A recent Qlik study found eighty-five percent of C-suite executives believe “being data literate will be as vital in the future as the ability to use a computer is today.” A Tableau study found eighty-two percent of decision-makers expect “basic data literacy from employees in every department—including product, IT, HR, and operations.” Furthermore, they anticipate that by 2025, almost 70 percent of employees will be expected to use data heavily in their roles, up from 40 percent in 2018.
With these increasing expectations, however, many employees feel ill-equipped to work with data on a regular basis. A 2020 Qlik-Accenture study found that “just 21 percent of the global workforce are fully confident in their data literacy skills.” As a result, more and more companies are making investments in data literacy training and rolling out programs to help upskill their employees in this key area. Gartner estimates, that by 2023 (yes, next year), it will be a key focus in more than 80% of data strategy and change management programs.
When we essentially define data literacy as the ability to read, work with, and communicate with data, it’s not surprising that data storytelling often gets lumped in with these abilities. If you examine most data literacy workshop offerings, you’ll find data storytelling prominently featured within the communication modules. While data storytelling is definitely an important data literacy skill, I’m not sure it’s a requirement for being data literate. Let me explain why.
Data literacy encompasses a wide array of data skills, and it also spans different levels of ability. As you gain more knowledge and skills, you become more data literate. I view data literacy as a journey. Becoming data literate is a key milestone in the early stages of the journey and establishes a solid base for additional learning. However, it is not the destination. It represents an inflection point where someone is sufficiently capable of using data in their role. Being data literate, individuals can participate in and contribute to the data conversations occurring within their organizations.
The primary goal of a data literacy program isn’t to turn all your employees into data scientists—or even fully qualified data analysts. You want to cultivate data citizens, not citizen data scientists. The major goal is to focus on the 90-95% of people within your organization who need foundational data skills but are not expected to use data for all their work.
To be data literate, most business users will just need to be competent and comfortable in handling the data they encounter in their day-to-day responsibilities. They need a functional baseline of data literacy—not advanced statistical knowledge or the ability to program in Python or R. With a minimum level of proficiency with data, they can begin to help your organization tap into the full potential that your data has to offer.
The first step is to establish what fundamental data skills are needed for your team to be considered data literate. Rather than outlining all the possible data skills someone could acquire in the area of data literacy, we should focus on what’s the “Minimum Viable Proficiency” (MVP) to be data literate. To help identify this set of criteria, I’d like to break out the skills by the data hierarchy tiers:
Across these three levels, I have added the data literacy definition’s three areas of focus—Read, Work With, and Communicate—to create the following 3x3 matrix.
One of the most essential aspects of data literacy is to be able to “read” data. At the fundamental Data level, people should be comfortable with basic numeracy or math skills. In addition, they should also be familiar with domain-specific metrics for their industry and function. For example, a retail worker should know what metrics such as conversion rate and inventory turnover are. A digital marketer should recognize metrics such as ROAS (Return on Ad Spend) and click-through rate.
At the Information level, employees should be able to follow and understand simple charts, diagrams, and maps (graphicacy). They should be comfortable with basic statistics (e.g., mean, median, mode, correlation isn’t causation, etc.) that help summarize the information. They also should be curious about the numbers and form questions about what they're observing in the summary statistics and graphs.
Finally, at the Insight level, people should be able to interpret what they see and find meaning in the numbers. They should also demonstrate a healthy skepticism of the numbers and think critically about the data. For example, it’s important to be mindful of how figures can be inadvertently or intentionally distorted.
At the base Data level, employees should feel comfortable interacting with the data in an analytics tool or a spreadsheet. In these tools, they should know how to perform basic operations such as filtering, formatting, or organizing the data.
At the Information level, individuals will need to be capable of performing both descriptive and diagnostic analyses to find actionable insights. The ability to create exploratory data visualizations will be helpful in making observations on the data.
At the Insight level, they will need to be comfortable with trusting insights to guide their decision-making and actions.
At the primary Data level, people should be able to respond to ad-hoc requests for data and be able to share relevant facts and figures in emails, texts, or other written forms of communication. They shouldn’t feel intimidated discussing data in their conversations with co-workers, managers, and other individuals outside of the organization such as clients or partners.
Stepping up to the Information level, employees will need the ability to share information via data presentations, reports, or dashboards. They will need to be able to create explanatory data visualizations to communicate their information effectively across various delivery methods.
Finally, at the Insight level, when someone wants to share a meaningful insight with a particular audience, data storytelling becomes an invaluable means for explaining the key findings, using both narrative and visuals. With a data story, you position your insights to be acted on and drive change for an interested audience.
Within this 3x3 matrix, all the listed data skills are important (as well as many others that aren’t featured in the table); however, not all of them are required to be data literate. In the following version of the same matrix, I’ve highlighted in blue the areas that represent the “minimum viable” data skills that are needed to be data literate. Ironically, the blue “L” happens to correspond with literate—as in data literate.
The ability to read data comes before everything else, and all three Read levels—Data, Information, and Insight—are necessary. However, I don’t believe all three levels are required for the Work With and Communicate areas to be data literate.
In the Work With column, while everyone can benefit from being able to analyze data (beyond just interpreting it), a lack of substantial analysis skills doesn’t make someone not literate with data. For example, a manager may not have the time to perform analysis herself, but she may be adept at interpreting the findings of analysts whom she partners with. Furthermore, not everyone will have the privilege or opportunity to make decisions based on data, but that doesn’t disqualify them from being data literate.
In the Communicate column, the capability to build a detailed data report or automated dashboard is a more specialized data skill. An employee’s inability to produce comprehensive or automated reporting also shouldn’t determine whether they are data literate or not.
When it comes to data storytelling, it too represents a more advanced form of persuasive data communication. If someone needed the ability to tell stories with data to be data literate, many analytics and data science professionals would fail to be considered data literate, which clearly doesn’t make any sense.
Back in 2016, I published a Forbes article that positioned data storytelling as “the essential data skill that everyone must master.” However, just like a university course, you’ll need to complete some pre-requisites before you’re ready to master telling powerful data stories. Before you can become an effective data storyteller, you must be data literate. It would be irresponsible and highly problematic to build and tell data stories without fully understanding the data, the context, and the meaning behind what you’re communicating.
While data storytelling may fall under the umbrella of data literacy skills, it sits within the top-right insight literacy quadrant as a specialized data communication skill. Most data professionals are already tasked with the responsibility of sharing information and insights. However, as data now pervades most roles and functions from sales to marketing to human resources, the training need for data storytelling skills is expanding beyond just the traditional data-intensive teams (analytics, data science, finance). As your data literate employees begin finding more insights as they explore the data, increasingly they're going to need this critical data skill to translate insights into action.
In summary, we all must be data literate to tell data stories effectively, but you don’t need data storytelling skills to be literate with data. If your organization has already established a data literacy program, you may be ready to explore how you can further enhance your employees’ data skills with customized data storytelling workshops. I’d love to partner with you and take your team to the next level!
Effective Data Storytelling teaches you how to communicate insights that influence decisions, inspire action, and drive change.