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.

Building a Data-Literate Culture

Data literacy is a foundational skill, much like reading or basic computer knowledge in today’s data-driven world. Whether you’re in marketing, finance, operations, or leadership, being data literate means you can navigate information with confidence and turn numbers into actionable insights.

While individuals benefit from data literacy skills, it generally refers to a company-wide effort. It’s about building a culture where everyone—not just analysts or data scientists—can engage with data in meaningful ways. That means making tools accessible, training teams to think critically about data sources, and empowering employees to use data in their everyday decision-making. A data-literate workforce leads to smarter strategies, faster innovation, and better outcomes across the board.

In this article, we’ll explore what data literacy is, why it matters, and how companies can build a culture that supports it at every level.

Why is data literacy important?

Data literacy is essential in today’s business environment. It’s at the core of nearly every decision organizations make. From measuring performance to identifying market trends, teams rely on data to guide strategy, optimize operations, and stay competitive. Without a strong understanding of how to interpret and apply data, valuable insights can be overlooked, misinterpreted, or misused, leading to costly mistakes and missed opportunities.

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 culture that supports data literacy takes more than just offering training—it requires intentional leadership, tools, processes, and mindset. When data becomes part of everyday conversations and decisions, organizations can move faster, think more critically, and make smarter choices.

To foster a data-literate culture in your company:

• Lead by example from the top down. Executives and team leaders should regularly use data in decision-making, highlight key metrics in meetings, and ask data-informed questions.
• Make data accessible and easy to find. Ensure employees can access the data they need through intuitive dashboards, shared sources of truth, and self-service BI tools.
• Offer ongoing data literacy training. Provide workshops, courses, or certifications tailored to different skill levels and roles—not just technical teams.
• Include data literacy in onboarding. Introduce new hires to your company’s data tools, sources, and expectations for data usage from day one.
• Celebrate data-driven wins. Recognize teams or individuals who use data effectively to drive results, solve problems, or uncover new opportunities.
• Encourage curiosity and critical thinking. Create a safe space to ask questions about data sources, assumptions, and outcomes—even if the answers are complex or uncertain.
• Create cross-functional data champions. Identify and empower individuals across departments to serve as go-to resources and advocates for using data well.
• Simplify the tools and language. Minimize jargon and make visualizations easy to interpret so non-technical users can engage with confidence.
• Host data office hours or “ask an analyst” sessions. Offer informal opportunities for employees to get help understanding reports, building queries, or exploring data sets.
• Embed data into everyday workflows. Integrate data insights into tools employees already use—like CRMs, project management platforms, or communication channels.
• Align data use with business goals. Make sure everyone understands how data connects to real outcomes—such as customer satisfaction, revenue growth, or operational efficiency.

Measure and monitor data engagement. Track usage of data platforms and how often teams use data to support decisions. Use that insight to improve.

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.