Big Data Needs Thick Data

Big Data can have enormous appeal. Who wants to be thought of as a small thinker when there is an opportunity to go BIG?

The positivistic bias in favor of Big Data (a term often used to describe the quantitative data that is produced through analysis of enormous datasets) as an objective way to understand our world presents challenges for ethnographers. What are ethnographers to do when our research is seen as insignificant or invaluable? Can we simply ignore Big Data as too muddled in hype to be useful?

No. Ethnographers must engage with Big Data. Otherwise our work can be all too easily shoved into another department, minimized as a small line item on a budget, and relegated to the small data corner. But how can our kind of research be seen as an equally important to algorithmically processed data? What is the ethnographer’s 10 second elevator pitch to a room of data scientists?

…and GO!

Big Data produces so much information that it needs something more to bridge and/or reveal knowledge gaps. That’s why ethnographic work holds such enormous value in the era of Big Data.

Lacking the conceptual words to quickly position the value of ethnographic work in the context of Big Data, I have begun, over the last year, to employ the term Thick Data (with a nod to Clifford Geertz!) to advocate for integrative approaches to research. Thick Data uncovers the meaning behind Big Data visualization and analysis.

Thick Data: ethnographic approaches that uncover the meaning behind Big Data visualization and analysis.


Thick Data analysis primarily relies on human brain power to process a small “N” while big data analysis requires computational power (of course with humans writing the algorithms) to process a large “N”. Big Data reveals insights with a particular range of data points, while Thick Data reveals the social context of and connections between data points. Big Data delivers numbers; thick data delivers stories. Big data relies on machine learning; thick data relies on human learning. 


As the concept of “Big Data” has become mainstream, many qualitative researchers from Genevieve Bell (Big Data as a person) to  Kate Crawford (algorithmic illusion, data fundamentalism), and danah boyd (privacy concerns) have written essays on the limitations of Big Data. Journalists have also added to the conversation. Caribou Honigdefends small data, Gary Marcus cautions about the limitations of inferring correlations, Samuel Arbesman calls for us to move on to long data. Our very own Jenna Burrell has produced a guide for ethnographers to understand big data.

Inside organizations Big Data can be dangerous. Steven Maxwell points out that “People are getting caught up on the quantity side of the equation rather than the quality of the business insights that analytics can unearth.” More numbers do not necessarily produce more insights.

Another problem is that Big Data tends to place a huge value on quantitative results, while devaluing the importance of qualitative results. This leads to the dangerous idea that statistically normalized and standardized data is more useful and objective than qualitative data, reinforcing the notion that qualitative data is small data.

These two problems, in combination, reinforce and empower decades of corporate management decision-making based on quantitative data alone. Corporate management consultants have long been working with quantitative data to create more efficient and profitable companies.

With statistically sound analysis, consultants advise companies to downsize, hire, expand, merge, sell, acquire, shutdown, and outsource all based on numbers (e.g.Mckinsey, Bain & Company, BCG, and Deloitte).

Without a counterbalance the risk in a Big Data world is that organizations and individuals start making decisions and optimizing performance for metrics—metrics that are derived from algorithms. And in this whole optimization process, people, stories, actual experiences, are all but forgotten. The danger, writes Clive Thompson, is that “by taking human decision-making out of the equation, we’re slowly stripping away deliberation—moments where we reflect on the morality of our actions.”


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