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While analytics training programs usually arm their students with software skills for looking at massive datasets, statistics training programs are more likely to make those skills optional.
The bar is raised when you must contend with incomplete information.
When you have uncertainty, the data you have don’t cover what you’re interested in, so you’re going to need to take extra care when drawing conclusions.
That’s why good analysts don’t come to conclusions at all.
Instead, they try to be paragons of open-mindedness if they find themselves reaching beyond the facts.
Keeping your mind open crucial, else you’ll fall for confirmation bias — if there are twenty stories in the data, you’ll only notice the one that supports what you already believe… and you’ll snooze past the others.
Beginners think that the purpose of exploratory analytics is to answer questions when it’s actually to raise them.
This is where the emphasis of training programs flips: avoiding foolish conclusions under uncertainty is what every statistics course is about, while analytics programs barely scratch the surface of inference math and epistemological nuance.
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Without the rigor of statistics, a careless Icarus-like leap beyond your data is likely to end in a splat.
(Tip for analysts: if you want to avoid the field of statistics entirely, simply resist all temptation to make conclusions.
Job done!)Analytics helps you form hypotheses.
It improves the quality of your questions.
Statistics help you test hypotheses.
It improves the quality of your answers.
A common blunder among the data unsavvy is to think that the purpose of exploratory analytics is to answer questions when it’s actually to raise them.
Data exploration by analysts is how you ensure that you’re asking better questions, but the patterns they find should not be taken seriously until they are tested statistically on new data.
Analytics helps you form hypotheses, while statistics let you test them.
Statisticians help you test whether it’s sensible to behave as though the phenomenon an analyst found in the current dataset also applies beyond it.
I’ve observed a fair bit of bullying of analysts by other data science types who seem to think they’re more legitimate because their equations are fiddlier.
First off, expert analysts use all the same equations (just for a different purpose) and secondly, if you look at broad-and-shallow sideways, it looks just as narrow-and-deep.
I’ve seen a lot of data science usefulness failures caused by misunderstanding of the analyst function.
Your data science organization’s effectiveness depends on a strong analytics vanguard, or you’re going to dig meticulously in the wrong place, so invest in analysts and appreciate them, then turn to statisticians for the rigorous follow-up of any potential insights your analysts bring you.
Choosing between good questions and good answers is painful (and often archaic), so if you can afford to work with both types of data professionals, then hopefully it’s a no-brainer.
Unfortunately, the price is not just personnel.
You also need an abundance of data and a culture of data-splitting to take advantage of their contributions.
Having (at least) two datasets allows you to get inspired first and form your theories based on something other than imagination… and then check that they hold water.
That is the amazing privilege of quantity.
Misunderstanding the difference results in lots of unnecessary bullying by statisticians and lots of undisciplined opinions sold as a finished product by analysts.
The only reason that people with plenty of data aren’t in the habit of splitting data is that the approach wasn’t viable in the data-famine of the previous century.
It was hard to scrape together enough data to be able to afford to split it.
A long history calcified the walls between analytics and statistics so that today each camp feels little love for the other.
This is an old-fashioned perspective that has stuck with us because we forgot to rethink it.
The legacy lags, resulting in lots of unnecessary bullying by statisticians and lots of undisciplined opinions sold as a finished product by analysts.
If you care about pulling value from data and you have data abundance, what excuse do you have not to avail yourself of both inspiration and rigor where it’s needed? Split your data!If you can afford to work with both types of data professionals, then hopefully it’s a no-brainer.
Once you realize that data-splitting allows each discipline to be a force multiplier for the other, you’ll find yourself wondering why anyone would approach data any other way.
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