It is usually well-intentioned. Sometimes urgent. Occasionally casual. And almost always revealing.
Because what is being asked for is rarely data in the raw sense. What people are often pointing to—without realising it—are outputs that look clean and legible: a map, a diagram, a timeline, a single graphic that seems to explain something complicated at a glance.
What is often misunderstood is that these are not datasets. They are the beginnings of a way of interpretation. After what might seem like a million hours pouring over information.
From Fragments to Meaning
Most of the work at Mod Foundation happens long before anything looks “presentable.” We rarely begin with neat spreadsheets. Instead, we work through fragments—government datasets that don’t align, policies that contradict each other, maps drawn at different scales, PDFs that refuse to speak to one another, and site realities that do not match official records. We often find ourselves reviewing archived or long-forgotten documents, sometimes decades old, meticulously tracing them forward simply to keep an analysis alive. Alongside this sits anecdotal knowledge—frequently dismissed, yet repeatedly proving to be disturbingly accurate.
Making sense of this material is not an act of formatting. It is an act of judgement.
We spend months—sometimes years—working across hundreds of datasets and sources, testing assumptions, discarding far more information than we keep, and debating internally what matters, what misleads, and what must be held lightly. Only after this slow work does something emerge that can be drawn, mapped, or explained.
When you see a single, “simple” graphic, you are not seeing the data. You are seeing the first stable story the data is able to tell.
Ways of Seeing
Data, on its own, is just that—data. Cities are already overflowing with it. But data that does not point to something, that is not used as an instrument of change, does not reveal relationships or raise better questions, is of limited value. What we do is analyse that data—along with everything that refuses to behave like data—to create meaning.
Graphics are not decoration; they are representational tools for seeing. They allow patterns to emerge from numbers, connections to be traced across disconnected sources, and relationships to be understood even when datasets refuse to correlate cleanly. They help us look through the noise—across formats, time periods, and institutional silos—and begin to see the city as a system rather than a collection of parts.
This is why we are often uneasy when asked to “just share the data,” as if the graphic were a superficial layer placed over a stable, objective core. In reality, the ‘pretty looking graphic’ is the work. It is the moment where interpretation happens—where disparate information is structured, prioritised, and made legible enough for others to engage with
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Why It Looks Easy (and Isn’t)
Good analysis hides its own labour. When it works, complexity disappears. Relationships feel obvious. The output looks calm, even elegant. That elegance creates a dangerous illusion—that the work must have been quick, straightforward, or easily replicable.
It isn’t.
A diagram that takes seconds to grasp often carries months of unresolved uncertainty behind it.When complexity disappears without distortion, we know we’ve done our job. And this ‘simple’ job is what the team does on a day to day basis. A map that feels intuitive may sit on top of countless decisions about scale, omission, sequencing, and emphasis. Analysis is never neutral. What we choose to show—and how we choose to show it—shapes how a city is debated, governed, and remembered.
Cities Need Fewer Answers, Better Questions
Our cities today are saturated with information but starved of meaning. More portals, dashboards, apps and open datasets have not necessarily made cities more understandable. Often, they have made them noisier.
Much of Mod Foundation’s work is about slowing down—asking better questions about water, mobility, land, governance, access, and equity, and then building tools that allow those questions to be explored collectively. Our graphics and frameworks are not conclusions. They are starting points—ways of seeing the city more clearly so that more informed conversations can follow. These ‘follow up conversations’ make us very happy.
In ‘beginning to understand’, we have a small request. The next time you feel tempted to write to us asking for “the data,” pause for a moment. Ask instead: What do you mean by data? What question led to this analysis? What assumptions were tested? What was difficult to reconcile? What remains unresolved?
Because we don’t deliver data. We build understanding—and that takes time.
-By Nidhi Bhatnagar (Inspired (immensely) by Sonia Das )
–Illustrations by Intern Gouri Ammanagi
