Sessions | Dialogues
library ⋅ Afternoon
Bangalore International Centre
Every chart highlights something. Every dashboard prioritises something. Every metric leaves something out. This dialogue asks the uncomfortable question behind all of it: what worldview are you building into your visualisation, and what are you nudging decision-makers to ignore?
Lakshmi Chockalingam
Doctor of Education Candidate·Bath
Every chart highlights something. Every dashboard prioritises something. Every metric leaves something out.
This dialogue starts from that discomfort. Before colour palettes and chart types, there are deeper choices: what counts as success, what gets measured and what does not, what assumptions are built into our models and proxies, and what forms of value get reduced to a number because they are easier to plot.
The session opens with a few real-world chart examples that look correct but embed strong assumptions. What story does each visualisation make obvious? What alternative stories disappear?
Small groups then dig into those questions through their own practice, using prompts like: a time when a visualisation changed a decision, a metric you felt uneasy about using, something important that could not be visualised. The goal is not consensus but surfacing the interpretive frames practitioners carry into their work, often without examining them.
The session closes with the room building a shared reflective toolkit together: a short checklist of questions to ask before the chart gets made, not after.
Data visualisation is powerful because it simplifies complexity. But simplification is never neutral. This dialogue is for anyone who wants to ask not just "is this chart clear?" but "what worldview does this chart assume?"
Lakshmi is a software engineer turned social impact professional whose work sits at the intersection of data, technology, education, and entrepreneurship. Having worked across India and the UK with government and third sector organisations, she brings a systems lens to questions of development and representation.
Now as a doctoral researcher, Lakshmi engages deeply with what data gets measured and whose knowledge counts. Her work attempts to interrogate (if not at least acknowledge) what is left out and whose realities are flattened in the process. Through this, she nudges toward more reflective, responsible, and context-sensitive approaches to both data and development, inviting researchers and practitioners to rethink (or at least surface) the assumptions embedded in how we see and shape the world.