The Flood: An Analysis

In my previous post I told the admittedly harrowing story of my near-demise in a flood caused by ‘Superstorm’ Sandy.  I consider this a personal intelligence failure of epic proportions.

Why would I — who sell my research and advice to companies on things related to threat, early warning, and shifting trends — publicly acknowledge this personal shortcoming in my own behavior?

You could say it’s part of the confessional ‘new intimacy’ in journalism.  But frankly I sense that my experience, far from being atypical, is characteristic of the way individuals and organizations (groups of individuals) misuse ‘intelligence’.  These are mistakes most of us make much of the time.  Only if we can harvest a little insight about why they happen, can we begin to cut down on them.

In other words, I had inadvertently turned my life into a field experiment in applied intelligence — albeit one completely uncontrolled.

A KVC analysis

In my monograph “The Knowledge Value Chain:  How to Fix it When It Breaks”, I put forth the idea that intelligence, far from being a ‘cycle’ as often portrayed, is actually a linear connection that leads from data up through intelligence and up through the production of value.  One of the axioms of the model is that it’s essentially serial in nature—each step must be adequately fulfilled in order for the whole process to work.  If a link in the chain breaks, the chain itself breaks.

KVC troubleIn this case, the bottom of the chain, DATA, was very much in place:  I had plenty of data.  As background, I had seen the movie An Inconvenient Truth when it first came out, and will always remember the scene simulating the overflowing of the Hudson River into downtown Manhattan (which is where I live, and which is essentially what happened.)  More immediately, all day on Monday, October 29, 2012 there were Twitter feeds (especially from @EricHolthaus) about where Sandy currently was, and what the effect on tidal levels in New York Harbor was likely to be.  In hindsight, these alerts were amazingly accurate.

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