Every chart simplifies. The question is whether the simplification serves the reader or obscures what they most need to know.
Healthcare data in India is a black box. The cost of chronic disease is buried in claims forms and scientific reports most people will never read. And even when data is available, the language tends to be cold, clinical, and shot through with fear mongering that makes people look away rather than engage.
Plum processes over 125,000 health insurance claims, 100,000 tele health consultations, and 25,000 health checkups every year. That is a lot of data. DataLabs is their attempt to turn that into something the people it is actually about can use.
This talk is about the craft behind that work. Saurabh will walk through the full editorial and design process: how they decide which stories to tell from a dataset full of possibilities, which chart types carry complexity without overwhelming, and where a simple table does the job better than any visualization.
You will hear why they chose horizon charts over bar charts, why violin charts work for length-of-stay data, and what interactive elements help readers who have never encountered certain chart types before. You will also hear about the quieter decisions: qualitative research to catch narrative bias before it reaches the reader, privacy guardrails when working with real patient data, and the editorial calls about which data points to cut because they would overwhelm rather than illuminate.
DataLabs is not a dashboard. It is an editorial initiative built on the belief that health data should serve the people it describes. The craft is in making complexity feel human rather than intimidating.