The challenge of recognizing and understanding new ideas is so ancient that we can reach back to the New Testament and read about a frustrated Matthew as he explains why he has to tell stories (parables) to help people understand the news that he brings them.
“Therefore I speak to them in parables; because while seeing they do not see, and while hearing they do not hear, nor do they understand.” Matthew 13:13
But telling stories, even ones with profoundly teachable moments, has its limits when the idea that must be understood is complex and its impact is pervasive. That’s where numbers come in. Numbers put us on solid ground when we want to talk about big, universal concepts.
“Some people make arguments by telling stories; other people make arguments by counting things.” (1)
The quote above comes from an article about income inequality that compares using stories and using numbers to make the argument about why a large wealth gap is harmful to society. The author, Jill Lepore, contrasts the story argument approach used in a book on the topic, Our Kids: The American Dream in Crisis by Robert Putnam to the numbers approach used by an economic index developed in 1912 by Corrado Gini to measure national income inequality. She asserts that numbers alone have their limitations as an argument technology (2) and cites an historian of science, Theodore Porter, to explain why.
‘“Quantification is a technology of distance….Reliance on numbers and quantitative manipulation minimizes the need for intimate knowledge and personal trust.” …But quantification’s lack of intimacy is also its weakness; it represents not only a gain but also a loss of knowledge.’
We need numbers, to be sure. But numbers alone do not make the kind of compelling arguments that are a pathway to knowledge. The most compelling arguments, the ones that persuade, include both numbers and stories. We want to be both emotionally moved, compelled in some way, and we want to know that we are on solid ground, not simply swept away by our feelings. The definition of argument includes emotion – heat and persuasion – and a presentation of facts or reasons. Arguments are the ultimate mash-up.
Argument: an exchange of diverging or opposite views, typically a heated or angry one; a reason or set of reasons given with the aim of persuading others that an action or idea is right or wrong.
We don’t use the term argument too much anymore in organizations – we strive to have fact-based discussions. Fact-based is code for numbers. But when numbers monopolize discussions, the exchange of ideas becomes strangely neutered. Most of the discussions we have in organizations are arguments devoid of heat and persuasion and they frequently fall short of helping a group reach a decision. In order to really “see,” we need feelings and facts.
Stories are not the only way of bringing feeling to an argument that leads to fuller knowledge than numbers alone can provide. Surprisingly, the imperative to bring feeling to numbers is taking place in the world of big data analytics. As it becomes possible to amass unimaginable amounts of data and represent it visually in various forms, it is paradoxically becoming important to develop a “feel” for how to visualize data so that it tells a story. For example, genetic scientists at Albert Einstein College of Medicine in New York have sought help from a painter and conceptual artist to “reimagine the way they represent their data…[because the] problem today is that biological data are often abstracted into the digital domain,…and [geneticists] need some way to capture the gestalt, to develop an instinct for what’s important.” (3)
The instinct for what’s important in this case comes from having a “good eye” to identify possible hooks or pivot points that can unspool meaning from data so that it tells a story. Developing this “good eye” is a capability all of us have. At some early point in our lives, we used what is called perceptual learning to detect subtle differences that allow us to discriminate between similar but different objects or shapes – the numbers 3 and 8, for example. We rely on this ability without thinking. It is an instinct.
Usually we develop these instincts slowly, over time as a result of many, many repetitions. But there are ways to accelerate the acquisition of this sort of instinct. Developing this skill boils down to a particular way of practicing. For example, in a research experiment, a set of medical students learning techniques for gallbladder surgery viewed short video clips that showed the procedure at various stages and were asked to instantly decide what stage they were seeing. Students who engaged in perceptual learning scored four times higher than their peers on tests about the procedure. Perceptual learning helps us to rapidly detect subtle differences and frees us up to consider what these differences might mean.
“One thing I try to argue is that it’s not just about bigger machines to crunch more data, and it’s not even about pattern recognition,…It’s about frameworks of recognition; how you choose to look, rather than what you’re trying to see….” Daniel Kohn, painter and conceptual artist.
The way that you choose to look at something, the framework, reveals what you see. Frameworks screen some elements out and force other elements to a dominant position. The framework teases a story from the data that gives it meaning. The combination of story and numbers makes a compelling argument that has the potential to lead to new insights.
Innovation needs both artists and scientists to find ways of looking that help us see what is possible. Facility bringing together the technologies of feeling and fact is a requirement for organizations that want to build a successful innovation capability. In our time, rather than the wolf lying down with the lamb, we have geneticists partnering with painters to unlock the mysteries of the universe.
(1) “Richer and Poorer: Accounting for Inequality,” Jill Lepore, The New Yorker, March 16, 2015.
(2) based on the dictionary definition of technology: “the application of scientific knowledge for practical purposes.”
(3) “Learning to See Data,” Benedict Carey, The New York Times, Sunday Review, March 29, 2015.