The Diagnosis Of Data: re-framing big data into a human context
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When I was young I remember paging through big medicine books. Both my parents are doctors and therefore we had them laying around in the open. My sister and I enjoyed these more than we enjoyed our own children’s books. From disease to syndromes, from minor rashes to complete body transformations: the human condition and all its variations were meticulously documented, ordered, and organised. To us it was spectacular to learn that the human body comes in all kinds of packages and sizes.
Upon asking my mom why these people were ‘sick’, she replied that it’s just the way our bodies communicate their condition; through little cues called ‘symptoms’. Doctors need to have a profound understanding of the nature of each condition, develop their experience and sharpen their intuition to be able to indicate a remedy or treatment. This, in a nutshell, is what diagnosis is all about and it has fascinated me ever since.
The fascination however didn’t drive me to study medicine. Today, I’m working on digital projects that explore new modes of engagement with data and storytelling. Yet, odd as it may seem, I realised that diagnosis still lays at the heart of what I do.
A single version of the truth
You see, over the years ‘big data’ has started a revolution, a new Age, defined a new generation; it has been declared the new black gold, the new oil, the new soil, the new science, the new common language; and it has certainly raised a lot of issues around its definition, mining, storage, analysis, interpretation and visualisation. Without getting too much into the issues that have been raised due to the promise and perils of big data, I would like to put forward that it’s not big data we’re talking about, it’s our human data.
Data, wether big or small, have always been a product of human behaviour and experience. Companies are collecting data, combining and analysing them to get a complete picture of operations that allows them to operate more efficiently, pick out trends and improve their forecasting. They use this data to get a so called “single version of the truth” — as the industry likes to call it. The result of any data analysis however, no matter how big its scope, also beholds a story about us. Removing the human context from this data, makes it susceptible to any interpretation.
We need to engage people and human intellect to get stories right
As Jer Thorp advocates, we need to re-situate this data into a “human context”;
This is deeply human data, though very often it is not treated as such. Here, perhaps we can invoke a comparison to fossil fuel in a useful way: where oil is composed of the compressed bodies of long-dead micro-organisms, this personal data is made from the compressed fragments of our personal lives. It is a dense condensate of our human experience. This re-framing of data into a human context is crucial (Jer Thorp, Data is not the new oil, 2012)
Most big data companies are not concerned with getting the story right. They are concerned with big data that can bring in big money.
Re-framing our data into a human context: diagnosis
We need to start thinking about an approach that is more prone to diagnosis. Instead of numbers, diagnosis deals with the complex reality of people. Diagnosis moves beyond approaching matters in a purely analytical fashion and includes experience, logic, intuition as a way to make sense of the complex reality we live in. Its goal is to improve our understanding of it. Like Melissa Terras from UCLA’s Centre for Digital Humanities puts it:
Just because you made a pretty map that looks pretty, it doesn’t answer a question that improves our understanding of it. We’re asking the big questions about society and culture.- Melissa Terras from UCL’s Centre for Digital Humanities
The world is one big data problem
We need to stand back and ask ourselves (not the machines or the data crunchers) why are we doing this? Diagnosis helps us approach that question with the right attitude. To me it means not jumping to conclusion towards things you haven’t come to understand yet. It means not starting to solve a problem, without determining why it’s a problem in the first place. Diagnosis is about not being prejudiced and trying to involve various perspectives and disciplines into your research. Like my mom always used to say “there is no ‘good’ or ‘bad’, there’s just a different way of looking at it.”
Let’s move beyond the discussion of “the volume, variety, velocity, veracity and verisimilitude of data” and set out a new challenge in finding big data’s real value to us.
This is where the promise of data ends and its potential starts.
