Universal data literacy: A wonderful goal (but perhaps not a realistic one).May 26, 2022
Maybe you have heard recent calls for universal data literacy.
It’s a wonderful goal: that every employee develops the ability to derive meaningful and useful information from data and to apply this to create better products, services and experiences. (datatothepeople.org).
Data literacy consists of three general data-related abilities:
- To read (consume information that includes data)
- To write (collect, manage, and convert)
- To comprehend (analyze, visualize, interpret)
One survey found that only 8% of employed individuals in the US have high literacy (to demonstrate and coach in all three areas) and over 50% have no data literacy skills at all. (datatothepeople.org).
Similarly, the Data Literacy Project found that only one-third of employees are confident understanding and using data.
Further, they report that 76% of business decision makers aren't confident in their ability to read, work with, analyze and argue with data.
As a result, their goal is to make data literacy training available globally, because they believe everyone can be “data brilliant.”
Their online coursework features a wide array of topics from basic fluency and terminology to advanced analytic topics.
Following this trend, companies have begun training employees to understand and incorporate data more consistently into their work.
Corporations such as Accenture, Experian, Quik, Cognizant, and others have adopted data literacy goals and introduced training, communities, and certifications to achieve them.
More on that in a minute.
Before we discuss the issue of data literacy, I’d like to call your attention to a parallel HR trend in corporate America: universal emotional literacy.
Experts estimate that only 36% of adults exhibit emotional intelligence.
In some instances, these organizations will require all employees to identify and exhibit seven different emotions on cue.
Training begins with members of the C-suite; each member of the executive team, including the CFO, must be able to recognize and label four different types of discomfort simply by looking at the eyes of their subordinates.
Training will focus especially on professionals in IT, actuarial and data science departments.
The goals for universal emotional literacy are to “facilitate widespread caring among all employees and encourage adoption of higher levels of emotional intelligence,” especially among those with the low levels of innate compassion.
Failure to pass required emotional recognition tests will result in suspension (until their kindness and empathy develop) or termination.
According to organizational psychologist, Dr. Ima Meany, people who claim that aren’t in tune with the emotions of others “just aren’t trying hard enough. Everyone must conform to empathetic standards whether it feels good or not.”
If this new trend comes as a surprise to you, it should. Because I made it up. There is no required universal emotional literacy project (that I know of, yet).
There are corporate efforts to call attention to the value of empathy, but no universal requirement to demonstrate it.
I described this imaginary, mandatory emotional literacy goal so readers can consider the premise of asking everyone to master any one skill.
Based on my experience in the workplace, it’s unlikely that everyone would easily master empathy skills.
While a goal of improving empathy seems reasonable, what level of it—or any skill—should be universally required?
How fluent must we all be?
Let me state that I am 100% supportive of efforts to improve data literacy. I believe everyone can become better users and consumers of data. I welcome new, inviting ways to help people gain new data-related skills.
But, like our imaginary universal empathy requirement, I wonder if universal literacy is a realistic goal.
While, yes, anyone can improve their data abilities, I am also keenly aware of the looks I get when I admit I took eighteen semesters of statistics, measurement, and analytics—and enjoyed them!
Individuals who hated high school algebra may not be first in line for data literacy class.
Certainly, in some cases it makes sense to require fluency in a specific skill.
- A piano teacher must have musical abilities.
- Data analysts need an understanding of statistical methods.
- Financial advisors should understand markets and risk.
But how do we decide who needs to be fluent? In which skills?
We all value skills that have served us well—and genuinely want others to experience the same benefits.
Not surprisingly, those who call for universal data literacy tend to be those who have already mastered data skills (just like those who call for empathy and emotional awareness are those who demonstrate them and recognize their value).
There are many, many learnable skills that would make workplaces better and more productive.
I dare say both universal emotional literacy and universal data literacy could significantly improve business performance and employee satisfaction.
But people also vary in their natural aptitude for either.
Just as there are people who struggle with recognizing emotions, I also know many smart, capable professionals for whom data interpretation does not come easily.
They aren’t rejecting the importance of data; they just don’t instantly grasp what numbers and graphs mean.
Translators can help.
Learning a foreign language takes time.
Becoming fluent usually requires more than coursework; it evolves from immersion in settings with native speakers.
Data literacy is no different.
We can all become better at the language of data.
But like being in a foreign country, when we need critical information, we look for a translator who speaks both languages.
As analytics become ever more complex and data capabilities ever more essential, it may not be realistic to convert 50% of all employees from novice to expert, from unfamiliar to fluent.
But we can equip a subset of employees to be translators: able to interpret information between analytic-types and business professionals.
These go-betweens would specialize in both communication and analytics, highlighting the necessary insights in both directions.
Analytic translators can create the bridge when evidence and understanding are too far apart to connect using basic literacy.
Plus, as designated interpreters, their task would be to facilitate shared comprehension without judging those with less skill.
Maybe I’m wrong. Perhaps the 65% with low emotional intelligence will embrace empathy training. Perhaps the 66% with low data literacy will be overjoyed to have analytic training.
Or maybe we need more translators.