Learnings from Advanced Train The Trainer 2025: Why Evaluation Matters More Than I Expected

When I joined the Advanced Train the Trainer (ATTT) 2025 in Hyderabad, I was mostly excited about meeting other Wikimedians and learning something new. I chose the Evaluation, Impact, and Data Storytelling track because I’ve always wanted to better understand whether the work I do in the movement is really making a difference.

What I didn’t expect was how much this track would shift the way I think, not just about evaluation, but about the work itself.

What Changed for Me

Before this workshop, “evaluation” sounded like something technical, maybe something that had to be done at the end of a project, mostly for reporting purposes. But very quickly, I started to see it differently. I realized that Evaluation isn’t just about collecting data. It’s about understanding change and long term impact.

It’s about asking questions like:

• What did participants actually take away from this experience?
• What impact did this project have beyond numbers?
• Who was affected, and how?

And more importantly, it’s about asking these questions early; not after everything is over, but as part of the planning itself.

Learning to Tell the Story

One of the most valuable things I took from the track was the idea of storytelling through data. We learned how to create what were called “impact snapshots” — short, visual, meaningful ways to show the results of a project.

Instead of saying, “We trained 30 participants,” we’d focus on what happened next: Did they continue editing? Did someone start a campaign of their own? Did anyone step into a leadership role?

That shift – from what we did to what it led to – really changed how I view success.

Tools That Felt Accessible

We also explored some tools that Wikimedia already uses, like Superset, SQL queries, and stats.wikimedia.org. At first, they seemed a bit complicated. But as we learned how these tools could help answer real questions like how to keep editors engaged or measure the success of a campaign – they started to feel much easier to use.

It helped me see that you don’t have to be a data expert to do meaningful evaluation. You just need to be curious, ask the right questions, and know where to look.

Designing Madam Admin: Turning Learning into Action

One of the most rewarding parts of the workshop was working in a small group to design a real project from scratch. Our team created Madam Admin, a project focused on increasing the number of women administrators across Wikimedia projects.

We noticed that while more women are contributing to Wikimedia than before, very few are taking on admin roles or stepping into governance spaces. Madam Admin is our response to that gap – a mentorship-based project that supports women through the process of becoming admins, from training on policies to building confidence in leadership.

We used everything we were learning in the track, from logic models to evaluation tools to shape Madam Admin into something thoughtful and outcome-focused. We didn’t just ask “What can we do?” but “What needs to change, and how will we know if we’re getting there?”

What I’m Taking Forward

This track changed how I think about projects. I’ve come away asking better questions, thinking more deeply about outcomes, and feeling more equipped to tell meaningful stories — not just in reports, but in conversations, presentations, and community updates.

Most of all, I’ve started to see evaluation as something that’s for us — not just for forms or formalities. It’s how we learn, how we grow, and how we make our work more inclusive and intentional.

If you’re someone who organizes events, runs campaigns, or just wants to understand your impact a little better — I’d really recommend spending some time with evaluation. It doesn’t have to be complex. Start small. Start with a question you care about. And let the learning follow.