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.

That's because humans are visual creatures by nature. People absorb information in graphic form that would elude them in words. Images are effective for all kinds of storytelling, especially when the story is complicated, as it so often is with science. Scientific visuals can be essential for analyzing data, communicating experimental results and even for making surprising discoveries.

Visualizations can reveal patterns, trends and connections in data that are difficult or impossible to find any other way, says Bang Wong, creative director of MIT's Broad Institute. "Plotting the data allows us to see the underlying structure of the data that you wouldn't otherwise see if you're looking at a table."

And yet few scientists take the same amount of care with visuals as they do with generating data or writing about it. The graphs and diagrams that accompany most scientific publications tend to be the last things researchers do, says data visualization scientist Seán O'Donoghue. "Visualization is seen as really just kind of an icing on the cake."

This month I read Andy Kirk’s absorbing Data Visualisation 2, or to give it its proper title Data Visualisation 2nd Edition. The subtitle for this book is A Handbook for Data-Driven Design, which hints at how this is packed with advice.

Although the paperback version is a comfortable weight, it is astonishing how much it contains. This really is a guide for practitioners and one they will want to refer back to.

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. In the business world, more companies are trying to understand big numbers and what they can do with them. Expertise in data is in high demand. Determining the right data and measurement scales enables companies to organize, identify, analyze, and ultimately use data to inform strategies that will allow them to make a genuine impact.

Data at the highest level: qualitative and quantitative

What are data? In short, they are a collection of measurements or observations, divided into two different types: qualitative and quantitative.

“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. So while the process behavior chart may be the Swiss army knife of statistical techniques, there are times when we need to leave the knife in our pocket, plot the data, and then listen to them as they tell their story.”
—Dr. Donald J. Wheeler

I agree with Dr. Wheeler’s comment about process control charts. Yet, I’m seeing far too many of them being inappropriately used as naïve attempts to interpret the mountains of questionable Covid-19 data being produced. I’ve done a few charts myself out of curiosity but none that I feel are worth sharing. Dr. Wheeler’s two recent, excellent Quality Digest articles have been the sanest things written—with nary a control chart in sight.

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. Further research into genetics, fertilizers, and pesticides enabled us to rapidly scale food production. From early sweatshops to almost fully automated factories, we’ve learned how to scale manufacturing and get far more productivity from fewer workers and more machinery and automation.

In this manner, we’ve learned to improve the deployment of human labor, land, tools, machinery, and other capital to improve our quality of life. Now, we must fully engage the asset that we have the most of that is producing the least for us: data. It’s time to put our data to work.

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. This article will present a global perspective on the pandemic and show where the United States stands in relation to the rest of the world at the end of the third week in June.

Here we will consider 27 countries that are home to 5 billion people (67% of the world's population). According to the European CDC database, which is the source for all of the data reported here, these 27 countries had more than 75 percent of the world’s confirmed Covid-19 cases and 86 percent of the Covid deaths as of June 20, 2020. So they should provide a reasonable perspective on the worldwide pandemic. Figure 1 lists these countries by region and gives the relevant Covid-19 counts and rates as of June 20, 2020.

Figure 1: Countries used for global summary

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. And it doesn’t mean turning the cognitive wheel entirely over to artificial intelligence, either—far from it.

We might think of quality intelligence as a natural progression of quality control. It’s both granular, in that core quality tools underpin it, and forward-looking because quality data are used to improve not only products and processes but also operational performance. It’s very deliberate in that its goal is to wring the maximum value possible from reliable data.

To do this, quality intelligence employs four key tools: ensuring compliance, grading collected data, exploiting software, and implementing data strategically.

Ensuring compliance

People often assume that compliance applies solely to government or industry standards, but the term surfaces in many shop-floor conversations and processes. For instance, there is compliance to limits: Are data in specification? Are the appropriate statistical rules being met? There’s also compliance to procedures: Are people collecting data in the right way, and on time?