Why Is Data Science Important - Viewpoint - careers advice blog Viewpoint careers advice blog

Why Is Data Science Important - Viewpoint - careers advice blog 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 Kahnemans 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: QA with David Pardoe, Group Head of Data Science, Hays SQL or NoSQL data for Data Science? Top 10 jobs in data The 8 hottest jobs in telecoms Digitally transform your business with these soft skills New cyber security challenges: What you need to know Visit your local Hays website Visit your local Hays website to search and apply for Data Science jobs near you. Search Join our LinkedIn Group Join our LinkedIn Group to share your thoughts and stay up-to-date with the latest on business, employment and recruitment news in the IT industry. Join our Group Share this blog: