Too Literal with Numbers

Data analysts always face the same white-knuckle fear when they present: Will someone derail my presentation by questioning the source of a data point, the quality of the data, statistical significance, or why two numbers don’t align?

These types of inquires may appear innocent and within the field of play. I find them counter-productive. They implicitly undermine the analyst’s credibility and worse, deny the rest of the audience the opportunity to hear the full analysis narrative.

I’ve suffered through enough of these train wrecks to wonder about the underlying cause of this disruptive behavior. Here’s my theory:

People who have experience and comfort with numbers have the ability to abstract meaning from analysis for themselves—even when numbers don’t line up, the data is unclear, or the analysis has minor flaws. They can ask themselves higher-level questions: What does this mean? What are the implications for the business? How else could these results be interpreted?

In contrast, people who are uncomfortable with analytics treat numbers literally. They are disturbed by surface level inconsistencies. They expect—even need&mdas;hthe numbers to line up in straightforward ways. The medium simply isn’t familiar enough to abstract their own meaning.

Combine this uncertainty with the omnipresent pressure in business to express an opinion (any opinion) to appear smart—and you end up with wasted time spend discussing superficial irregularities.

Does this sound like the rantings of a data elitist? Fair enough, but I’d suggest that a similar phenomena is common across any field of expertise. Take art, for example. People who have little exposure to fine art are bound to ask for help in explaining the underlying meaning, remark on the superficial beauty, and find themselves attracted to the most obvious reflections of reality. Meanwhile, those who have experience can gather their own meaning, think creatively about what they are seeing and understand how it fits in context with other examples.

If this notion is accurate, where does that leave us? On the one hand, it highlights the need to understand your audience and anticipate and educate those people who are inclined to be disruptive. It also hints at some of the poorly understood realities of analysis:

  • Analysis is as much art as science. Analysis relies on the perspective and skill of the analyst. Managers might want an objective reality, but they should recognize that the "truth" won’t tell an actionable story.
  • Analysis should strive for directionally-correct results, not precision and comprehensiveness. Good analysis impacts decisions—therefore speed matters.
  • Give people something tangible and incontrovertible to hang onto. This is one reason we really like the bottom’s-up analysis approach. When you create visualizations of individual customers, people can’t argue with this granular data—and it gives them something that they can fully understand and appreciate.
  • Finally, there is nothing quite as valuable as personal credibility to make an analysis valuable. If people trust the work you have done in the past, they are likely to avoid arguing the small stuff.