• Ujwal Arkalgud

Artificial Intelligence can make observational research even more powerful

On this day, 15 years ago, I started my first immersive ethnographic research project. I was still in university, shadowing an incredible anthropologist and teacher, following and observing the lives of tribal people in the hills of southern India. Fast forward to today, I can still remember the rush I felt the first time I realized the power of pure observation and the induction method of research (as Franz Boas would call it) rather than the commonly practiced method of deduction. So it’s likely not a surprise to you then to learn that from the first day that we set out to create a technology that would leverage big data and AI to scale observational ethnography, I was hell bent on ensuring that the power of pure observation was not lost in the process of creating machine-enabled ethnography.


We launched our technology, MotivBase Trends, just over a year ago after a lot of research, development, testing and validation. I’m proud to say that not only have we ensured that the richness gathered from the observational method has been carried well into this technology, but we’ve also managed to benefit from the sheer scale of data to do much more with ethnography than we previously imagined.

Namely, being able to model data to quantify a culture (how many people care about something a certain way), predict where it’s headed, and determine its maturity on the adoption curve.

Interestingly, the value of scale has benefited us even more than we had previously imagined because we focused so heavily on building a machine that understood context (like an ethnographer), so it could capture the natural and organic ways in which people give meaning to the topics they discuss. That is, our machine learned to understand the contextual boundaries around a search topic so it could capture and quantify the different related topics that people naturally link to the underlying search topic. Instead of losing out on the value of the observational and inductive method of research, we ended up with a technology that not only enables it, but also enhances it.


After all, culture is nothing but the combination of meanings that human beings create around topics and ideas through the natural interactions they have with one another. Today, these types of interactions happen more so online than they do offline. And whenever that happens, we’re there to capture and model the outcome so smart researchers can do their jobs better than they’ve ever been able to in the past.

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