Safe Driving in Data Blizzards

I just finished reviewing two hefty reports on unrelated subjects.  Their common feature is reliance on huge amounts of data to evaluate new ideas.  To protect the authors, I won’t mention their topics. I’m very interested in both topics, and I believe in good analytics.  To borrow from Mark Twain, I not only believe in good analytics, I’ve seen them.  They were pretty common even in the ancient days before personal computers.

The analysis and discussion in the reports revolved around crunching numbers…more numbers than anyone would dream of crunching without a computer…the kind of analysis that any large or complex business or organization does routinely every day.  For good reason.  “Business as usual” generates a lot of data, and not crunching it and seeing what it is telling you can be fatal.

The reports are from reputable people and organizations.  So, they are written to be bullet proof, i.e. no faulty data or implausible results.   The data used is the best available.  The models used are the best available.  But the reports have a common deficiency.  Crunching business as usual data using business as usual models results in….guess what?   All options look like business as usual.  Not much help deciding whether to change direction.

Not knowing whether to change direction has consequences.  Disruptions and trends in the world these days will determine how our energy systems need to adapt or transform…changes in technology, relative costs, and the competitive need at all scales of energy use to respond both opportunistically and strategically.

Imagine driving at 90 miles per hour in a blinding snowstorm.  Obviously unsafe.  No one would do it even if there were no other cars on the road.  But our permanent energy data blizzard does tend to obscure the road ahead, and our current circumstances don’t allow us the option to slow down.

Pretty scary.  But there is a way of improving visibility.

An old axiom says that the first and most important step in effective problem solving is defining the problem.  This requires insight and valid assumptions more than huge databases and powerful number crunching software.  It’s a matter of knowing which assumptions really matter and focusing on howthey affect outcomes…also having a feeling for how a very small number of key assumptions are changing, and finally, understanding that tomorrow’s business as usual will be today’s visionary scenario. - Gerry Braun

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