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In the digitalized process automation environment and IoT-world, subsystems and components like sensors, measuring instruments, valves, machines, etc., are becoming more and more intelligent and independent, and a huge amount of data is becoming available, so called Big Data. The factory-level computers have almost unlimited computing power and data handling capabilities. Could we actually move all the intelligence to the top level or should we utilize even more the intelligence of the smart process equipment? And what is the role of a human being in the middle of the digitalization process? Big data and analytics SW-tools, are they software nightmares or concrete support for production quality improvements?

The origin of old stories is hard to verify. This story could be true or not, but I like it anyway: The lead engineer at the famous Lockheed Skunk Works, Clarence Leonard "Kelly" Johnson, made the phrase "Keep it simple stupid", KISS. He was an advocate for frugal innovation long before the term was coined. It is told that he gave the engineering team of a new jet aircraft a set of very basic tools, such that every average mechanic could use. Then he gave guidance to the team that the new high-tech military plane they were designing must be repairable with these basic tools in the field under combat conditions.

”First find out questions relevant to your business and after that start utilizing big data and analytics successfully.” One should really pay attention to figuring out the questions that could bring useful results. For example, in “The Hitchhiker’s Guide to Galaxy” the poorly formulated ultimate question of life, the universe, and everything received an answer of “42” despite the use of a supercomputer surely capable of handling big data in all its forms.

“Do not sell us big data analytics”, said a director of a globally operating company to me a few years ago. “Sell traffic lights instead”, he continued. That is when I realized that I had been selling technology for technology’s sake and at the same time alienating customers from actually understanding how analytics could help them.