The Value of Data Science Skills

They are in demand and provide critical insights for numerous organizations across the globe.

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We are constantly looking to predict the future; millions are won and lost trying to predict who will score next, what horse will win and what numbers will be the first pulled out by the lottery machine. Some people say we have lost the ability to be what they call “living in the now”. I think that’s true and I also believe in the business world we can’t afford to live in the now; you have to be able to predict the future but the decisions are made backed up with huge amounts of data.

Now there is a difference between running complicated prediction models powered by algorithms written by your friendly data scientist and simple "what if" analysis against set metrics powered by knowledge of the industry and your own knowledge of the way your company works. I recently presented Qlik to a group of PhD students wanting to become data scientists. Let’s not forget these people could be earning a lot of money using their undeniable skills, which have been gained over years of education at the best universities. But will your company be able to employ such skills? And do these skills work in isolation? I think not.

When I present at conferences and get immersed in the cutting edge of BI and how it will evolve in the future, I tend to forget that this is not representative of the vast bulk of company’s fighting toe-to-toe in the world today. A vast majority of people out there are still going into battle with an old favorite called Excel and experiencing the light and dark side of that relationship which many have become so familiar and cozy with.

I have recently been involved with creating an app looking at the possibility of a white Christmas across some parts of Europe and North America. Do you think I am locked in a room writing hundreds of lines of code using the alchemy of data science?  I am afraid not as that particular skill is not in my tool set. What I am doing is gathering data freely available and loading it in to a Qlik application. Of course there is no guarantee that any prediction the application makes will be correct and I won’t be going out to buy a sledge any time soon that’s for sure. It is enough for me to know my prediction will be backed up by data which in this case goes back 10 years (we won’t go in to climate change).

There will always be a case for data scientists and that’s why they can demand such high salaries. And you must not forget their skills are invaluable applied to the correct scenario, which includes funnily enough working out the odds for most large-scale betting organizations. But let’s not forget, provided the data is good enough, there is still a huge place for just using Average, Sum, Count and Max.

This will be my last blog before the Festive break so Merry Christmas to everyone who reads me and I look forward to putting keyboard to document in the New Year!

Photo credit: morberg via Foter.com / CC BY-SA

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