At Genbu I've been working on what most closely relates to my BA studies – ethnography. Here, I'd like to go into detail and describe what is "thick data" and why you and your business should care, especially if you've ever only heard of "big data". If you want a summary, begin with this video.
Keep reading and I'll show you how deep the rabbit hole goes.
Big Data has been a buzzword for analytics and marketing for nearly 20 years
The importance of Big Data in modern society cannot be understated. It has been a buzzword for analytics and marketing for nearly 20 years already. The information society in which we live constantly produces and stores unimaginable amounts of information about our daily behaviour. It is possible, with the right tools, to filter and process various very distinctive findings from this informational mass, that can be then used to enhance marketing, management and product development strategies.
Or so they say. A few years ago behavioural economist Dan Ariely aptly remarked: ”Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”
In the last few years big data has been criticised for its tendency to fade its subjects into a faceless set of numerical data, and for the risk of distorting results and evaluations acquired if big data is taken as an objective representation of real world occurrences. Despite the massive size of individual data sets, every chart and data set is always a model of reality – and the methods for gathering that data influence its usability and reliability.
Traditionally, corporate decision-making has relied on quantitative information. Nevertheless blind trust in statistics is a risky decision. There has been a lot of talk about intuition in the corporate world, often the word is brought up when the managers decide to stop looking at numerical data. This does not necessarily imply a ”shot in the dark” attitude, instead it can mean bringing in an assessment of human nature and multifaceted experience to the decision-making process.
Big data needs to be complemented with Thick data
Therefore Big data needs to be complemented with Thick data. Ethnologists, anthropologists and other humanities and social sciences researchers are a key factor in producing thick data. Tricia Wang, who has popularised the term over the past few years, refers to Clifford Geertz’s ”thick description”, a method that is the basis for modern ethnographic field research. Geertz has compared thick description to peeling onion skins: to get to the core of something interesting, be it a model of behaviour or the entirety of some social group, you need to disassemble if layer by layer, meanwhile recognising the connections in-between.
Whereas big data entails mechanic processing of large data sets, thick data comes down to analysing much smaller but attentively selected data sets from an expert’s professional viewpoint. As a method ethnography can be very straight-forward: typically the researcher interviews key persons and observes everyday situations. However a researcher well-acquainted with their subject matter and its surrounding context is equipped with the know-how to ask questions and pursue answers that otherwise would not have ever surfaced in a more cursory survey.
Ethnographic research provides and collects diverse narrational and observational content and processes them into stories through analysis. In prudent decision-making these stories that arise from the actual people and places that are affected by said decision-making are invaluable.
Ethnographic thick data should not be seen as a replacement for big data, but as its complementer. Big data can clarify certain things, but quantitative data needs qualitative data and human centered analysis for balance, especially when there is an interest toward how and why people act, not just what are they doing.
- Antti Kiviranta
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