What Difference Does Data Literacy Make in the Workplace?

Put simply, data literacy in the workplace refers to employees’ abilities to extract meaningful data insights, then use their findings to make business decisions beneficial to the organization.

And you’d better believe it’s a hot-button issue in today’s data analytics landscape, as data literacy—or lack thereof—plays a major role in any company’s ability to use data-driven decision-making to generate value.

Wondering what difference data literacy makes in the workplace? Here are some key points to consider.

Why Data Literacy Is So Crucial

According to the Gartner Annual Chief Data Officer Survey, data literacy ranks as the second-largest internal roadblock to the success of the CDO. And organizations are starting to do something about it.

Gartner also predicts by 2020, 80 percent of organizations will “initiate deliberate competency development in the field of data literacy to overcome extreme deficiencies.” Why? Because by then, about half of all businesses will likely lack the data literacy skills they need to achieve value.

In today’s competitive business landscape, companies simply can’t afford to leave anything on the table. Without ensuring their workforces reach at least adequate levels of data literacy, organizations will miss out on much of the value lurking under the surface of their stored data.

As the Field Chief Technology Officer for ThoughtSpot notes, data literacy as a whole breaks down into several components:

  • Tools and technology
  • The ability to think critically about data to understand usefulness
  • A culture in which data is valued by everyone “as a primary vehicle for decision making”

Data-literate companies have the right tools, have provided the right training to help employees think critically, and are actively fostering a data-literate corporate culture.

Avoiding Pitfalls of Data Interpretation

Even if employees have access to data analytics tools, a lot can get lost in translation. Context is key. Only data literacy helps employees have the framework they need to avoid common pitfalls of interpreting data, like “drawing conclusions from too little data” or “the tendency to focus on results that support our existing beliefs.”

In other words, data literacy helps employees vet the quality of information they’re receiving, and trains them to think critically about the results in front of them. The result is fewer instances of employees acting on flawed or irrelevant data, making decisions that’ll ultimately cost the company money. Effective training in data literacy essentially helps employees avoid barking up the wrong tree, as the saying goes.

An understanding of how to use business intelligence software and data analytics tools is a start; but data literacy gives data insights meaningful context, helping employees decide which results are worth pursuing as part of decision-making vs. which results are immaterial.

Beyond Access: Actually Democratizing Data

There’s no doubt today’s data analytics tools increase access for non-specialized employees. No longer do IT specialists and data analysts have to act as the intermediaries between company data and, well, everyone else. This is a promising development, as it frees up specialists to work on more high-level projects while business users can answer any questions they have in real time, independently.

But, as one Forbes contributor notes, the ability to make data more available and accessible isn’t enough on its own. Put it this way: Without the ability to read, a book is just paper with ink markings. Data literacy ensures employees derive genuine value from self-service analytics tools because they have the ability to understand the data visualizations they’re seeing and communicate their findings with others.

Here are a few skills bolstered by literacy training, per the aforementioned Forbes contributor:

  • Knowledge: Understanding metrics, mathematics, correlation and quantitative vs. qualitative data.
  • Assimilation: Interpreting features of interactive charts like titles, filters, goals, dimensions, etc.
  • Interpretation: Observing trends, patterns, skews, noise, omissions, clusters, etc.
  • Skepticism & curiosity: Understanding how results came to be, and gauging insights’ collection methods, truthfulness and significance.

Ultimately, data literacy is the determining factor on how much value any org can extract from data. It’s important to get this right.