Data science is one the hottest career tracks at the moment, but why? What makes data science so valuable and why should candidates be training to be data scientists? David Pardoe from Hays Recruitment has the answer.
Why is it that data science is flavour of the month? I could quote from numerous sources about the “explosion of data”, the “big data” phenomenon, the rapid expansion of the internet of things (IoT) and so on.
There’s no denying that there is a monstrous amount of hype about big data and data science, but why? Here is one of my perspectives on why data science should be flavour of the month, and why we should all strive to cut through the hype.
The fundamental goal of data science should be to help humans make either quicker decisions or better decisions. You could assume that this is truer for some industries than others, but I would suggest it is true of all industries, even those where decisions are automated or seemingly happen without human intervention (eg online shopping/retail). Even in those industries, a human needs to determine how the machine will make the decision.
If you accept that it is humans that lie behind all decisions, we need to be aware of why and how humans make those decisions. Again, there are plenty of articles and books that describe this using terms like “cognitive bias” or “behavioural economics”. What they describe is the things that affect the way humans make decisions or judgements.
Take, for example, the Monty Hall problem. Without relaying the whole thing, the essence is that when someone is given a choice in this game show, most people choose a suboptimal option – because they are affected by human cognitive biases.
I have demonstrated numerous times with a large audience that I can affect the way they answer a question (about a topic they should know something about) by giving them a piece of paper with a number written on it. Those (randomly) given a high number always, on average, answer the question with a higher figure than those given a low number. This is an illustration of anchoring bias.
I could mention hundreds of other examples.
My point is that data science, if done correctly, is not affected by these biases. If we can only harness the outputs of data science and let them supplement our human experience, judgement, intuition and knowledge, we will surely make better decisions.
Do note that I am certainly not suggesting that data science replaces human decision-making because there are many facets where the human mind trumps data; it is still the case that the fastest supercomputer is vastly slower than the human brain.
So make every effort to cut through the hype and employ data science techniques that can help your organisation make better decisions, as the combination of smart human thinking and data science is unbeatable.
By David Pardoe
A version of this article originally appeared on Hays’ Viewpoint blog.
David Pardoe is the group head of data science at Hays. He joined in 2015 as Hays embarked on establishing data science as a core component of decision-making across the group.
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