Does Machine Learning Signal the End for MR Pros?
By Sinead Hasson
When someone introduced me to the concept of ‘machine learning’ a few weeks ago I caught an unsettling glimpse of the not-too-distant future. For the uninitiated, machine learning is a new field of data analysis, which Stanford University defines as ‘the science of getting computers to learn without being explicitly programmed’. We’re talking seriously smart algo-based software. So smart, in fact, that it can sift through the petabytes without being directed and identify many more trends than a human analyst can, produce more reliable forecasts and as a consequence (gulp) make better decisions too.
It’s news to no-one that a human analyst’s data interpretation is innately biased. That’s human nature; absolute impartiality is impossible for us to achieve. This, in itself, isn’t a problem – we live in a human world where the idea of ‘useful knowledge’ is heavily influenced by our social, economic, historical and cultural situation. The problem here is that we don’t always know the right questions to ask in the first place. By sheer depth and volume of analysis, however, machine learning promises to reveal what those missed lines of enquiry might be, having traced them back from abstract trends that it has already discovered.
All of which begs a question; if researchers are no longer needed to define the questions, discover the trends, make the forecasts or indeed influence the resultant decisions, they might as well all go home, right?
Wrong. Because fortunately, we don’t live in a world governed by machinery, and science fiction aside, it’s pretty unlikely that we ever will. Not all insights can be gained from logic. The ‘right’ decision isn’t always made from an operational analysis, or as a result of a statistical forecast. It’s why Mr. Spock needed Captain Kirk. In fact (humour me here) I think machine learning is Kirk delegating to Spock so he can get the broadest possible picture, upon which he can then impose his ego-centric, ever-so-human judgement.
Around 90% of all decisions made in major commercial organisations are operational in nature, so you can see why people are getting so excited – machine learning’s potential to raise corporate efficiency is huge. But understanding an audience that consists of you and me, each with our own wonderful and unique set of illogical, emotional quirks, is something quite different.
I hear that the Machine to Machine (M2M) age is coming, but you know what? Until we need to understand how machines, and not us fleshy consumers, are influenced and motivated by our clients’ brands, I’m not worried. Neither should market research professionals.
Join the debate @sineadh