Outlier
a statistical observation that is markedly different in value from the others of the sample
Today I had lunch with a person who I don’t know all too well. Yet our time together was very meaningful and impactful. Our meeting uncovered greater significance, however, as he told me he’d had a massive heart attack three months ago and almost died. The scary part is that this man is as fit as I have ever seen a 45 year old. As we discussed his health event over lunch, the word “outlier” kept coming up. His case is far outside the middle of the bell curve in regard to this type of health event. He had no warning signs, no history, zero indication that he’d have a massive blockage in his heart. His numbers were and still are “perfect” according to his doctor. He had one small episode prior to the big one: he fainted while doing a strenuous cross-fit boot camp. He went to the hospital and they gave him tests upon tests. They said he could run a marathon — he crushed the stress test. They sent him home, saying he was “probably dehydrated.” Weeks later, he lay on a soccer field for 10 minutes with no pulse — kept alive by a doctor who was fortunately on site to perform CPR. A single piece of arterial plaque from somewhere in his body clogged a major pathway attached to his heart. A simple and effective killer emerged out of nowhere. Defeated by the expertise and knowledge of his caregivers.
The discussion left me puzzled and confused. “What did the doctors say? Of course there was some weird thing they missed, but can now explain?” I implored. “What’s the root cause?” He said there have been no answers — also very few real credible theories or even a plan to dig deeper. We talked about how the medical and research communities focus primarily on solving for the middle of the bell curve. The mass market. Not the outlier. Most of the effort has been focused on those who show overt signs with identifiable causes: “Smoke? Yes. Drink? Yes. What are your numbers? OK — take this pill.” Or “we’ll have you in and out in a jiffy — we have a stent to solve for the problem. We’ve got this.”
We talked about how some of the doctors he’s come into contact with specifically indicate they have heard of his case and want to talk to him. Likely rooted in the fact that he is the outlier. The scientist in those doctors wants to speak to the outlier in the flesh. They all want to meet him and hear the story, yet apparently none so far have actually wanted to follow-up and go deeper — to find something in his case that uncovers a fringe area in cardiac medicine or some other connection to inflammation. In fact, none of the professionals have supported testing at a genetic level or around other non-core areas of their practice to determine if there are other drivers that can be discovered to explain the event. It sounds like the professionals are drawn back to the mass market. They have to. Its where the most problems and business reside. It makes sense.
I connected with this scenario — and thank God I’m not saving lives but merely trying to build or fix businesses in my ‘practice’. Everyday we go into projects looking at the most obvious problems and symptoms. It’s so easy to ask our clients, partners and employees the obvious questions. To put things in boxes and categories, breaking them down into small containers that can be analyzed and tested. We have a general process that helps us identify the pain points. We find what works for them, and then drive it to scale so it can make money. Being smart inside the bell curve is easy when you know the process, the ropes, the typical signs, and the rules.
Focusing on the outliers requires much more courage, patience, commitment, and more time than we ever expect. And that’s the challenge. Because the outliers need someone to test the genetics and ask the questions that aren’t asked everyday and watch how the data uncovers new concepts that often come to the surface over longer periods of time. It’s important to connect with the outliers in the flesh, and walk beside their outlier-ness.
Sometimes problems worth solving don’t start with the obvious. They don’t appear in the familiar spots, with the identifiable symptoms that are commonplace and easy to detect. Sometimes we have to start with the outliers. It’s not easy to deeply engage with the outliers because they might not pay off. But sometimes the outliers teach us things we didn’t know. And those lessons lead us to new insights that sometimes uncover answers to bigger questions. And those questions lead us towards new problems worth solving.
