Data literacy (DL) describes how well an individual or organization understands, works with, analyzes, visualizes, and applies data to reach their goals. The specific context and use case determine what applying data literacy looks like in practice.
For example, while reading visualizations on product deliveries provides value, true data literacy involves going further. It involves actively seeking out and interpreting customer feedback data to address negative customer experiences. It demonstrates the ability to acquire, make sense of, and apply various aptitudes.
As individuals improve their DL skills, they experience benefits such as better data-driven decision-making on the job, career advancement, and the joy of pursuing interesting questions. Results like these take on a personal meaning, growing personal and professional opportunities.
In contrast, organizations cultivate data literacy to foster a data-driven culture. In this environment, employees across roles and teams leverage data capabilities to manage risk and gain insights for a competitive advantage.
Data literacy programs serve individual and organizational DL needs. They start by assessing DL competencies, then state learning objectives, train people, and measure achievement. Although DL program details can vary, they actively improve the understanding and application of data to drive better outcomes.
Data Literacy Defined
While the details may differ, most definitions of DL emphasize improved data understanding and application with proficiencies. For example, Gartner notes that data literacy includes understanding and applying data sources, constructs, methods, and techniques. The United Nations Educational, Scientific and Cultural Organization (UNESCO) includes identifying, interpreting, creating, and computing with and communicating with data.
In contrast, other explanations characterize data literacy more by its outcomes. Eastern Michigan University includes “results of drawing correct conclusions and recognizing when data is” misleading or inappropriate. A DATAVERSITY® article describes DL as the result of effectively working with data.
Some explanations try to keep DL simple and to the point. For example, TechRepublic states that DL is “the ability to read, understand, analyze and communicate information.”
The scope of data literacy can span a wide or narrow range of capabilities and levels of knowledge, depending on the situation. Below, we will expand on each of these components.
Data Literacy Components
Data literacy covers key attributes including understanding, working, analyzing, visualizing, and applying data to a task. We will expand on each of these components below.
Understanding Data: Understanding data involves researching different data sets to explore possibilities and evaluating their quality as possible sources. Knowing generally about where data comes from and the path data takes to visualization represents a key aspect in understanding data. For example, the Kelly Blue Book has more accurate car values than some used car dealerships.
Working with Data: Working with data involves creating, acquiring, cleaning, and managing it. This skill involves more technical knowledge including how data is structured and stored, how to clean the data so it is properly formatted, and data generation processes and end-points. Knowing how to run a grammar check tool exemplifies working with data.
Analyzing Data: Analyzing data covers computational thinking, which is the process of breaking down complex problems into smaller manageable parts, applying logical reasoning, and leveraging tools as needed. This skill set also includes representing data based on analysis type and recognizing and selecting data appropriate to the business goal. For example, when someone writes a plan to test software quality from requirements, they analyze data.
Visualizing Data: Visualizing data describes grasping insights from large datasets presented as visual aids, like graphs and charts. This skill requires examining data visualizations, recognizing patterns and relationships among the visualized data, and understanding how those insights apply meaningfully to the circumstance.
Applying Data to a Task: Applying data requires using data to communicate a narrative story backed by numerical evidence. Personalizing market campaigns based on data about the profitable customer segments from purchasing behaviors is an example of this. Those skilled in applying data leverage it to persuasively support hypotheses and arguments.
While data literacy requires some business knowledge, it expands above and beyond business literacy. Both see problems in slightly different frameworks, as described in the next section.
How Does Data Literacy Differ from Business Literacy?
Business literacy (BL) refers to the skills needed to understand how a business operates and to achieve its financial objectives. This kind of knowledge includes reading and writing about business topics in a clear, action-oriented manner that focuses on simple, understandable results.
While data literacy does cover some of the same capabilities as business literacy when applied to data and metrics, there are key differences. For example, one could understand marketing data without necessarily knowing how to write a full marketing plan for a business.
Furthermore, DL may involve more advanced analytical and math skills, like statistics. A data-literate professional questions results by examining limitations, uncertainties, and biases in the underlying datasets.
