Thick data/big data
Thick data, a term originally coined by Tricia Wang, is qualitative data (like observations, feelings, and reactions) that provides insights into consumers’ everyday emotional lives. Big data is quantitative data sets (collected at an extremely large scale) that may reveal patterns and trends relating to human behavior.
When is thick data used versus big data?
Because thick data aims to uncover people’s emotions, stories, and models of the world they live in, it can be difficult to quantify. It’s squishy and is collected in small sample sizes, but that doesn’t make it insignificant or unimportant. In fact, despite all of that, thick data provides never before seen perspective, depth of meaning, and emotionally-powered stories that can influence business decisions and build customer empathy.
Big data, for all intents and purposes, is the opposite of thick data. Most obviously because it’s quantitative in nature—meaning it deals with numbers and figures. What’s characteristic about big data is that it’s collected at such a large scale, and growing exponentially over time, that most processing powers aren’t even capable of capturing, storing, and analyzing it. Enter: a $138.9 billion market dedicated to just that.
Because big data is captured at such a high volume, it becomes difficult to analyze. But once this is complete, data scientists can focus on certain elements in the mass of data to isolate trends and make predictions. This is valuable, but there’s one big problem: big data is largely void of context and emotion.
Thick data can fill the cracks in the trends that big data uncovers by providing the necessary context and emotion that is lost when making big data usable.
Why big data needs thick data
Only using big data or only using thick data is like opting out of one of your five senses. Alone, each of your senses is valuable and provides you with information about the world around you, but together they form a more holistic view of any given situation. By integrating big and thick data, organizations are able to depict customer needs more holistically.
Thick data aims to build empathy and understanding of humans between data points while big data uncovers insights by isolating variables to identify patterns.