re afraid of stepping on undocumented logic or sacred ground.
Knowledge hoarding used to feel like job security.
Now, it’s more like technical debt in human form.
Modern data work thrives not on solitary brilliance, but on shared understanding—what some might call open-source knowledge culture, applied internally. It’s not just about writing code that works. It’s about writing code (and thoughts, and workflows) that others can work with.
Teaching as a Superpower (Even if You’re Not the Expert)
There’s a quiet magic that happens the moment you try to explain something.
It doesn’t matter if it’s in a webinar, a documentation page, a diagram drawn on a whiteboard, or just an offhand “oh by the way…” in a team meeting—the act of teaching reveals the gaps in your own understanding.
And that’s not a weakness. That’s the power.
Most people think you need to be an expert before you teach. But in reality, teaching is what makes you an expert.
It forces you to untangle your own shortcuts.
t pushes you to make the implicit explicit.
It transforms instinct into something structured, repeatable, and shareable.
When I was preparing the R webinar for Excel users, I found myself reframing concepts I took for granted. Why do we use pipes? Why do we care about reproducibility? What makes mutate() intuitive once you “get it,” but weird at first glance?
The session was for them—but the prep was for me. And I left it with more clarity about my own tools than I’d had going in.
That’s the quiet return on investment when you teach in a data team. You’re not just adding value for others—you’re refining your own fluency.
And it doesn’t need to be a full-blown presentation. Some of the most impactful knowledge-sharing happens in:
a five-minute screen share
a comment in a pull request
a “here’s what I learned today” Teams or email thread
an ugly-but-honest wiki page
When knowledge is shared in motion, it spreads without formality or friction.
And when everyone on a team feels just a little responsible for teaching, something amazing happens:
The team starts teaching itself.
Internal Communities & Mentorship—The Real Engines of Team Learning
While teaching moments can happen spontaneously, teams grow faster when learning is intentional. This is where internal communities and mentorship structures come into play—not as corporate formalities, but as real engines for scaling knowledge without bottlenecks.
💬 Internal Topic Communities: Your In-House Open Source
Think of these as guilds, squads, or circles—organic spaces where people with a shared interest gather around a common tool, problem, or practice:
An R community that swaps packages, patterns, and ggplot quirks.
A dbt guild that aligns on model conventions and reviews PRs together.
A “data storytelling” circle where analysts share how they’re communicating findings in dashboards or decks.
These groups aren’t about hierarchy or performance—they’re about peer-to-peer learning and shared curiosity.
They create psychological safety for asking “dumb” questions (which are usually the smartest ones) and turn quiet team members into visible contributors.
In a way, they replicate the spirit of open-source communities—except the audience is internal. You’re not building for the world, but you are building for each other. And that makes the knowledge stickier, faster, and more aligned with the organization’s real challenges.
🧭 Mentorship: The Knowledge Shortcut We All Need
Formal or informal, mentorship is a force multiplier in data teams.
The best mentors aren’t just technical powerhouses—they’re navigators.
They help you:
✅ Understand how the team thinks
✅ Spot patterns in the chaos
✅ Avoid reinventing the wheel
And just as importantly, they show you how to share back what you learn.
Mentorship creates a chain reaction of learning:
A mid-level analyst mentors a junior on query optimization.
That junior later explains the same concept to a new hire.
Now the team has two people teaching what used to live in one person’s head.
Multiply that by a few topics, and suddenly you’ve got distributed resilience—not just expertise in isolated nodes.
Mentorship doesn’t have to mean long-term pairings or formal programs. It can be as simple as:
“I’ve done this before—want to pair on it?”
“Here’s a doc I wish someone had shown me earlier.”
“Let me know if you want feedback—I made that mistake last quarter.”
When internal communities and mentorship become the norm, your data team stops relying on tribal knowledge—and starts operating like a living, breathing learning system.
Be the Teammate You Needed a Year Ago
When we think of “impact” in data, we usually think in numbers: pipelines deployed, dashboards launched, models shipped, insights delivered.
But here’s a quieter metric—harder to measure, but just as important:
How much knowledge did you leave behind for others to build on?
Being generous with what you know—whether it’s through a webinar, a how-to note, or a Slack message that starts with “not sure if this helps but…”—isn’t a distraction from the work. It is the work.
When you take time to teach, you’re not just helping someone else catch up.
You’re also:
Reinforcing your own learning
Documenting the “why,” not just the “how”
Making your future self’s job easier
Contributing to a culture where nobody has to start from zero
In a field as fast-moving and fragmented as data, knowledge-sharing is the most sustainable form of velocity.
You don’t need to be a principal engineer or a staff-level scientist to have something worth sharing.
You just need to be one step ahead of someone else—and willing to turn around and say, “Here’s what I learned.”
So here’s a final thought:
Be the teammate you needed a year ago.
Or six months ago.
Or yesterday.
Because in the end, the best data teams aren’t just smart.
They’re generous.