• by Gord Hotchkiss , Featured Contributor, February 6, 2014

Last week, I talked about our ability to ignore data. I positioned this as a bad thing. But Pete Austin called me on it, with an excellent counterpoint: “Ignoring data is the most important thing we do. Only the people who could ignore the trees and see the tiger, in real time, survived to become our ancestors.”

Too true. We’re built to subconsciously filter and ignore vast amounts of input data in order to maintain focus on critical tasks, such as avoiding hungry tigers. If you really want to dive into this, I would highly recommend Daniel Simons and Christopher Chabris’ “The Invisible Gorilla.” But, as Simons and Chabris point out, with example after example of how our intuitions (which we use as filters) can mislead us, this “inattentional blindness” is not always a good thing. In the adaptive environment in which we evolved, it was pretty effective at keeping us alive.  But in a modern, rational environment, it can severely inhibit our ability to maintain an objective view of the world.

Austin also had a second, even more valid point: “What you need to concentrate on now is 'curated data,' where the junk has already been ignored for you.”

And this brought to mind an excellent example from a recent interview I did as background for an upcoming book I’m working on.  This idea of prefiltered, curated data becomes a key consideration in this new world of Big Data.

Nowhere are the stakes higher for the use of data than in healthcare. It’s what led to the publication of a manifesto in 1992 calling for a revolution in how doctors made life-and-death decisions. One of the authors, Dr. Gordon Guyatt, coined the term “evidence-based medicine.” The rationale is simple here. By taking an empirical approach not just to diagnosis, but also to the best prescriptive path, doctors can rise above the limitations of their own intuition and achieve higher accuracy. It’s data-driven decision-making, applied to healthcare. Makes perfect sense, right? But even thought evidence-based medicine is now over 20 years old, it’s still difficult to consistently apply at the doctor to individual patient level.

I had the chance to ask Dr. Guyatt why this was the case. “Essentially, after medical school, learning the practice of medicine is an apprenticeship exercise, and people adopt practice patterns according to the physicians who are teaching them and their role models," he said. "There is still a relatively small number of physicians who really do good evidence-based practice themselves in terms of knowing the evidence behind what they’re doing and being able to look at it critically.”

The fact is, a data-driven approach to any decision-making domain that previously used to rely on intuition just doesn’t feel, well, very intuitive. It’s hard work. It’s time-consuming. To Austin’s point, it runs directly counter to our tiger-avoidance instincts.

Dr. Guyatt confirms that physicians are not immune to this human reliance on instinct: “Even the best folks are not going to do it. Maybe the best folks -- but most folks are not going to be able to do that very often.”

The answer in healthcare, and likely the answer everywhere else where data should back up intuition, is the creation of solid data-based resources, which adhere to empirical best practices without requiring every single practitioner to do the necessary heavy lifting. Dr. Guyatt has seen exactly this trend emerge in the last decade: “What you need is preprocessed information. People have to be able to identify good preprocessed evidence-based resources where the people producing the resources have gone through that process well.”

The promise of curated, preprocessed data is looming large in the world of marketing. The challenge is that, unlike in medicine, where data is commonly shared and archived, in the world of marketing much of the most important data stays proprietary. What we have to start thinking about is a truly empirical, scientific way to curate, analyze and filter our own data for internal consumption, so it can be readily applied in real-world situations without falling victim to human bias.