It’s hard to believe that just a few years ago, most organisations were struggling to get to grips with the idea of big data. Nowadays, we’ve all bought into the importance of analytics as a driver of change, and the new topic of conversation is data science.
I'm reminded of the Shakespeare quote: “If music be the food of love, play on.” Data may not be the food of love (unless you're Facebook Dating) but it certainly feeds a lot of other things – more inclusive societies, digital leapfrogging, and better ways of doing things.
If data science is the answer to saving the world, then we need to play on.
That may sound simple on paper – but what if you’re a small organisation that doesn’t have data science skills on hand? Even if you have a team of analysts at hand, what if you don’t have enough knowledge to give them the right direction? After all, even the best orchestra won’t get very far without a conductor.
Thankfully, the days when data science was only accessible to the brainiest of number-crunchers are behind us. With the rise of machine-learning-as-a-service solutions, open source platforms and communities, and educational resources, data science is more accessible than ever. And it’s not just the coders and developers who should be taking advantage of this accessibility.
Yes, I’ve heard the excuses before. It’s a complex discipline and most people lack the foundational knowledge of STEM needed to really understand. But, since we’re on the subject of Shakespeare, I’d like to ask how many people struggled with his works at school? How many of us never quite grasped what he was trying to say because the language was just too different to what we were used to?
Shakespeare may be hard to grasp today but in his time, he was popular with the common people. They just got him – his language was their language and his puns and in-jokes were something drawn from their everyday lives.
One great way to understand how data science can be effectively applied in business is by watching the companies that are doing it best.
Even today, hundreds of years after ruffled collars stopped being a style statement, people still devote time to figure out what he was trying to say. You don’t need to be a Shakespeare scholar to understand what’s going on in Romeo and Juliet – you just need some time and context.
Unless you work for the Royal Shakespeare Company or have Kenneth Branagh’s number on speed dial, you probably don’t need a working knowledge of the bard to make it in business. But data is a different story. And just like Shakespeare, a little devotion can go a long way to grasping the fundamentals of data science.
It’s all Greek to me
Okay, let’s say you want to learn to speak data so you know what the analytics nerds are talking about when they mention their algorithms. Where do you start? Do you take a sabbatical and get a degree at a university? Or do you dedicate your evenings to completing an online course from a MOOC?
From Coursera to Datacamp to Harvard itself, there is no shortage of excellent data science, statistics, analytics and programming courses available at all levels of knowledge. Then there are the communities, both local and international, that have grown in leaps and bounds over the last few years.
Just as Shakespeare is best grasped when you watch it performed, learning to apply data science is a far more effective way to learn it than just reading the theory. In between whatever course or tutorial series you might be following, you might want to find a place to practise.
Web sites like Kaggle and kdnuggets are great virtual playgrounds for testing out your predictive analytics skills, offering everything from free tools, code, datasets and challenges. Or, if your goal is to get more comfortable with the basics of data analysis, it’s easy enough to open a Google Data Studio account and dive in.
All difficulties are easy when they are known
Eventually, it should all start to make sense and the data will start to come alive with meaning. At least, that’s the promise.
But there’s one skill that can’t be easily taught – developing what Nate Silver, the analytics guru behind fivethirtyeight, calls statistical intuition. How do you put the data to work doing something useful?
One great way to understand how data science can be effectively applied in business is by watching the companies that are doing it best. Blogs from the likes of Facebook, IBM, Oracle and Google won’t necessarily give you the technical depth you’d get from GitHub, but they do offer valuable insights on how to ask the right questions.
And that, more than anything else, is what separates impactful data science from pointless number-crunching.
Again, Shakespeare’s got a quote to sum it up – to thine own self be true. It doesn’t matter how you get to a working understanding of data science, only that it suits your own needs and capabilities.
When you do get to the point where it all starts to make sense, then you focus on what you want to ask it. The right questions won’t come from a course or a blog, but from your own business challenges. In my own business, we call this #solvingproblemsthatmatter.
That’s when the music of change and success will flow free.
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