I distinctly remember the day I discovered information design as a field. I can recall every neuron in my brain lighting up and feeling fuzzy—the kind of feeling you get when you enter flow state. I've always grappled with the fact that I am truly a generalist, but information design seemed to reward those with range. It is a field that requires one to be curious, ask questions, perform research, architect information, communicate insights, and visualise data intuitively. And given my career has spanned science, communications, and design over 8+ years, my hodgepodge of a skillset ticked these boxes. It didn't take me long to proclaim to the universe that this was the field I wanted to set up camp in.

However, information design is still in its nascency in India. Landing a purely information design-only role can be close to impossible. That's why I don't take it for granted that a few months after my proclamation, I chanced upon an opening for a designer at a brand new Indian information design agency—Revisual Labs. What made it all the more better was that it was founded by Gurman Bhatia, an information design leader whose data stories I deeply admired. The job description was completely aligned with my goals at the time, and even though it requested a short email, I sent them a long(ish) one. Two weeks later, on 2nd October 2023, I started my Monday morning being added to Revisual Labs' Slack. And set up camp, I did.

Two years later, in September 2025, I had the privilege of introducing 22 B.Des. Graphic Design students to the world of information design at the National Institute of Design (NID), Ahmedabad.

Here are some reflections from two weeks of my time as a visiting faculty:

Graphic design students are predisposed to being good information designers

On day 1, I conducted a short "encoding drawing" session, inspired by one that Gurman once ran at our studio. It presented the students with a very simple dataset:

Number of parental leaves given per year (in days)

My ask to the students was simple—spend 5 minutes on pen and paper sketching as many ways as they could think of visualising these numbers. Even though the majority of my batch stated that they hadn't worked with, read, or made a lot of charts before, the visualisations they whipped up were incredibly intuitive. Many of them ended up making quintessential charts, unbeknownst to them that these were actual chart types. This was a great litmus test—it let me engage with them over the next few days with a lot more rigour and confidence.

On day 2, after a session on data structures and visual encoding, we attempted a slightly more elevated exercise. The batch was split into groups and they were requested to visualise this dataset by choosing the best possible visual encodings:

They were requested to not use AI assistance or look up visual references on the internet. Here are some of their outputs at the end of 30 minutes:

We did a show-and-tell, critiquing all groups' visualisations to understand what worked well and which redundancies could be eliminated. But overall, they did a fantastic job! As graphic designers, their foundational understanding of space, layout, Gestalt principles, and colour all translate incredibly well into information design.

Design students are VERY keen on learning non-design tools as well

While preparing to teach this course, I faced a serious conundrum—which tool do I introduce the students to for data wrangling? Using spreadsheet tools was out of the question—it would've been too difficult to trace their workflows and troubleshoot, given these tools modify the original dataset and do not document any pipelines. R would've been too complicated given it is a code-based tool, and nor am I an R expert. I needed to use a tool that'd be intuitive, easy to pick up, traceable, and open-source.

At the suggestion of my incredible colleague Aman Bhargava, I chose to use Orange Data Mining to teach my students how to wrangle data and create summary statistics. Orange bridged all the gaps I was concerned about. Given it is a visual programming software, it maps out all workflows and pipelines (which would help me troubleshoot problems), and it does not modify the original dataset, but simply transforms it throughout a workflow.

I could go on and on about the tool (I'm planning on writing a dedicated post soon!), but let me circle back. Now that I had my choice of tool, new questions made me nervous—would they find the tool boring? Would they disregard its importance? Scratch that, would they even care about data wrangling and summary statistics in the first place?!

But on day 3, they left me stunned. Our curriculum for the day included:

• Why create data summaries?

• Calculating basic statistics to create summaries

• Introduction to Orange

• Demo 1: Heart disease dataset to create a simple summary

• Demo 2: Iris dataset to demonstrate more Orange operations

• Self-led activity: Penguins dataset

  • Examine data

  • Ask research questions

  • Create a data summary

  • Arrive at a simple story

• Exercise: Practice worksheet (Scooby-Doo dataset) | Credits: Aman Bhargava

After completing the exercise, I requested them to break for lunch at 1:30 pm. Much to my surprise, none of them left voluntarily. They were so engrossed by this new tool they were learning, that they wanted to figure out every single operation possible. They wanted to create more visualisations. They wanted to establish more correlations. They wanted to add more widgets; link them with more nodes. They wanted to create many more summary statistics, given the Scooby-Doo dataset was quite extensive. They wanted to stay and figure out this tool and all its nitty-gritties!

I did NOT expect such positive and instant tool adoption. It goes to show that students like to take up a challenge, even if the tool is from a domain that is far removed from theirs and has little implication on their design practice. But this is what delighted me most about their time using Orange over the next few days: Each of their thought processes were uniquely mapped out on the canvas by how they chose to arrange widgets and nodes. It was almost like a self-portrait of how each of their brains functioned. Some of them were pedantic and clean; some were messy yet ambitious; some had way too many checkpoints to verify their data tables after performing a function; and some were laid out the way they because, well, they looked pretty!

Here's what the Orange canvas of one of my student groups (Apurva Rouduri, Gauri Bandevar, Tiya Wagh, and Manuel Joseph) looked like at the end of their course project:

By the end of the course, they were all so adept that using Orange, that they could very well have taught their peers. They learned how to read and understand feature statistics, pivot tables, edit domains, merge data tables, "group by" to aggregate data and create summary stats, and create visualisations + export them as SVGs for aesthetic styling. Their interest and willingness to learn Orange was one of my biggest highlights from this teaching experience.