What’s your problem?

“A problem well stated is a problem half solved.”
Charles Kettering
Inventor of electrical automobile ignition

I first came across this quote in Douglas W. Hubbard’s book titled “How to measure anything”.  This book will change how you view measurement.  Hubbard defines measurement as the ‘reduction of uncertainty based on one or more observations’.  So often we talk of the ‘intangibles’ of our work that are just too difficult to put a metric to or measure.  Can you measure creativity?  Can you measure wellbeing?  The answer is yes according to Hubbard, if you ask the question the right way.  When challenged by questions like the ones above, he responds with his five step Applied Information Economics method.

  1. Define a decision problem and the relevant uncertainties
  2. Determine what we know already
  3. Compute the value of additional information
  4. Apply the relevant measurement instrument(s) to high-value measurements
  5. Make a decision and act on it

The defining of a problem is the step we most stumble on.  Hubbard would start with asking the following questions.  “What do we mean by wellbeing?  Why do you care about wellbeing?  What is the dilemma with wellbeing?”  These questions are not concerned with how to measure wellbeing but more about discovering what the real problem is.  Articulating what we actually mean by answering the above questions starts to drill down and remove ambiguity.  Hubbard states

“if a measurement matters at all, it is because it must have some conceivable effort on decisions and behaviour.  If we can’t identify a decision that could be affected by a proposed measurement and how it could change those decisions, then the measurement simply has no value.”

Once we have articulated the problem, the next step is to determine what we know already.  In an age of information saturation, schools have so much data to call on.  The challenge lies with how to make it work for you.  Hubbard lists four useful measurement  assumptions:

  1. Your problem is not as unique as you think
  2. You have more data than you think
  3. You need less data than you think
  4. An adequate amount of new data is more accessible than you think

These assumptions assist with reducing uncertainty about decisions by developing upper and lower ranges.  100% certainty is not always attainable but we can reduce uncertainty by speaking to others with the same problem, taking stock of all the data that you do have currently around this area, using the rule of five to reduce uncertainty and the development of new metrics once the problem is clearly articulated.

Step 3-4 of Hubbard’s Applied Information Economics method analyse the value of the information you have and explore basic measurement methods to help reduce uncertainty. The value of information is the chance of being wrong times the cost of being wrong.  These chapters definitely blew the cobwebs off my math knowledge and honestly were challenging to read late at night (sorry mathematicians!).  An interesting point that Hubbard raises is that we tend to measure what we know how to measure and that we can have greater gains by exploring a range of easy to use and validated measurement techniques such as various sampling procedures or types of controlled experiments.

The final step is the optimum decision and not always a yes and no decision.  It is a decision with the greatest reduction of uncertainty and risk.

For anyone looking to improve their capacity to find problems and develop solutions, I implore you to read this book.



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