Everyone is more comfortable with the qualitative than they are with the quantitative. It’s just easier to eyeball something than it is to precisely measure it over and over in a way that can seem needlessly repetitive. Quantifying things is hard, after all—harder in many cases than describing something’s overall quality or leaping to a conclusion based on just a few observations and intuitions.

But it’s always—even in creative contexts—more useful to analyze what enables the quality of something than it is to perform a quick appraisal and call it a day. Employing data analysis causes us to arrive at less biased, more accurate conclusions about the quality of something. And that’s why adding data analytics to your arsenal of skills is crucial.

Data helps us acquire a more truthful snapshot of reality. Take fitness trackers as an example of this: the quality of a workout is easy to comprehend holistically when you have completed it, you may think—the sweat beading on your forehead or dripping off your nose, the heart thudding in your chest—but if you are like me, you may think you are on the verge of death before you have actually reached your limit. The mind is a curious thing, seeking always to conserve energy and halt pain, and thus can trick itself quite easily. But numbers don’t lie unless humans have forced them to (either intentionally or unintentionally). A fitness tracker on your wrist may not be the most accurate, but it’s more accurate than your telltale physiological signs may suggest. It can estimate you are a few hundred steps shy of your goal of 10,000 steps per day, when you feel you have already walked miles and miles. Perhaps it doesn’t track elevation gain, so you’ve actually expended more energy and burned more calories than it estimates, but when it comes down just to counting steps, it has you beat. It aids you in creating a clearer picture of what is taking place in your workout. Accordingly, you may adjust your conclusions and alter your courses of action for a better outcome.

That’s exactly how data is being employed in businesses—from marketing to finances to customer experience to HR practices to user experience. Data analysis married to qualitative observation helps create clearer pictures of what is happening so that the business can be healthier.

In situations where data collectors are more sophisticated than fitness trackers (and consequently more reliable), assessment of quality is even more important and human judgment is even more frail on its own. Take healthcare providers, for example. As regulations compel hospitals to begin assessing the quality of care provided across the spectrum more carefully in order to get reimbursements, analyzing which clinics or individual physicians are performing well becomes more and more important in at least a financial sense—and it was always important in a purely human sense. But human judgment is notoriously suspect in these matters. We are likely to rate a doctor with a poor bedside manner but excellent performance as overall worse than a congenial physician who actually made a few mistakes. To avoid such biases and reinforce whichever assessments we’ve made that are correct, we must dive into the vast volume of data points generated by a single visit to a hospital (patient vitals at time of entry, medication orders, adherence, post-visit checkups, etc.) and try to figure out which factors are most important in determining what went well and what went wrong.

In our current era, where data-driven practices are being applied across the board, it’s important to remember that what has staying power is the marriage between data and the human element. It’s easy to forget that whichever software programs you use to crunch numbers are still only crunching the numbers you gave them. Even if you were using artificial intelligence algorithms that worked independently, you would still have to assess their performance and whether the correlations are robust enough to justify conclusions. (Crime rates have often been seen to rise in the summer—is that due to rising temperatures, leading to more irritability? Longer daylight hours should discourage burglars, though, right? Or are crimes simply better reported in the summer because there are more people on the streets? No matter how much data you bring to this scenario, you need people interpreting the data.)

In the end, it’s your judgment that will stand as the end-all, be-all. Using data analysis to parse out situations will help strengthen that judgment, however. If used correctly, data can only help improve your judgment.

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