The Art of Defining a Problem and How it Affects Results
original article on andersonmatias.co
“What’s the Problem?”, say one. “That’s the problem.”, says another. Everyone understands the concept of what a problem represents and consequently the idea of “Problem Solving”, but are we really determining it in a standardized way? You bet not. Truth is, the perception of what is an issue and what isn’t is deeply personal. Each and every one has their own version of what bothers them and what should be changed. It can be what starts an argument about creed, politics, the environment and so on. This is basically because everybody sees reality differently, has their own values, ethics notion and sense of what’s right or wrong.
If in our personal lives this simple aspect can be troublesome and complicate things, can you imagine the impact of that in business? It can be catastrophic! And that basically comes down to one simple thing we are normally eager to do in a corporate and competitive environment: assumptions. I say we are eager to do it because it is simpler to assume something than analyze or deep dive on the subject until we get a clear understanding of what’s going on, what outcome is expected and how do we proceed. There is sometimes too much pressure for an answer when in fact you don’t have anything to back it up, so you end up not with the most effective solution, but with the quickest and easiest to report.
Let’s talk a bit about Six Sigma for a second. You see, through the methodology, one can find a very structured and logical sequence to understand a problem, determine root cause, propose improvements and establish how to sustain the gains. These steps are known as DMAIC (Define, Measure, Analyze, Improve, Control). On the very beginning, through our Define phase, is when you need to get a deep understanding of what is considered a problem, how do you describe it, quantify it and what kind of benefit will you get from working on this issue. That’s probably one of the most important parts of the process, as it is going to give you a direction to take on every other phase that follows. During the time I’ve worked with Quality Continuous Improvement and led several Six Sigma Projects, however, there were often team members trying to rush into a solution right away, because of the assumptions he/she made about what the problem likely was (and of course the pressure for results). One would find a proposed “solution”, implement it right away without knowing the root cause, to only then try to justify the actions taken through the methodology, which means bending the system by filling out papers for tracking purposes. This clearly doesn’t work. Imagine the scenario where the project would last 6 to 8 months to be concluded and only when you were in the Improve Phase you realize that your Define Phase was not well performed and you are now with solutions to everything but the problem you were supposed to solve. Months wasted, several hours of work gone and potentially tons of money lost either by the missed opportunity or by the resources already compromised.
We see such symptoms when starting a business and working on Startup ideas too. How often do we hear of stories from people that had the next “great idea” and went to hustle right away without actually understanding what was the problem they were trying to solve, if there were any? That’s how some end up with products that no one wants to buy, which means, no value. For some, there is no recovery to it and the venture dives into a quick end. That is how important it is to really understand and have a deep awareness of how to describe the problem you are solving.
How to solve it then? Data. Being data-driven is more than a fancy skill you want to put on your CV, it is the best way to mitigate the risk of taking the wrong direction whenever you are starting a project or venture. The best source of data? Your customer. By customer, I mean the broad version of it. It is not just the people that actually buy a product, is everyone that benefit from your work somehow. It is obvious when someone downloads an app, sees an issue and gives a feedback, or buys a car and go to the dealer to repair something, but depending on the kind and scope of the problem you are dealing with, you will need information from internal customers as well. Most important is to gather reliable data, from the right source and that also properly represents and relates to the Y(x) function (effects versus causes) that best describes the issue. Defining the right Process Inputs and Outputs to analyze is also key and you will always prefer to deal with quantifiable and objective metrics instead of subjective ones. This is why variable data (normally numeric: millimeters, liters, watts etc) deliver easier-to-infer insights than attribute data (yes/no, ok/nok, pass/fail types), with less sample size to reach statistical significance, which normally means more efficiency to provide results…
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