Data are valuable assets, so much so that they are the world’s most valuable resource. That makes understanding the different types of data—and the role of a data scientist—more important than ever.

“With data from an epidemic there is no question of whether a change has occurred. Change is everywhere. The question is whether we are getting better or worse.

Throughout human history we’ve constantly sought out tools and capital to make us more productive. From the formation of basic tools to assist in farming to real cultivation and shaping of the land for greater yields, humankind learned to grow food.

In May 2019, James Beagle and I published an article that contained tables for the analysis of mean moving ranges or ANOMmR (pronounced a-nom-m-r). By request of those using this technique, I have expanded these tables. This article contains these expanded tables and

The daily Covid-19 pandemic values tell us how things have changed from yesterday, and give us the current totals, but they are difficult to understand simply because they are only a small piece of the puzzle.

What is quality intelligence, exactly? It’s more than marketing spin. More, even, than the sum of its many control charts. It’s not collecting data simply to further go/no-go actions.

An organization can achieve great results when everyone is working together, looking at the same information generated from the same data, and using the same rules.

Blame it on Moore’s law. We live in a digital Pangaea, a world of borderless data driven by technology, and the speed and density with which data can be transmitted and handled.

It’s no secret that manufacturing companies operate in an inherently unstable environment. Every operational weakness poses a risk to efficiency, quality, and ultimately, to profitability.

For nearly a century, statistical process control (SPC) has been the cornerstone of quality management and process control. But traditional SPC can’t keep up as the pace of manufacturing accelerates.