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People used to think the earth was flat. That theory was disproved by many famous mathematicians, but before those discoveries, humans perceived the universe in two dimensions. Fast forward to today, while the earth is still round, business, an integral part of modern-day society, is still only running on engines of two dimensional data.

There is a Wall Street Journal article that compares and contrasts the different types of metrics that companies use to evaluate their success. The article started off by saying that most companies traditionally rely on ‘Big Data’ to guide most of their decisions. Big Data refers to both the massive volume of statistics compiled by companies, and the non-traditional processing techniques required to analyze such large outputs. The end result of Big Data analysis is similar to early interpretations of our own planet, completely flat.


While Big Data is important, it only tells part of the story.

People are alive. It’s one of the main things that differentiate us from say, paperclips. Since people, or in this context, customers, are the reason businesses even have these massive data sets, it would stand to reason that gaining deep insight into them would be imperative. Thick Data is a term used to refer to qualitative metrics used in research. Qualitative data measures more subjective data points often collected in settings like focus groups.

There is another type of data, that to date has not been considered, a third dimension to analysis that goes beyond traditional quantitative and qualitative measures . Imagine if you could capture information about people as they interact with content and products in real time, gathering their sentiment, presence and engagement. Capturing this information would allow you to qualify both quantitative and qualitative data points, creating a more sophisticated level of business intelligence.

Human Metrics, a method of measurement of human responses and behavior, provides the opportunity for smarter decision-making. People take action or make decisions based on what they cognitively know, but they also do these things based on how they emotionally feel. Understanding this third dimension is critical to understanding a person’s motivation and actions along the consumer journey.

In her 2015 book, 'Data-Informed Product Design', Pamela Pavliscak (a leading design researcher) explains the distinction between Big and Thick data:



What, where, when, how

Transactions, customer service logs, analytics, A/B tests, social media posts, GPS


A large number of people

Collected by machines


Behaviors and actions of many people

Collected as people do what they normally do

People are not highly aware of data being collected

Analysis uses statistical methods


How and why

Interviews, contextual research, usability studies


Relatively few people

Collected by people


Behaviors, actions, emotions, intentions, motivations of a few

Collected as part of a study

People are highly aware of data being collected

Analysis includes developing codes, summaries, and themes

Pavliscak, Pamela. "Designing with All of the Data." Data-Informed Product Design. O'Reilly Media, June 2015. Web. 29 Oct. 2015.


Just from reading the language Pavliscak uses to describe thick data, as opposed to big data, you can begin to imagine the individual behind the information.

Yet, you can also see this distinction isn't always a clean one. Organizations now blend this dichotomy, and take into consideration how people behave in everyday scenarios. So, the context in which we 'measure' people is just as important as the data collected.

The aforementioned WSJ article later made the assertion that business is really just about making bets on human behavior. Those lucky enough to hit the jackpot revel in success. How can a company increase their odds of winning?

Incorporate Human Analytics

Breathing life into flat data brings clarity to your analysis. Meaningful data, the kind of data that incorporates quantitative metrics with human emotion, provides business leaders an effective framework for evaluation because it tells a more complete story - ultimately getting closer to the "why".

It turns out there was more to the idea of the earth being flat. And there’s more to the idea that data has to be flat. With Human Analytics, your data gets a new dimension.

Give your data a human face. Talk to us about how.


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