If life ever feels mundane, find some friendly local data scientists and ask them what they’re working on. For bonus points, ask them what they’d like to be working on.
Then, sit back and bask in the unadulterated glory of undiluted jargon. If they’re worth half their salt you’ll at least hear about big data, dark data, structured data, unstructured data, semi-structured data, and probably more types of data than you knew there were words for.
You know how they say Eskimos have over a hundred words to describe snow? That’s nothing compared to data scientists when they get going.
They’ll probably talk about decision trees, MapReduce, SQL, gradient boosting, support vector machines, supervised learning, machine learning, and lots of other things that sound really cool but also confuse the heck out of you. As far as mind-benders go, the next step is probably string theory.
Who doesn’t love a quark?
Facing a firehose of jargon, it’s easy to get lost in the noise. Let’s be honest; most of us weren’t that great in Algebra 101, let alone Intro to Calculus. Business analytics is hard, right?
Not really. I’ll let you in on what’s probably the best-kept secret in the discipline. It’s actually a two-fer:
1) The benchmark isn’t how smart something is. It’s whether or not it’s better than what you’re currently doing. There are only two ways to drive a better outcome. Make it more sophisticated or make it more timely. Cut through the confusion and it really is that simple.
Sophistication is a seductive temptress. It’s always possible to make things more targeted, more effective, or more efficient. In the long run, perfection isn’t just an ideal; eventually, it becomes the goal.
Unfortunately, in the long run we’re also dead. It’s better to get something working, even if it’s not perfect, than it is to hold out for the ideal solution. If the current benchmark is walking to the shops, don’t try to fly to the moon on day one. Just try to make a scooter. You’ll have more success: people like a working scooter more than they like an imaginary rocket. That takes us to the second rule…
2) Making things better only happens in two ways. Either make it smarter or make it faster.
If you treat all your customers exactly the same, go and stand in the corner. You’re not customer-centric; bad marketer!
Just split them, even if it’s based on simple rules. If you’ve already split your customers, try using analytical segmentation to better target. Just remember this: while the simple things usually make the biggest difference, it’s the complex things that eventually create competitive differentiation.
If you’re trying to flag fraud and you’re reviewing transactions on a weekly basis, your exposure is whatever you didn’t pick up in the last week. If you look at things daily, your exposure is limited to a day’s worth of transactions. If you review in real-time, you don’t have any exposure. The more timely you can make your decision-making, the better the outcome you’ll achieve.
And that’s pretty much it! Your data scientist certificate is now in the post. Just don’t try and get into an argument about the relative merits of Bayesian or frequentist statistics. You’ll regret it.
Besides, we all know frequentists rule.
Evan Stubbs is the author of The Value of Business Analytics, a book that explains why teams fail or succeed. His most recent book, Delivering Business Analytics explains the link between business analytics and competitive advantage, outlines the Data Scientist’s Code (a series of management principles that move organisations towards best practice), and provides solutions to twenty-four common business analytics problems.