Why Is Data Science Important?

So 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, “Data is the new oil”, the rapid expansion of the “Internet of Things” and so on. As most readers are well aware, there is a monstrous amount of hype about big data and data science.

In this short post I thought I would share 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.

As I have mentioned in a previous post the fundamental goal of data science should be to help humans make better decisions; either quicker decisions or better decisions. You could assume that this is more true 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 (e.g. 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” (ref. Daniel Kahneman’s book Thinking, Fast and Slow) or “behavioural economics”. What they describe is the things that affect the way humans make decisions or judgements.

The picture at the top of the post represents the Monty Hall problem / gameshow. I will not relay it completely here (everyone knows how to use Google) but the essence is that when someone is given a choice in this game show most people choose a sub-optimal 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.

Hopefully you found this blog interesting. Here are some related articles which you also might enjoy:

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Author

David Pardoe

Group Head of Data Science, Hays

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David 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. Data Science is all about using sophisticated machine learning and other advanced mathematical and statistical techniques to provide insight that supports our consultants in making better, more informed decisions. Big Data tools and applications are being developed and embedded alongside the core production applications to get this information right to the front line of Hays’ operations.

David has over 20 years’ experience in this field across many different industries; including Insurance (AIG, Prudential, Lloyds Banking Group), retail (Morrisons), travel (British Airways) and newspaper and digital publishing (News UK). He has also worked for some of the leading management consulting firms in this space, including Accenture and Capgemini. David also regularly speaks at Data Science events and conferences on these topics and has a Master’s degree in Statistics.