What is Data Literacy? Skills, Challenges, and How to Get Started

Much like learning to read, data literacy has a learning curve. Diving into data can be difficult, but once you understand it, you open up a world of understanding and communication. Data literacy is an essential skill for everyone, no matter your role.

The ability to understand and work with data is no longer just a skill for analysts—it’s a must-have across every role and industry. As organizations collect more information than ever, the real value lies in converting data into insight and action.

It involved reading, interpreting, analyzing, and communicating data effectively, empowering individuals to make smarter decisions based on facts rather than assumptions.

Despite its importance, many organizations struggle to close the gap between having data and actually using it. It’s not just about knowing how to run reports or build charts—it’s about critically evaluating data, asking the right questions, and using insights to make informed decisions.

What Does Data Literacy Really Involve?

At its core, data literacy is about more than just working with spreadsheets or dashboards—it’s about being able to confidently understand, analyze, and communicate with data. Whether you’re interpreting a chart or telling a story with numbers, these foundational skills make up true data literacy:

• Understanding data: Know where data comes from, how it’s collected, what types exist (structured, unstructured, qualitative, quantitative), and what limitations or biases might exist.
  
• Analyzing data: Look for patterns, trends, and relationships in the data. Draw conclusions that are grounded in evidence—not just instinct.
  
• Communicating data: Share insights clearly through reports, dashboards, or visualizations that others can understand and act on.
  
• Thinking critically: Always ask questions. Where did the data come from? What’s missing? What’s influencing the outcome? A healthy dose of skepticism leads to smarter decisions.
  
• Visualizing data: Use charts and graphs the right way—choosing formats that tell the story without distorting the truth.
  

Data literacy blends technical fluency with critical thinking. And the more these skills show up in day-to-day work, the more valuable your data becomes.

Why is data literacy important?

Data literacy is essential in today’s business environment, but its value goes even further. It’s at the core of nearly every decision we make—whether that’s optimizing a campaign at work, evaluating healthcare statistics, or understanding the latest news report. In a world powered by data, being able to read and reason with numbers is no longer a niche skill—it’s a universal one.

For individuals, data literacy empowers better, faster decision-making. When employees across departments—not just analysts—can understand dashboards, ask data-driven questions, and evaluate metrics with confidence, they become more independent, informed, and effective in their roles. Since everyone develops a common language around data and outcomes, technical and non-technical teams can also collaborate better.

At an organizational level, strong data literacy contributes to a culture of accountability, innovation, and agility. Companies with data-literate workforces are more likely to recognize patterns, react to change, and adapt strategies based on evidence rather than intuition. In a fast-moving, data-saturated world, that kind of agility isn’t just a competitive advantage—it’s a necessity.

Examples and use cases of data literacy

What does data literacy look like in a healthy organization? How do teams apply data literacy skills? Here are some examples and use cases of data literacy in action.

1. Marketing teams optimizing campaigns

A data-literate marketing team reviews campaign performance dashboards to compare conversion rates across channels. Instead of relying solely on gut instinct, they use data to adjust budgets, A/B test content, and refine audience targeting—all without needing an analyst to interpret the metrics for them.

2. Sales reps using data to prioritize leads

Sales teams use CRM data to identify which leads are most likely to convert based on historical patterns. A data-literate sales rep understands how to filter and interpret lead scoring models, helping them prioritize outreach and close deals more efficiently.

3. Operations teams reducing bottlenecks

An operations manager identifies a delay in product shipments by examining supply chain metrics in a dashboard. Because they’re data literate, they can drill into the data, isolate the issue, and work with vendors or partners to resolve the problem—all before it affects customers.

4. HR teams tracking workforce trends

HR professionals monitor retention, engagement, and hiring data to inform talent strategies. A data-literate HR team can recognize patterns in attrition by department or demographic and use that information to improve onboarding, training, or internal mobility programs.

5. Executives making data-informed strategic decisions

Leaders with strong data literacy can confidently interpret financial, operational, and customer metrics in real time. Instead of relying on static reports or summaries, they engage with live dashboards, ask smarter questions, and base high-level decisions on evidence, not assumptions.

Specific data literacy skills

Besides just a concept, data literacy is also a concrete set of skills. If you want to build data literacy in your organization, you have to know what data literacy looks like in practical application. Here are some specific data literacy skills, organized by difficulty level, that your team members should know.

Beginner data literacy skills

These foundational skills help individuals start working with data confidently and accurately.

• Reading and interpreting charts and graphs: Learn how to extract meaning from visualizations like bar charts, line graphs, pie charts, and dashboards.
• Understanding basic statistical concepts: Familiarize oneself with terms like mean, median, standard deviation, correlation, and statistical significance to interpret data appropriately.
• Recognizing data quality issues: Identify problems such as missing values, duplicates, inconsistencies, or outliers that may skew results.
• Understanding data sources and context: Know where data comes from, how it was collected, and what limitations, assumptions, or biases might affect its use.

Intermediate data literacy skills

These skills allow users to interact with data, explore trends, and begin drawing conclusions independently.

• Filtering, sorting, and segmenting data: Use spreadsheets, BI tools, or queries to isolate relevant information, compare data segments, and drill into insights.
• Interpreting metrics and KPIs: Understand what key performance indicators (KPIs) measure, how they are calculated, and what they reveal about business performance.
• Asking data-driven questions: Formulate clear, focused questions that guide analysis and align with business goals or challenges.
• Using self-service BI tools: Navigate platforms like Tableau, Power BI, or Domo to create dashboards, explore datasets, and generate visual reports without relying on analysts.

Advanced data literacy skills

These higher-level skills involve applying insights, communicating findings effectively, and navigating ethical responsibilities.

