Being ‘Data-Driven’ Isn’t Compatible with Being Innovative gave me fuel for this post, written for you.
Here are some good reminders about data sets, analysis, and application. Consider them when being “data-driven”. Keep it in mind when listening to others claiming to be “data-driven”. And apply it to your critical thinking and general life decision-making!
Know What You’re Getting Into, and Stick to Your Objective
“Maximizing big data’s strengths starts with recognizing its limitations. This begins with understanding what information is being gathered. Is it complete? Can it be analyzed? Is it representative? And – most importantly – is it actionable?”
Never Forget the Difference between Causation and Correlation
Mixing the cause of a phenomenon with correlation. If one action causes another, then they are most certainly correlated. But just because two things occur together doesn’t mean that one caused the other, even if it seems to make sense.
Revenue and website traffic. You might find a high positive correlation between high website traffic and high revenue, but that doesn’t mean that high website traffic is the cause for high revenue. There might be a common cause to both or an indirect causality that makes high revenue more likely to occur when high website traffic occurs.
Proving the null. One of the ways to prove causation is to try to eliminate the variable that you suspect is causing the phenomenon.
For example, if you found high correlation between number of leads and number of opportunities (a classic B2B data question), you might infer that a high volume of leads will lead to a high number of opportunities.
To prove that a high volume of leads causes a high volume of opportunities, try to eliminate the variable (note: the variable is not leads, but a high volume of leads). Change the volume drastically and see if it has an impact on the number of opportunities. You might find that the number of opportunities doesn’t change, which will, in turn, allow you to cut costs on lead generation.”
Determine How You Will Use Your Information Wisely, Fully Understanding the Consequences of Your Decisions
“When we mistake correlation for causation, we find a cause that isn’t there. Once upon a time, perhaps, these sorts of errors—false positives—were not so bad at all. If you ate a berry and got sick, you’d have been wise to imbue your data with some meaning. (Better safe than sorry.) Same goes for a red-hot coal: one touch and you’ve got all the correlations that you need. When the world is strange and scary, when nature bullies and confounds us, it’s far worse to miss a link than it is to make one up. A false negative yields the greatest risk.
Now conditions are reversed. We’re the bullies over nature and less afraid of poison berries. When we make a claim about causation, it’s not so we can hide out from the world but so we can intervene in it. A false positive means approving drugs that have no effect, or imposing regulations that make no difference, or wasting money in schemes to limit unemployment. As science grows more powerful and government more technocratic, the stakes of correlation—of counterfeit relationships and bogus findings—grow ever larger.”