The Chain of Clarification and the Wall of Clarity

‘What do you mean by that?’

Scott Berkun’s post, The Best Definition of Innovation, points out a very important issue about how we solve business problems:

The best thing is ask anyone who uses the word innovation is: What do you mean when you say that?

Even in what he regards as the best definition of innovation:


There’s still the question of “what does significant mean”.

So here’s something that’s very, very important –

In the context of solving business problems:

  1. All problem statements can always be clarified to reveal the goals behind them;
  2. All goals can always be clarified to reveal a certain kind of measurement necessary to determine whether the goals are met;
  3. That measurement can, in turn, always be clarified to reveal a certain set of criteria used to implement that measurement.

The Process, the Chain and the Wall

That process of clarifying is what I call the Chain of Clarification. And that set of criteria is what I call the Wall of Clarity.

So generally speaking:

  1. When we start to address a problem, we start by clarifying what we mean in our description or interpretation of the problem, to identify the goals we want to meet in the potential solutions.
  2. Then we continue to clarify what we mean by meeting those goals, to identify the kind of measurement we need to decide.
  3. Once goals are tentatively defined, we clarify further to identify what kind of observable and testable criteria we can create to realize that measurement.
  4. As soon as an initial set of criteria is defined, we start reverse-engineering from the criteria back to the action plan.
  5. The reverse-engineering process is iterative, a constant back-and-forth or loop of creative exploration.
  6. Eventually, new insights we gained during the exploration are brought in; the initial problem statements are very likely modified or changed all together.
  7. The result: we get a much much better idea about the nature of the problem and the scope of the potential solutions.

What is that?

Interestingly, but perhaps not surprisingly, that’s one very basic technique to solve problems.

While, of course, when we trim down the subject matter to just defined goals and criteria, we sacrifice a lot of dynamics inherent in both the problem and the solution. That’s where domain knowledge, experience, big data, and sheer human judgement can help.


When trying to solve problems, always ask:

  1. What’s our chain of clarification?
  2. Have we hit the wall of clarity?
  3. What have we learned on the way back to action plan, from the wall of clarity?


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