Leadership studies pioneer Warren Bennis once said that ‘the factory of the future will have only two employees – a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment’. Are we anywhere in the vicinity of that future yet?
Best-selling business author Bernard Marr’s recent blog, Can AI Make The Sexiest 21st Century Job Obsolete?, examines one of the most revolutionary developments in the world of data analytics and big data – the ability of machines to take over the work of their masters – the data scientists. Marr writes: “If computers can run algorithms to do just about anything our brains can – surely they can be taught to look at a dataset, work out what sort of data it might contain and how it could be useful, then decide what algorithms are likely to extract that value?”
While the prospect of these developments may seem terrifying to some data scientists, at this stage a considerable chunk of data analytics functions still remains within their control. Therefore, notwithstanding alarming stories about the looming takeover of the world by machines, such a future is impossible or at least quite far-removed from the realms of our current reality. What we have at hand is the story of mind+machine, where men and technology combine their might to create spectacular results.
In my experience, when it comes to analytics, one of the most common issues resulting in low or negative ROI occurs when the implementation happens without a clear definition of the specific business issues it seeks to address. Letting machines create analytics use cases for the sake of analytics will certainly lead to a situation where you have 10000 spoons when all you need is a knife! So, the relevance of the human mind still remains intact. Its role includes specifying end users, defining business issues to be solved, and identifying client benefits.
Moreover, there is another crucial topic that can impede the speed at which completely machine-driven analytics is adopted by businesses. I like to call it the psychology of analytics. Analytics might appear to be a very rational discipline, but just like mathematics, it can trigger strong emotions in people. The quantitatively gifted will quickly see the benefits of analytics and exploit them, while the people on the opposite end of the spectrum will avoid it at all cost. Additionally, just as in any other field, analytics has its own language, which can act as a further barrier for those who might try to overcome their initial aversion.
In view of these restrictions, automating the mundane parts of data analytics seems like a more plausible step forward. However, without a data scientist to control and own the process, it risks missing the point. It will be the data scientist’s role to engage with those who need the insight that data analytics can provide, whether they like the means or not. In other words, as machines become more robust, the role of the mind will not only become more focused but also more important.
Contrary to what the headline might suggest, even in a world with much more advanced technology, only the coming together of mind+machine can lead to real benefits.