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Thick Data vs. Big Data


  Date: 2016-01-20
  Author: Tricia Wang
Source: https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7
  Domain: Understanding Data
  Subdomain: Defining Data
  Type of Resource: Term

According to Tricia Wang, it is important to understand what thick data is and why it is valuable, especially in an age when Big Data gets all the hype. According to Wang, “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.”

Keywords:  Thick Data    Big Data    Ethnography    Qualitative Research    Keyword 5  

MLA Citation: Wang, Tricia. 'Why Big Data Needs Thick Data” Ethnography Matters' 20 January 2016. https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7. Accessed on 15 May 2023.



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