Since the early 2010s, big data, or the mass aggregation of quantitative data, has been trending. Big data still reigns  supreme in boardrooms across industries to inform critical business decisions regarding product development, services, and marketing; thus, quantitative data is a critical asset. However, used on its own, it can not only miss half the picture, but dangerously skew the story on the ground. It necessitates the reduction and standardization of data to be machine-processable, stripping it of context in the process.  

Technology ethnographer Tricia Wang described how over-reliance on big data contributed to the infamous downfall of Nokia; the company ignored unexpected rich insights from a small qualitative sample in favor of large quantitative datasets which confirmed the status quo. Wang drew from the ‘thick description’  championed by anthropologist Clifford Geertz to coin the term ‘thick data’ to describe qualitative data such as motivations, meanings, and emotions [Geertz, Wang ]. Thick description is  captured by the qualitative method of ethnography which anthropologists use to dig deeper into a phenomenon by interpreting the cultural context.

By providing such context, qualitative data can unearth the ‘why’ behind the ‘what’ captured by quantitative data:

Big Data vs Thick Data

Applying qualitative methodology to capture thick data reaps bountiful rewards as it allows a deep dive into the root cause - the ‘why’ behind a phenomenon, generates unexpected insights, and emotionally connects your audience to a narrative that humanizes numbers, promoting buy-in. Qualitative data is a necessary complement to bridge the gaps of quantitative data in today’s swiftly changing  world.

Health care professionals can leverage the power of thick data to complement the clinical outcomes of trials and the quantified patient-reported outcome measures on quality of life (PROMs) by unveiling why a patient is not adhering to a treatment, and which symptoms or comorbidities are most impacting them. This information can ultimately improve health care outcomes by painting a rich picture of the motivations and drivers of behavior.

Pharma companies can use thick data to generate evidence on clinicians’ preferences and concerns about new treatments, as well as to shed light on the subjective experience of patients along the patient journey, bridging data gaps by capturing what is meaningful and why. This evidence can support marketing, market access and medical affairs. For example, these insights can then be used to craft more impactful patient support programs and have meaningful conversations with HCPs to improve the health care system from inside.

You can learn more about how to complement big data with qualitative insights to enhance healthcare and other industries through our case studies.


Wang, T. (2016). Why Big Data Needs Thick Data. Ethnography Matters.
Geertz, Clifford. 1977. The Interpretation of Cultures. New York: Basic Books