Building a Knowledge Infrastructure for Utilities

Preserving institutional knowledge

When I ran an electric utility operations division, one of my favorite employees was a guy named Stanley. Stanley started as a line worker; climbed poles; became a foreman, later a supervisor; then managed all the crews in the region. I remember how Stanley worked.
As the hot, humid day would turn into evening, just when the crews returned to the service center, storm cells would start to form. If they matured, they could cause heavy rain, wind, thunder, lightning, and sometimes minitornadoes. Stanley had to decide whether to send the crews home or keep some or all the crews on overtime. No one really knew if the storm cells were going to dissipate or cause havoc to the electric system. If Stanley sent the crews home and a bad storm hit, it would take a long time to get the crews back to work. If he kept the crews on overtime and the cells dissipated, he would have wasted company money. Stanley almost always made the right call. He didn’t know it, but he was using spatial analytics in his head.
Then Stanley retired.
The average age of U.S. utility workers is almost 50. Thousands of workers like Stanley will leave the industry over the next several years. Imagine all the wisdom and analytic power that will be missing. People like Stanley know where infrastructure problems exist. They know where the utility has not trimmed trees. They know the location of old and frayed wires that are just waiting to fall down. They remember where storms generally hit and the problems storms cause.
What many utilities are missing is an ability to capture as much of that wisdom as possible before the Stanleys of the industry retire. What we need is a way to share what retiring workers know and how they know it. The common denominator of that knowledge is location. Utilities have been capturing facts in geographic information systems (GIS) for years. Today, GIS can capture observations and predictive information, collect data from all kinds of sources, and help utility staff make better risk predictions the way Stanley did. GIS can create geoprocessing models, which document the data sources, run the analysis, and produce the results in the form of a map. The key is to have these models validated and supplemented by experienced workers before they leave, so that utilities can truly build a knowledge infrastructure.

Can the utility GIS community provide a platform to build a knowledge infrastructure that leverages experienced workers before they leave?

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