This aspect of DL wants to use advanced techniques and help others see the complex possibilities and interesting results. However, people with high technical literacy, which is the understanding of computation, may not necessarily cover all the angles of data literacy.
How Does Data Literacy Differ from Technical Literacy?
Technical literacy refers to the ability to effectively use computer-based tools and applications to solve problems and complete tasks. Both technical literacy and data literacy involve understanding how to work with data in digital environments.
However, data literacy goes beyond just basic computing skills. Depending on the role, it may also involve advanced technical abilities like statistics, math, and machine learning – e.g. the data scientist.
Additionally, DL covers important nontechnical skills like communication, collaboration, and project management. A data-literate person needs to present why the data has the significance it does.
Crucially, data literacy skills extend beyond the digital realm as well. A data-literate professional can work with, analyze, and apply data through manual, non-technical means as needed.
For example, a person may stop in an office supply store on the way to work and pick up a few items they remember were needed. To complete that task, they need to understand how to go to a storeroom to inventory office supplies, manually keep track of that information, know when to restock those items, identify them in the store, and purchase them.
So, DL has an overlapping but different scope than digital functioning. While technical skills are involved, Data literacy fundamentally centers around understanding data itself – where it comes from, how to analyze it, what insights it provides, and how to apply it effectively across contexts.
How Does Data Literacy Differ from Data Governance?
In contrast to data literacy’s focus on data understanding and application, data governance serves a different core purpose. DG describes a business program and bedrock that supports harmonized data activities across the organization. For example, governance services may decide what customer data to keep and for how long.
Data governance serves a different purpose than just helping workers make better choices with data. DG ensures reliable, valid, and accurate data across the company to leverage for profit and to mitigate risks. Moreover, DG addresses data accessibility and security needs to comply with regulations.
However, some governance activities include improving data literacy. For example, a DG program may implement and maintain a business glossary, a resource sharing what internal terminology means. This reference plays an essential role in data literacy, helping to understand, work with data, and interpret visualizations.
Since DG deliverables meet the needs of multiple teams and the organization, it does not have the resources to focus on the data literacy goals of a particular team or individual. So, a data literacy program picks up the slack through training and workshops, such as how to read a pie chart. These dedicated DL initiatives and training help assess and improve workers’ capabilities to meet business goals.
Data Literacy Use Cases
When organizations meet business goals because of increased data literacy, they can see remarkable improvements. These benefits span multiple industries as demonstrated by the use cases below:
A multinational bank implemented a comprehensive DL program. It trained employees to interpret and analyze financial data. Consequently, the bank saw significant improvements in its decision-making processes. Workers became more adept at identifying patterns, trends, and correlations within complex datasets, enabling them to make informed decisions based on reliable insights.
A leading global retailer used data monetization techniques to identify the most profitable customer segments based on purchasing behaviors. This goal was made possible by having a high level of Data Literacy in its workforce.
Beck’s Hybrids worked closely to upskill its data literacy within its business. Its approach included:
Going out to field sales meetings and talking to the salespeople about why data literacy is important and training them how to use the data.
Meeting with the dealers and helping them succeed with DL to extend Beck’s Hybrids core values and beliefs.
Consequently, Beck’s Hybrids saw a marked improvement in the types of reports partners and workers requested and in their self-service capabilities.
PwC responded to Australia’s not-for-profit sector by providing needed Data Literacy skills to drive action. It ensured quality information and availability to these clients and established foundations to strengthen organizational DL capabilities.
The United Nations World Food Program (WFP) delivers food assistance in emergencies. It established a formal data literacy program focused on collaboration, peer leadership, and talent. As the WFP used its data better, it saved $138 million by 2020 while supporting over 120 locations.
Airbnb launched an in-house Data Literacy initiative called “Data University.” The program boosts skills in understanding, interpreting, and using data effectively in all roles. Now, 45% of Airbnb is a WAU (weekly active user) of their internal data platform.
The city of Syracuse, New York had no data to prioritize roads needing repaving. A plan to reconstruct almost 23 miles of roads in every city quadrant and seal and resurface another 40 resulted from a gradual increase in Data Literacy.
