Data related to qualitative aspects of human experience and behavior, particularly when used as context for the analysis of a large data set.
This term is a mashup of big data and thick description, an anthropological research methodology that documents not only human behavior, but also the context of that behavior.
Thick data is simply the idea that numbers alone aren’t enough. To really understand data, you often need to consider things like human emotion, which is rarely data-driven.
Big data is yielding to thick data and that’s a good thing – BloomReach
Thick, or small data, is human-scale data, collected from small samples in everyday contexts, making visible the motivations, values, preferences, perceptions, and environments of people often left out of public consultations or large-scale datasets…Data that can explore behaviours and intentions, within the places and spaces people live their lives, is data that can help to inform how to frame public policy problems, and how to identify leverage points and attractive interventions – all from the perspective of people on the ground
—InWithForward, Thick Data Primer
Thick Data, in contrast to Big Data, gives us the permission to put all of our sensemaking tools at our disposal, not just what we have the most of, to understand the complicated human endeavor of teaching, learning, and schooling. While Big Data arrives in large standardized quantities and without context, Thick Data is informed by context, stories, and values that can help us make sense of individual experiences.
Thick Data is data brought to light using qualitative, ethnographic research methods that uncover people’s emotions, stories, and models of their world. It’s the sticky stuff that’s difficult to quantify. It comes to us in the form of a small sample size and in return we get an incredible depth of meanings and stories. Thick Data is the opposite of Big Data, which is quantitative data at a large scale that involves new technologies around capturing, storing, and analyzing. For Big Data to be analyzable, it must use normalizing, standardizing, defining, clustering, all processes that strips the the data set of context, meaning, and stories. Thick Data can rescue Big Data from the context-loss that comes with the processes of making it usable.
Integrating big data and thick data provides organizations a more complete complete context of any given situation. For businesses to form a complete picture, they need both big and thick data because each of them produce different types of insights at varying scales and depths. Big Data requires a humongous N to uncover patterns at a large scale while Thick Data requires a small N to see human-centered patterns in depth. Both types of Thick Data relies on human learning, while Big Data relies on machine learning. Thick Data reveals the social context of connections between data points while Big Data reveals insights with a particular range of quantified data points. Thick Data techniques accepts irreducible complexity, while Big Data techniques isolates variables to identify patterns. Thick Data loses scale while Big Data loses resolution.
It’s worth noting that the concept of street data has recent echoes in other fields. For example, in the corporate world, innovators are beginning to challenge the dogma of “big data” and push for an emphasis on “thick data.” Global tech ethnographer Tricia Wang defines thick data as “data brought to light using qualitative, ethnographic research methods that uncover people’s emotions, stories, and models of their world. It’s the sticky stuff that’s difficult to quantify. It comes to us in the form of a small sample size and in return we get an incredible depth of meanings and stories.” (Wang, 2016).
She contrasts thick data with big data—“quantitative data at a large scale that involves new technologies around capturing, storing, and analyzing.” Sound familiar? Wang sees value in big data, but her critique is sharp: The normalizing and standardizing processes it employs tends to gut the data set of meaning and context—a problem that only thick data, or in our case street data, can rectify.
In a fascinating story about her own research at the cell phone company Nokia in 2009, Wang describes how years of conducting ethnographic work in China helped her uncover an insight that challenged Nokia’s entire business model: Low-income consumers were ready to pay for more expensive smartphones. Instead of embracing her findings, the company derided them, arguing that her sample size was too small and thus irrelevant. She responded that their notion of demand was “a fixed quantitative model that didn’t map to how demand worked as a cultural model in China” (Wang, 2016). In short, Wang was right, and Nokia met its downfall because it over relied on the numbers and ignored the import of cultural stories and context.
This is the power of street data. It offers us insight into localized cultural models that, if we dig deep, illuminate the root causes of inequities as well as places of opportunity and cultural wealth.
Safir, Shane; Dugan, Jamila. Street Data: A Next-Generation Model for Equity, Pedagogy, and School Transformation (p. 59).
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.
Thick Data can be used as a counterbalance to Big Data to mitigate the disruptiveness of planned organizational change. Quantitative data may suggest that a change is needed, but disruption inside organizations can be costly. When organizational charts are rearranged, job descriptions are rewritten, job functions shift, and measures of success are reframed — the changes can cause a costly disruption that may not show up in the Big Data plan. Organizations need Thick Data experts to work alongside business leaders to understand the impact and context of changes to from a cultural perspective to determine which changes are advisable and how to navigate the process.
