Thick Data and Big Data

In March 1968, Robert F. Kennedy said, of the Gross Domestic Product index: “It measures neither our wit nor our courage, neither our wisdom nor our learning, neither our compassion nor our devotion to our country, it measures everything in short, except that which makes life worthwhile.”

“What is measurable is not always what is valuable.” Wang (2016b) paraphrased Kennedy, originally referencing GDP and its inability to measure the qualitative human condition. With the exponential increase in attention to Big Data as of late, the focus on speed and scale have left out things that are “sticky” or “difficult to quantify” (Wang, 2016b). This disparity reflects the traditional gap between qualitative and quantitative research. In fact, Wang found referring to the qualitative efforts in traditional terms (e.g., ethnography) was met with enough skepticism and pushback that a new term friendly to data jargon had to emerge—and thus the term thick data was born.

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Courtesy Tricia Wang

At first glance, thick data is not attractive in the traditional sense of big data. It is inefficient, does not scale up, and is usually not reproducible. However, when combined with big data, it fills the gaps that the quantitative measures leave open. While big data can identify patterns, it cannot explain why those patterns exist. If big data can go broad, thick data can go deep. Thick data relies on human learning and complements the findings from machine learning that big data cannot provide adequate context for. It shows the social context of specified patterns and is able to handle irreproducible complexity. It is the qualitative complement to quantitative data, the color and nuance to a black-and-white picture.

Forces against the adoption of thick data typically stem from bias against qualitative data. Again, it is messy…inefficient, sticky, complicated, and nuanced. Most of the big data world values what can be quantified and the relationships that can be mapped. As (Wang, 2016a) notes, quantifying is addictive, and it can be easy to throw out data that doesn’t fit a numerical value. It isn’t a zero-sum game, however. Both big data and thick data complement each other. But “silo culture”—the same phenomenon that disrupts data integration and wreaks havoc across enterprise data environments—threatens the symbiosis between these two (Riskope, 2017). While thick data is not an innovation in the same sense of cutting-edge artificial intelligence or new developments in IoT technology, it is an innovation in how we think about the world around us and what is important when studying that world.

References

Riskope. (2017). Big data or thick data: Two faces of a coin.  Retrieved from https://www.riskope.com/2017/05/24/big-data-or-thick-data-two-faces-of-a-coin/

Wang, T. (2016a). The human insights missing from big data.  Retrieved from https://www.ted.com/talks/tricia_wang_the_human_insights_missing_from_big_data

Wang, T. (2016b). Why big data needs thick data.  Retrieved from https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7

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