In a former life I worked on an actuarial team in healthcare. The prevailing wisdom seemed to be that every number had more meaning when expressed as a four-digit decimal to the sales or marketing people. That’s why when we shipped out reports I would get so many calls that began, “Josh, what does this really mean?”

Living and breathing the data can be a good thing, but not when it causes a disconnect between the experts and the business. Today, working with database engineers, big data engineers, and data scientists, I constantly emphasize that you can't just put numbers on a page or things that look like database fields and expect the audience to find the insights. Data needs to be accessible, humanistic versus robotic, and usable versus meaningless.

A data literacy checklist

If you’re trying to address data literacy in your organization, make sure that you stress several elements of data literacy with your analyst or the team about to produce a report. This includes considering: 

  • The appropriate extraction of the data, and whether you are using the right charts to display it
  • The ideal user experience, so end-users are clear about what the report is pointing out
  • Whether you’ve given the end-users the ability to make their own arguments using the visualizations
  • The proper elevation of the message to people who are not data experts 
  • Perhaps most importantly, the value of the data in making better decisions

Although our executives may have some data science background, this is not the reason they are in charge of the company. They’re also tight for time. So the more complicated you make the end solution, the tougher it becomes for everyone. 

Just as a young child can pick up an iPad and use it without instruction, our users should find everything we produce intuitive. The ability to understand intuitively is the easiest way to bridge data literacy. What’s really hard is to say more with fewer words and less complicated visuals and charts. But if you look to some of the classic visualizations of all time, they manage to convey complexity but communicate it simply. It’s a tall order, but one worth shooting for as data leaders.