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When you look at a painting or a sculpture, what do you see?
Some art is simple and we see it for what it is, like a landscape or a person, for example. Other art is more abstract. Before long, we may focus on the colors, the media used, the texture or other minute details.
Maybe we look for the artist intent, meaning or message behind the art and certainly we draw our own conclusions. We may even develop a story to go along with the painting or sculpture. Some art we fall in love with, while other pieces are just OK. And, there is some art that we may find a strong dislike toward.
People can see the same picture or sculpture and see different things. Just like with art, people can see the same data and see different things and derive different meanings. A-ha! Here within lies a barrier to improvement. While this diversity in viewpoints works just fine in the art world, it certainly makes kaizen more difficult.
I just assumed once data is proven that the data becomes fact and everyone can move forward in agreement. It is a great way to overcome opinions people bring to the table. Add going to Gemba to the get the facts and we have a powerful method for kaizen. Facts are facts, right? Not so fast, my friends. It is not that simple.
At a recent kaizen event, we wanted to make some process changes that would benefit our paying customers. But before we moved forward, we wanted to evaluated and address some of the potential process issues that would be barriers to this change. After multiple experiments and simulations, we obtained an excellent bundle of data for our kaizen effort which seemed to point us in a course of action.
At this point, I thought it would be easy to move forward when it happened. Holy Smokes, our data suddenly became art. Not everyone on the team looked at the same data and came to the same conclusion. More important, they were passionate about their viewpoint that caused disagreement between team members.
I also noticed that when our opinions run deep within us it causes us to discount or ignore data that tells us otherwise. I have learned that when data supports our opinion we accept it without much fight however when data is counter to our opinions, we tend to argue against the data. The main lesson I learned is that it is not about the numbers, it’s really about what the numbers mean.