What is data literacy?

In today’s AI-powered, data-driven culture, fundamental data literacy skills are crucial for employees at every level. Organizations create and collect more data than ever before: According to IDC, global data creation is expected to reach 181 zettabytes in 2025. It’s no longer feasible or strategic for only data scientists or machine learning engineers to leverage this information for data-driven decision-making.

However, becoming data literate does not require becoming a data scientist. Rather, it means individuals have the confidence and technical skills to use data effectively in their roles to uncover insights and make smarter decisions. Increasingly, it also means they know how to query AI tools and interpret AI-generated insights.

Advances in technology are helping to democratize data access across organizations—a supporting element of data-literate cultures. Business intelligence (BI) dashboards, natural language queries and user-friendly interfaces are powerful tools for data understanding. But even these tools require basic data literacy to navigate, interpret and effectively use.

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The data landscape is saturated with powerful tools servicing every stage of the data lifecycle—from acquisition to analysis to visualization. At the same time, organizations are also collecting and generating unprecedented volumes of data. Together, these trends create an environment rich with potential insights.

However, without the skills to use these tools and interpret data effectively, organizations might be disappointed with the business impact of their data initiatives (or lack thereof).

In a 2025 study, 40% of US and UK leaders cited decreased productivity and 39% highlighted inaccurate decision-making as the primary risks of inadequate data literacy skills.¹ Despite these risks, only 27% of organizations report having a high level of data literacy.²

The need for strong data skills is even greater in the age of AI. Organizations will increasingly seek data-literate employees who understand how AI tools use data to make decisions—and how to separate useful insights from flawed, potentially harmful, recommendations. (This is why data literacy is also considered a core competency of AI literacy.)

In a 2025 report, 41% of executives identified data literacy as the fastest-growing skillset over the past five years.³ But what exactly does this skillset contain?

 According to MIT researchers, data literacy consists of four core abilities:⁴

• Read data
• Work with data
• Analyze data
• Argue with data

Like traditional literacy, data literacy benefits far more than just the individual. Organizations with data-literate cultures may experience the following benefits:

• Reduced silos
• Improved data privacy and security
• Lower costs
• Advanced decision-making

Data-literate organizations foster clear flows of communication and knowledge sharing. Teams understand the needs of the broader business and how their work contributes to organizational goals. By breaking down data silos, diverse teams can use data synchronously and align efforts with the organization’s broader mission.

Employees with strong data literacy skills are more aware of the privacy regulations and risks associated with improper data handling. They can also better identify threats to data security, such as malware, phishing and insider threats—strengthening the organization’s overall security posture.

When data is no longer trapped at the functional level, stakeholders can uncover meaningful insights faster. Additionally, when employees have the skills to do basic data work themselves, technical teams can utilize their time and skillsets more effectively. This efficiency translates into cost savings and increased productivity.

Data-literate employees are more skilled at interpreting data in context, enabling business units to make impactful data-informed decisions. This level of curiosity and use of data across an organization can foster greater creativity and innovation.

When implemented effectively, a data-literate culture should exemplify the following principles:

1. Democratized data access across the enterprise
2. Organized, transparent and explainable data
3. Empowered data citizens trained to responsibly use and analyze data
4. Leadership that champions and drives data literacy

In a data-literate organization, data is accessible to those who need it, when they need it. Achieving this requires an architecture that enables quick, secure and simple access to governed data across a complex and siloed data ecosystem.

For example, a data fabric unifies data across an organization’s on-premises and multicloud environment using intelligent and automated systems. This capability addresses challenges such as data silos and growing data volumes, while also enabling easy, self-service data access.

Data intelligence further supports data accessibility for data literacy. It automates and streamlines core data activities such as metadata management, data discovery, data governance, quality assurance and data analysis.

Once organizations establish governed data access, it’s important to help decision makers understand how data moves throughout the system. For instance, governance tools use metadata to provide transparency by showing context and lineage. These tools also help standardize data definitions and terminology across teams.

When data is organized, transparent and explainable, people can more easily understand its value and how they can use it in their roles. To support this transparency and understanding, users should have the access, information and tools needed to answer key questions such as:

• What is the data source, and is it trustworthy?
• What metadata, rules and compliance policies apply?
• How does this data drive business value and outcomes?

According to a survey from the IBM Institute for Business Value (IBV), 85% of leading chief data officers (CDOs) are expanding training, 77% are reskilling staff and 70% are hiring new talent to increase data literacy across their organizations.

A successful data literacy program empowers employees to translate data into compelling, visual stories that lead to actionable insights. Data literacy courses and trainings should upskill employees with practical skills, including how to use data visualization tools and storytelling techniques aligned with practical use cases and business objectives. 

Just as importantly, users should also learn how to be responsible data stewards. A data-literate workforce can confidently access, store and manage data according to appropriate business policies and relevant regulations.

A 2025 report found that, when implementing data literacy training programs, 24% of leaders cite a lack of executive support, which makes it difficult to drive company-wide adoption.⁵

A data culture starts at the top. Providing employees with guidance, materials, education and tools is likely not enough—they also need senior leadership support. Data literacy should be an integral part of the culture and fabric of the organization at all levels.

In practice, leaders should model desired data literacy skills because their example sets the tone for the rest of the organization. It’s also key to provide the opportunity for feedback on data culture and practices. Encouraging open conversations that include diverse perspectives will generate better outcomes.