Being “data-driven” is a common refrain - often followed by an insistence that people consider both “quantitative” and “qualitative” data. The subtext is that, on their lonesome, numbers are incomplete, and possibly cold and insensitive. Human intuition, and compassion, is necessary to supplement dry quantitative analysis.
Logic is, in fact, incomplete - though not really in the way most people think. And the human perspective is integral to many decisions (as they impact humans) and should not be neglected.
But the best way to balance “quantitative” data (henceforth simply data) and human needs and perspectives isn’t to conflate the two. Doing so dilutes numerical explanations while skewing opinion, leading to an overconfident “data-driven” rationalization of what is actually a distorted analysis.
Data (in the modern “data science” sense of the term) is a series of observations, consistently recorded facts and information. It is inherently quantitative (even if it is categorical, messy, or unstructured), as each observation can be enumerated, and on that basis some form of statistics can be calculated (at least counts, aka quantities, for unstructured data - and that’s before applying whatever clever techniques to engineer proper features).
Qualitative information (N.B. not data) is about qualia - a concept even fuzzier than NLP and unstructured data. You can (and should!) dive into the sundry academic perspectives, but for a pithy contemporary take:
“Tragedy is when I cut my finger. Comedy is when you fall into an open sewer and die.” - Mel Brooks