Beyond Big Data: The Art of Thick Description in Qualitative Research

Published on Monday, January 14, 2019
Last updated Wednesday, October 25, 2023
3 min read

Big Data has taken center stage in numerous sectors, from healthcare to economics, promising data-driven solutions to complex problems. But is this wealth of quantitative data truly effective without qualitative context? This article delves into the role of "thick description," an ethnographic research tool, in providing the necessary qualitative metadata that makes big data genuinely impactful.

What is Thick Description?

Ethnographic research goes beyond mere observation to deeply understand the various contexts embedded within any environment. It acknowledges that surface-level data—while valuable—doesn't provide a full picture. Contexts, whether cultural, personal, or related to hidden meanings and artifacts, often elude even those intimately familiar with their environments.

Geertz, a pioneering figure in ethnographic research, cautioned against relying solely on analytical methods used in mathematics and logic when interpreting human behavior. Ethnography aims to tell the nuanced stories that numbers alone cannot convey. It provides a richer, more meaningful lens through which to view the world.

Thick Description vs Metadata

Just as metadata is data about data, thick description is the deeper meaning behind an observation. It enhances data by adding layers of context, making it incredibly valuable to a wide range of professionals. For example, Baillie and Benyon's study on the role of technology in the home used thick description to uncover issues related to technology placement and usage that were invisible through observation alone.

The Challenge and Tools

Conducting effective ethnographic research can be daunting, given the level of detail it requires. Baillie and Benyon amassed 12 hours of video footage and 41 pages of notes in a single study. While experience is invaluable, there are tools and methodologies designed to facilitate this research. However, it's essential to strike a balance so that these aids don't become obstacles to genuine observation.

Addressing Bias

Ethnographic research raises concerns about potential biases. Introducing an external entity—like a researcher—into an environment can alter behavior, thereby introducing bias. While it's challenging to entirely eliminate this bias, methodologies aim to minimize its impact, allowing for more authentic observations. Researchers must also be aware of their own biases and work to separate them from their interpretations.

Real-world Applications

Consider the healthcare sector, which has been increasingly entrusting patient care to big data analytics. While constant health tracking promises "personalized healthcare," it often misses the deeper stories within people's lives that significantly affect their health. The thick description offers a path to capturing these vital contextual elements, making healthcare truly personalized.


Big data, while powerful, presents a limited view when devoid of qualitative context. Thick description enriches this data, offering deeper insights and more effective solutions. As we continue to navigate an increasingly data-driven world, the value of thick description in providing a fuller, richer understanding of human behavior cannot be overstated.