In the intro to this series we noted that, too often, quality tools and the data we glean from them are used only to solve immediate, mostly shop-floor problems.

This story was originally published by Knowable Magazine.

Imagine a science textbook without images. No charts, no graphs, no illustrations or diagrams with arrows and labels. The science would be a lot harder to understand.

This month I read Andy Kirk’s absorbing Data Visualisation 2, or to give it its proper title Data Visualis

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.

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.