Information at the Gemba

Data input doesn’t output knowledge

Published: Monday, August 29, 2016 - 16:17

Uncertainty is all around us. A lean leader’s main purpose is to develop people so they can tackle uncertainty. There are two ways to tackle uncertainty: One is genchi genbutsu (go and see, or seeing for yourself), and the other is to employ the plan-do-check-act (PDCA) cycle, a method for learning and improvement developed by statistician Walter Shewhart.

Claude Shannon, the father of Information Theory, viewed information as the possible reduction in uncertainty in a system. In other words, greater uncertainty presents a greater potential for new information. This can be easily shown as the following equation: New information gain = reduction in uncertainty.

Shannon called uncertainty “entropy” based on advice from his friend John Von Neumann, a mathematical genius and polymath. The entropy in Information Theory is not exactly the same as the entropy in thermodynamics. They are similar in that entropy is a measure of a system’s degree of disorganization. In this regard, information can be viewed as a measure of a system’s degree of organization. Shannon recalled his conversation with Von Neumann, which I quote here from Scientific American (1971), vol. 225, no. 3, p. 180:

“My greatest concern was what to call it. I thought of calling it ‘information,’ but the word was overly used, so I decided to call it ‘uncertainty.’ When I discussed it with John von Neumann, he had a better idea. Von Neumann told me, ‘You should call it entropy, for two reasons. In the first place your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, nobody knows what entropy really is, so in a debate you will always have the advantage.’”

I loved the encouragement from Von Neumann that Shannon would have an advantage in a debate since “nobody knows what entropy really is.”

I’m not going into the mathematics of Information Theory here. In fact I’m not even going to discuss Information Theory but rather the philosophical lessons from it. From a philosophical standpoint, Information Theory presents a different perspective on problems and failures at the gemba. When you are planning an experiment and things go well, and the results confirm your hypothesis, you don’t learn any new information. However, when the results don’t match your hypothesis, there is new information available for you. Thus, failures or similar challenges are opportunities to have new information about your process.

The following are seven lessons I want to share.

Information gain ≠ knowledge gain: One important lesson from information available at the gemba is that information does not translate to knowledge. Information is objective in nature and consists of facts. This information gets translated to knowledge when we apply our available mental models to it. This means there is potentially a severe loss based on the receiver. For example, imagine Sherlock Holmes and Dr. Watson at a crime scene; they’re both looking at the same information available, but Holmes is able to deduce more.

Be open: When you assume full knowledge about a process, you are unwilling to gain knowledge from additional information available. Welcome additional information and be open to other people’s viewpoints. They may have a lot more experience and more opportunities for gathering information. Increase the possibilities to learn something new.  

Go to the gemba: Most of the time, the source of information is the gemba. When you don’t go to the source, the information you get won’t be as pure as it could be. Instead, the information you get has been contaminated with the subjective perspective of the informer. Go to the gemba as often as you can. The process is providing  information at all times.

Exercise your observation skills: As mentioned in the Holmes and Watson example, what you can gain from presented information depends on your ability to identify information. There can be a lot of noise in information, and you have to weed out the noise and look at the core information available. One of my favorite definitions of information is by the famous cyberneticist Gregory Bateson. He defined information as “the difference that makes the difference.” The ability to make the difference from the information given depends mostly on your skill set. Go to the gemba more often and sharpen your observation skills. Ask “For what purpose?” and “What is the cause?” more often.

Go outside your comfort zone: One of the lessons in lean that doesn’t get a lot of attention is: Go outside your comfort zone. When you stay inside your comfort zone, you’re unwilling to gather new information. You get stuck in your ways and trust your mental model rather than challenging and nourishing it so that you can develop yourself. Failure is a good thing when you understand that it represents new information that can help you understand uncertainties in your process. You will not want to try new things unless you go outside your comfort zone.

Experiment frequently: You learn more by exposing yourself to more chances of gaining new information. And you do this by experimenting more often. The scientific process is not a single loop of PDCA. It is an iterative process, and you need to experiment frequently and learn from the feedback.

Challenge your own perspective: The Achilles’ heel for a lean leader is his confirmation bias. He may go to the gemba more often, and he may experiment frequently, but unless he challenges his own perspective, his actions may not be fruitful. My favorite question to challenge my perspective is: “What is the evidence I need to invalidate my viewpoint right now, and does the information I have hint at it?” Similar questions ensure that the interpretation of the information you are getting is less tainted.

I’ll finish off with a funny story I heard about Sherlock Holmes and Dr. Watson.

Holmes and Watson decided to go on a camping trip. All the way to the campsite, Holmes challenged Watson with observation lessons about wht they saw as they traveled. After dinner and a bottle of wine, they lay down for the night and went to sleep. Some hours later, Holmes awoke and nudged his faithful friend.

“Watson, look up at the sky and tell me what you see.”

“I see millions of stars,” Watson replied. 

“What does that tell you?” Holmes asked.

Watson pondered for a minute. “Astronomically, it tells me that there are millions of galaxies and potentially billions of planets,” he said. “Astrologically, I observe that Saturn is in Leo. Horologically, I deduce that the time is approximately a quarter past three. Theologically, I can see that God is all powerful, and that we are small and insignificant. Meteorologically, I suspect that we will have a beautiful day tomorrow. What does it tell you, Holmes?”

Holmes was silent for a moment, then spoke. “Watson, you idiot. Someone has stolen our tent!”

Always keep on learning.

About The Author

Harish Jose

Harish Jose has more than seven years experience in the medical device field. He is a graduate of the University of Missouri-Rolla, where he obtained a master’s degree in manufacturing engineering and published two articles. Harish is an ASQ member with multiple ASQ certifications, including Quality Engineer, Six Sigma Black Belt, and Reliability Engineer. He is a subject-matter expert in lean, data science, database programming, and industrial experiments, and publishes frequently on his blog Harish’s Notebook.