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Big Data’s premise is that once huge amounts of data are collected, it’s possible to notice trends and patterns, and those patterns in turn lead to conclusions and predictions about future trends. Big Data’s focus is algorithmic and sterile. The mathematics and algorithms behind Big Data are quantitative.
Thick Data is the opposite of Big Data. It is qualitative and considers the insights, emotions and context of where data is created. Big Data is the realm of computer dashboards, statisticians and mathematicians. Thick Data, on the other hand, is the realm of anthropologists, sociologists, and social scientists. Big Data on it’s own can lose sight of the emotional aspect of data.
Tricia Wang, ethnographer, wrote that “when organizations want to build stronger ties with stakeholders, they need stories. Stories contain emotions, something that no scrubbed and normalized dataset can ever deliver. Numbers alone do not respond to the emotions of everyday life: trust, vulnerability, fear, greed, lust, security, love, and intimacy. It’s hard to algorithmically represent the strength of an individual’s service/product affiliation and how the meaning of the affiliation changes over time.”