Data: all about me, or is it you?

TED is an organisation that promotes itself with the tag line ‘ideas worth spreading’. Presenters have included Tony Robbins, Steve Jobs, Elizabeth Gilbert, Richard Dawkins and Malcolm Gladwell. There are also thousands more on the website (which also include transcripts). TED seems to be a place where all the cool people tend to converge when they want to spread the message about inspiring others and changing the world. This next person suggests that changing the world starts with us as individuals and can be done with the use of a device very close to most of our hearts, the smartphone.

Gary Wolf, contributing editor to Wired and blogger for the Quantified Self,  is a journalist who gives a five minute introduction to an intriguing new pastime: using mobile applications and always-on gadgets to track and analyze your body, mood, diet, spending, just about everything in daily life you can measure in glorious detail.

Click to see a video of the presentation

Wolf talks about how numbers are “useful when we reflect, learn, remember, and want to improve.” He goes on to add that “[t]he self is our operation center, our consciousness, our moral compass. So, if we want to act more effectively, we have to get to know ourselves better.”

The talk ends with the tempting thought that numbers are the way to get to know ourselves better but it doesn’t go on to say how. I suggest that the next step is to find some way to present the data and then to find some meaning in it through analysis.

David McCandless, another TED talker, is a ‘data journalist’ because he uses data and graphics to present a story. I attended his TEDx presentation in Brussels in 2009 and he made the fascinating point that the brain takes time to interpret numbers and text in order to give them context but it can absorb graphics instantly. See the figure below:

The keyword coverage during elections and terror alerts is presented graphically to show an apparent association between the two over time. Most show an association although 2008 does not. The question at the end of the article is: ‘any correlation?’ Our eyes tell us there is a correlation, that the two events are related to the extent that when one happens we can increase our prediction rate of the other happening, but what does it mean? The association could be random, a comment made on the site by Tim suggests that he took the ‘time’ factor and even after accounting for it still found some correlation which he calls highly significant. He probably means that there is less than 0.0001% of a change that the pattern we see is due to randomness.

However, and to use a favourite statistical saying, correlation does not equal causation. Just because we can see an association, it does not mean that one event causes the other. To find out whether events did occur randomly, have some significant (non-random) relationship and what is the strength, direction and cause of this relationship, we would have to analyse the data and there are various techniques available.

However, that’s a matter for another day, for now it is useful to note how seeing numbers in a graphical format can add context very quickly and suggest some pattern or story. If you are collecting information about your caffeine intake and regular moods then you might find an association between those which may not be so easy to spot when you look at them as numbers.

There are three elements to quantifying the self: 1) collecting the data; 2) presenting the data in order to identify associations; and 3) to understand what the associations mean.

A useful source for exploring the data collection stage is the Quantified Self site which has tools to help. Ideas on presenting data are brilliantly presented on the site and McCandless is also published in the Guardian. The third part however is not often presented easily and as prettily as the rest.

I’ll leave that last one for another day.

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