The world’s largest corporations are increasingly investing in projects to try and use data for better decision making in their businesses, to improve how they work from marketing to operations.
Why is it then that nearly three-quarters of big data projects are unprofitable?
One explanation is put forward by Tricia Wang in this TED talk.
We use data to help us understand how the world around us works, and we hope that this understanding will help us predict what is going to happen in the future.
But, depending on how we approach the idea of data, this results in different tactics by different organisations.
The “hot” approach is the one of big data.
Everything is connected – the internet of things (IOT).
Data is collected automatically, recording everything from your browsing history to when your toaster turns on and off.
Tricia Wang has invented the term “thick data” for an approach to collecting data by observation – something done by the likes of ethnographers and anthropologists.
This is a modification of the term “thick description” that tries to explain behaviour and the context in which the behaviour takes place.
So, in big data, computers collect information from customer “touchpoints” – the places where you interact with the machines.
In thick data, people collect information by observing and interacting with other people.
Tricia’s example of how this results in different outcomes is the case study of Nokia.
The huge amount of data collected by Nokia from its customers and market research failed to alert them to the possibilities of the smartphone.
Tricia’s research showing that low-income Chinese people were willing to spend half their monthly income on buying a phone convinced her that the smartphone would take off.
And we all know what happened to Nokia when the iPhone took over the world.
In a big data world more is better – sample sizes are huge.
We collect millions of data points and store these in the cloud.
Tools like IBM’s Watson help you analyse and evaluate this data for not just quantitative insights but also, through natural language processing, for emotional components and behavioural predictions.
With thick data, we have a small number of data points.
Someone has to spend time with people, observing what they do, where they do it and draw conclusions on what that means for the future.
Big data helps you quantify the world.
All the measurements you take give you the ability to look at how people interact with your business in a level of detail beyond anything that was possible before and express this in numerical terms.
Thick data helps you explain why people do what they do.
Taking time to watch and interact with people gives you insights into the way they think and behave and, crucially, what they might do next.
The point is that it is not an either-or situation.
Using just big data is not enough.
Combining the power of big data to quantify and the power of thick data to explain can give you a better understanding of the situation.
Take a simple example of thick data in action.
If you have watched The Social Network, you’ll remember a scene where Zuckerberg is trying to figure out what to do with his system.
Over a drink, his friend comments that it would be great if people had a badge that said there whether someone was single or attached.
Zuckerberg has a flash of insight and adding that feature to facebook causes subscriptions to rocket.
In other words – if you work out how use both big data and thick data in your business, you are more likely to be able to better understand and predict the future.