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Fishing for patterns

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As individuals and organisations we are being flooded by exponentially increasing amounts of data. It’s scary and therefore tempting to turn to the apparent safety of outsourcing what all this data might mean to the algorithms, processors and experts that are generating it. But that is to underestimate our own ability to interpret the infinitely complex.

The Moken – ‘people immersed in water’ – are a predominantly nomadic race and one of the last to be touched by the modern world, with no words for ‘want’, ‘take’, or ‘mine’. They are born and live on the sea; they learn to swim before they can walk, can dive to depths of 20 metres on a single breath and stay there for minutes, naturally regulating their buoyancy without external weights to be able to walk on the sea floor or hover above potential quarry. About 1,000 of the 4,000 Moken live on the island of Mo Ku Surin which was at the focus of the devastating destructive power unleashed by the tsunami of 26th December 2004. Every one of them survived – not because of their aquatic skills but because of their story telling. 

Moken freedivers

Theirs is an oral tradition and many of their stories revolve around Laboon (or seventh wave) ‘an ancient thing that has swallowed whole islands before’. On that Boxing Day they noted the signs as foretold in the stories – in the behaviour of the sea, of animals – and following a discussion amongst elders sounded the alarm to head for higher ground…just in time.

Their knowledge of the precursors of an incredibly rare but uniquely threatening event survived through generations in story form. Not all stories are the same, it’s said there are as many stories of the Laboon as people to tell them. Nonetheless the ready accessibility of the lessons inherent was a shared experience; the signs were noted by more than one member of the tribe independently observing separate signals in different locations.

The natural word can sometimes sense the impending catastrophic threat more directly. Stories of animals acting strangely before earthquakes have been recorded since Roman times. In December 2013 the German Aerospace Agency announced a €19m project ICARUS (International Cooperation for Animal Research Using Space) to track the migration patterns of birds using transponders linked to the International Space Station. Birds use the ability to sense magnetic fields to navigate whilst migrating. It’s also known that there are fluctuations in the earth’s magnetic field prior to an earthquake because rocks give off ‘clouds’ of positive electrical charge when subjected to seismic stresses. ICARUS is to study this effect’s potential to use migrating birds and bats as earthquake predictors.

Just as effective yet significantly lower-tech, the Moken narrative made sense of the behaviour of animals by placing it in the context of a threat that demanded an immediate response. Humans have adapted a pattern recognition system to quickly assess threat, making sense of the most immediate limited data more quickly than it can be consciously processed. In this way survival is prioritised until more complete analysis can be done from a secure vantage. Safety first. Stories are a way of externalising this pattern recognition that can share its benefits amongst a community.

The Moken use stories to make sense of patterns in nature. Can we then use them to make sense of the sea of data that is rising around us? The details of the data will be new in their nature and scope, but the messages will be familiar in their pattern, interrelationship and consequences. And might this approach be at least as effective if not superior to the higher tech alternatives?

We’ve never been more sophisticated at crunching numbers and yet the best paid analysts in the world couldn’t make sense of the data surrounding the massively researched UK general election well enough to paint a portrait even remotely resembling the electorate. Such was their collective and public failure that they recently convened an emergency summit to create a story they could tell the corporations, advertisers and retailers who pay their bills in between elections about why they aren’t wasting their money.

There were many suggestions as to why the polls were unanimously wrong involving characters such as ‘shy Tories’ and ‘lazy Labourites’ and last minute swingers.  It was all, it seems, the voters’ fault. Writing in The Telegraph on 24th June 2015, Dan Hodges calls this as a lie. His thesis is based on the fact that at first and for most of the election campaign the polls weren’t unanimous at all, they were all over the place. Phone polls and online polls were showing significantly different results. Until around 72 hours before the election, when the polls began to converge.

Why?

Hodges says the pollsters’ key calculation is that it’s better to be one amongst a group of people that are wrong, than risk making a specific judgement and being wrong alone.

“The commercial and reputational risk is too great. So they herd. They deliberately manipulate their results to bring their own figures back into the pack. There is, they believe, safety in numbers. What’s more, there is even greater safety in being able to say an election is too close to call. ‘It was too close to call. How were we supposed to know? No one knew. We couldn’t call it. No one could call it.’”

So if you want to make sense of data – or the world – it would seem that being aware of human instinct is a key skill. First be aware of the herd instinct for self preservation that will drive analysis to an ever increasing consensus – whether right or wrong. Second understand that humans have evolved to be acutely sensitive to the patterns in nature and data. So much so that this sensitivity works ahead of and beneath the conscious. A way to expose it is to frame it in narrative, to ask people to share stories about the topic as a means to make sense of new unfamiliar data even as it emerges.

Next time you have the results from a survey or a new set of data don’t give it to an ‘expert’ to analyse. Instead ask a random but diverse group of colleagues to share stories about the topic at hand. Then ask them to tell you what the stories mean. Then share the data or findings of your survey with them and ask them what to do about it.

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