BIG DATA, SHRINKING VIOLETS

lascaux1

Education is not the piling on of learning, information, data, facts, skills, or abilities – that’s training or instruction – but is rather making visible what is hidden as a seed.
Thomas Moore

Not a week passes without yet another report from government or business that puts a spin on analysis of data to suit the agenda of those who commission or produce such reports. We also live in an age when we no longer blithely accept propaganda and we read most of what is pumped out with a healthy dose of scepticism if not outright cynicism. We are additionally promoting challenging the status quo as a desirable activity or mindset within our organisations as one facet of the new collaborative, creative and innovative workforce. There is a general acceptance that this is a good thing, and perhaps that is correct. After all, rigorous peer review has been the norm in scientific research for centuries. The outcome of this process is not always attractive, as had been seen recently in the field of climate change where rancorous debate and sharp practices have clouded an important issue, but a constructive exchange of opinions and perspectives can open up the conversation and promote broader understanding.

The world around us is a complex ecosystem. The universe in which that world exists is perhaps more complex still. We have made but small inroads to our understanding of this planet and it is generally acknowledged that we know more about some of the other planets in our solar system than we do about the ocean depths. Since the dawn of sentient humanoids we have sought to make sense of the complexity that surrounds us. The evidence is there in the beautiful simplicity of the paleolithic cave paintings at Lascaux in France. At the other end of the spectrum it led to the formation of organised religion (and we all know how that turned out). Similarly, the business world is also occupied by efforts to find order in the apparent chaos of organisational life.

One of the people who had most success in rendering the natural world in to some semblance of order was the 18th century Swedish scientist, Carl Linnaeus, whose work resulted in the adoption of binomial nomenclature, that is, (and here I quote directly from Wikipedia) the formal system of naming species of living things by giving each a name composed of two parts, both of which use Latin grammatical forms, although they can be based on words from other languages. Such a name is called a binomial name (which may be shortened to just “binomial”), a binomen or a scientific name; more informally it is also called a Latin name. The first part of the name identifies the genus to which the species belongs; the second part identifies the species within the genus. For example, humans belong to the genus Homo and within this genus to the species Homo sapiens. So great was Linnaeus’ achievement that no less a figure than Goethe was moved to say that nobody had influenced him more strongly with the exception of Shakespeare and Spinoza.

Thanks for the history lesson, but what does any of this have to do with Big Data, I hear you thinking. Well, I think there lies an important lesson in Linnaeus’ work. And that relates to classification. With one specific exception that I will come to presently, data relates to systems. Leaving aside the fact that the binomial “Big” unhelpfully creates a mountain of the mind, the key to starting to unpick data is in classifying it correctly. In broad terms, once we understand what data we have and how it is (or isn’t) organised, we can better begin to understand what it can tell us. This classification can also assist us in recognising inconsistencies in the data available and focus our attention accordingly. Although we, and businesses, are connected like never before, data continues to reside in different systems in incompatible formats. Appreciating that the picture we have is incomplete means we can see where assumptions need to be made or gaps filled.

And so to that exception. Social Media is the wild-card in data terms. It is widely seen as a powerful source for sentiment analysis and is, by its very nature, subjective. Anyone who has taken part in an employee satisfaction survey will know how badly many businesses manage and understand sentiment across the workforce. It is also a factor in why companies have such restrictive SoMe policies. Buechner’s quote “They may forget what you said, but they will never forget how you made them feel” appears on motivational posters in offices the world over but we so often fail to follow the advice in practice. Data may have helped you classify your employees but it sure as hell hasn’t helped you understand them any better. Those people you consider to be shrinking violets because they don’t speak out in meetings might be tweeting honestly and transparently what they daren’t tell you face to face. You might not like what that data tells you but we’re all about challenging the status quo, right?

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