• Communicating data insights clearly: Translate complex findings into plain language using visual storytelling, executive summaries, or slide decks tailored to non-technical audiences.
• Maintaining data privacy and ethical standards: Understand how to responsibly use data, including respecting privacy laws (like GDPR or HIPAA), securing sensitive information, and avoiding biased or unethical interpretation.

Challenges to data literacy

Many organizations recognize the importance of data literacy but struggle to implement it effectively across their workforce. One of the most common challenges is the skills gap between data professionals and non-technical employees. While analysts and data scientists may be fluent in data tools and concepts, many business users lack the confidence or training to interpret data or ask informed questions. This creates a dependency on data teams for even simple requests, slowing down decision-making and limiting agility.

Another major hurdle is the inconsistent access to data or tools. In some organizations, data is siloed within departments or locked behind complex systems, making it difficult for employees to explore or use it meaningfully. Even when self-service tools are available, they’re often underutilized due to a lack of training or unclear expectations. Additionally, there’s often a cultural barrier—some teams are hesitant to adopt a data-driven mindset or may distrust the data due to past inconsistencies or lack of context.

Organizations also face the challenge of balancing access with governance. Enabling more people to interact with data increases the risk of misuse, misinterpretation, or compliance issues if proper guardrails aren’t in place. Finally, embedding data literacy into the day-to-day workflow—not just offering occasional training—is a long-term effort that requires executive support, clear communication, and a shift in mindset. Without sustained focus, even the best intentions around data literacy can lose momentum.

How to gauge data literacy in your organization

Individual-level assessment

Gauging data literacy in your organization requires looking at individual capabilities and company-wide culture. At the individual employee level, data literacy can be assessed by evaluating specific skills such as the ability to interpret charts and graphs, identify trends, ask meaningful data-driven questions, and use self-service analytics tools like dashboards or BI platforms.

Surveys and assessments are a good starting point: Employees can self-report their comfort level with common data tasks, or complete scenario-based exercises that test their ability to draw insights from sample data sets.

Some organizations use formal assessments or certifications to benchmark individual skills, while others rely on manager feedback and observation during projects. Reviewing how often employees access and interact with data—whether they build reports, rely heavily on analysts, or frequently request data exports—also provides insight into their data fluency in practice, not just theory.

Organization-level assessment

At the company level, data literacy should be evaluated by looking at how data is embedded in decision-making processes, collaboration, and culture. One indicator is how widely self-service tools are adopted across departments—are only data teams using analytics platforms, or are business users regularly exploring data to answer questions and support decisions? Organizations can also track how frequently data is referenced in meetings, strategic planning, or performance reviews.

Another signal is how consistently data quality is discussed and prioritized—a data-literate organization recognizes that clean, accessible, well-governed data is essential for trustworthy insights.

Companies should examine how data literacy is supported through training, onboarding, and leadership. If data fluency is siloed to technical roles or treated as optional, it likely reflects a lower level of organizational maturity. Conversely, a company that encourages data curiosity, invests in education, and empowers all employees to engage with data is likely much further along in its data literacy journey.

By assessing the individual and organizational aspects, companies can pinpoint where gaps exist—whether it’s a need for more hands-on training, better access to tools, or a broader cultural shift toward evidence-based thinking. From there, leaders can build a targeted data literacy strategy that supports growth at every level.

How to Build a Company Culture That Fosters Data Literacy

Building a data-literate culture takes more than rolling out new tools or hosting a one-off training. It requires ongoing commitment, clear leadership, and an environment where everyone—from analysts to frontline staff—feels confident using data in their daily work.

A strong data culture starts at the top. When leaders use data to inform decisions, reference metrics in meetings, and ask data-driven questions, it sets the tone for the rest of the organization. But top-down support is just one part of the equation. Employees also need easy access to trustworthy data, training that meets them where they are, and encouragement to engage with data regularly.

Here are some practical ways to build a more data-literate culture:

• Lead by example. When executives and team leads use data in conversations and planning, it signals that decisions should be backed by evidence—not just instinct.
• Make data accessible. Use intuitive dashboards, shared datasets, and self-service BI tools so employees can explore data independently without jumping through hoops.
• Offer ongoing training. Provide tailored learning paths for different roles and skill levels, including hands-on workshops, certifications, or short tutorials.
• Encourage practice and exploration. Give employees space to interact with data in their day-to-day tasks. Even small actions—like adjusting a dashboard filter or analyzing a trend—build confidence over time.
• Promote mentorship. Pair less experienced team members with data-savvy peers who can offer guidance, answer questions, and build comfort with tools and terminology.
• Celebrate wins. Highlight real-world examples of data-driven decisions that led to positive outcomes, and recognize the teams behind them.
• Simplify the experience. Remove barriers by reducing jargon, building user-friendly visualizations, and embedding data insights directly into tools people already use.
• Embed data into onboarding. Introduce new hires to your data tools, sources, and expectations from day one.
• Create a safe space for curiosity. Encourage people to ask questions—even when they don’t have all the answers—and foster a culture that values evidence over opinion.

A data-literate culture isn’t built overnight, but consistent effort pays off. When people feel empowered to use data, they move faster, make smarter decisions, and bring more value to every conversation.

Build data literacy with Domo

Data’s not slowing down and neither should your team. If you want to turn passive data into active decisions, it starts with building real data literacy across your organization. The companies winning today aren’t just collecting numbers—they’re making them work.

Domo gives you the tools to make data easy to access, understand, and actually use. Ready to close the gap between knowing and doing? See how Domo can help you build a smarter, faster, data-driven business. Watch a demo of our data literacy-building tools today.