Workforce data literacy has been a priority ever since companies started using data analytics. With artificial intelligence, generative AI, and augmented analytics, the need persists, meaning end users should understand the basics so they can drive clear business value using data.
“In 2025, ‘data literacy’ should mean the ability to engage with data critically and confidently, emphasizing data quality and efficacy. Non-technical roles should not only know how to interpret data but also evaluate its reliability, relevance, and ethical implications in their workflows to achieve business goals,” says Dan Merzlyak, senior vice president, global head of data, analytics and AI at Postgres data and AI company EnterpriseDB. “These roles should also be comfortable using AI-powered tools and automated workflows, understanding how they streamline repetitive tasks, uncover insights, and enhance productivity.”
Casey Foss, chief commercial officer at global business and technology consulting firm West Monroe, has been emphasizing the importance of data literacy across her company for the past couple of years, because of its role in AI and the competitive advantage it can drive in any organization.
“If you haven’t invested in building these skills yet, you’re already behind,” says Foss. “For non-technical roles, the focus is on using data to make faster, smarter decisions. It’s about leveraging data to tell compelling stories to your team, clients, or investors. It’s about using data to stay competitive and measure your performance against market leaders and anticipate future market makers. “
What Everyone Should Know in 2025
At its core, data literacy requires understanding data quality including accuracy, completeness and timeliness to make informed decisions.
“[Data literacy] extends to evaluating AI-generated insights by understanding model fundamentals, limitations and ethical considerations. Effective data literacy also involves collaborative data practices, such as utilizing shared single source of truth dashboards to ensure teams work with consistent, up-to-date information,” says Merzlyak. “Finally, it demands ethical awareness in data handling, including recognizing biases, protecting privacy and weighing both business and societal impacts of data usage decisions. Together, these components enable professionals to effectively leverage data and AI while maintaining responsible practices.”
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Dan Merzlyak, EnterpriseDB
Alex Li, founder of StudyX at AI education company StudyX.AI says data literacy for non-technical people means being able to understand the meaning of data, make sensible decisions based on data, and collaborate effectively with technical teams to jointly promote business development.
“The foundation of data literacy lies in having a basic understanding of data. Non-technical people need to master the basic concepts, terms, and types of data, and understand how data is collected and processed,” says Li. “Meanwhile, data literacy should also include familiarity with data analysis tools. Although there is no need to become a professional data analyst, mastering some common data analysis tools can assist non-technical people in conducting simple analyses on data and identifying trends, patterns or anomalies in the data. In addition, the basic knowledge of data security and privacy is also an important part of data literacy.”
Why Some Think Data Literacy Alone Is Insufficient
At this point in time, Raviraj Hegde, SVP of growth at non-profit online fundraising platform Donorbox, believes data literacy shouldn’t just mean knowing how to read charts or understand different types of data.
“It’s more important to focus on AI literacy,” says Hegde. “To get the most out of data, people need to learn how to do something with it, like using AI tools to turn it into something useful. A lot of training focuses too much on tools and buzzwords instead of how to use data or AI to solve problems.”
Madeleine Wallace, founder and CEO of organizational and technological transformation consulting firm Windrose Vision and author of Thrive in the AI and Digital Age, also says non-technical employees need to grasp AI basics, including its strengths, limitations and ethical considerations, along with practical skills for tools like dashboards and AI platforms.
“From my perspective, data literacy is a mix of understanding AI and mastering tools to apply knowledge meaningfully in daily tasks,” says Wallace. “Embedding data tools into everyday workflows ensures employees engage with data naturally. Pairing this with hands-on, role-specific data projects brings learning to life.”
Kjell Carlsson, head of AI strategy at enterprise MLOps platform Domino Data Lab, believes in 2025, the most needed skill for leaders and employees is GenAI literacy.
“[GenAI literacy] is about mastering how to effectively use GenA) models to enhance their work, roles and productivity. [It] involves knowing how to find and understand information using GenAI tools; create content like text, images, and presentations; integrate GenAI outputs into daily workflows, and identify and correct hallucinations to ensure reliable results.”
However, GenAI literacy is not an evolution of data literacy, he says. Traditional data literacy — analyzing and making decisions with data — remains as vital as ever, especially since current GenAI tools are still nascent in handling structured enterprise data.
“Organizations should also avoid the misconception that fostering GenAI literacy alone will help developing GenAI solutions. For this, companies need even greater investments in expert AI talent — data scientists, machine learning engineers, data engineers, developers and AI engineers,” says Carlsson. “While GenAI literacy empowers individuals across the workforce, building transformative AI capabilities requires skilled teams to design, fine-tune and operationalize these solutions. Companies must address both.”
Joe Depa, global chief innovation officer at business management consulting firm EY believes data literacy is a company-wide priority that can make or break an organization.
“Data literacy isn’t fixed with workshops or an online training. It’s baked into the ‘innovation muscle’ of how your team operates day to day. Organizations should equip employees with AI dashboards that explain insights in simple, relatable language instead of overwhelming them with data overload,” says Depa. “It’s also about building a culture [in which] using data to make decisions is part of the company’s DNA. This includes creating an environment where it’s okay to test, make mistakes and learn together. Achieving this requires coordination between technology teams, security and business leaders to align workforce education with organizational goals.”
Why Data Literacy Training Isn’t Enough
The data literacy program itself influences a workforce’s ability to use data. According to Piyanka Jain, CEO at data analytics consulting firm Aryng, companies may train employees on statistics, machine learning and data visualization, but overlook professional skills like stakeholder collaboration and decision-making.
“In 2025, the focus [of data literacy] needs to shift. It’s not about turning everyone into a data scientist. It’s about enabling employees to deliver measurable business value using data — and that’s where the real challenges lie,” says Jain.
Organizations need to be mindful about and address cultural resistance, silos and outdated processes. They also need to recognize that AI and analytics tools are evolving so quickly that many employees can’t keep up.
“Data literacy in 2025 can’t just be about enabling employees to work with data. It needs to be about empowering them to drive real business value,” says Jain. “That’s how organizations will turn data into dollars and ensure their investments in technology and training actually pay off.”
Alejandro Manzocchi, Americas CTO at technology services company Endava also believes improving data literacy requires integrating it into the organization’s culture and day-to-day workflows.
“Organizations should embed data tools into daily work, encouraging employees to rely on data when making decisions. For instance, ask teams to present data-backed reasoning for their proposals,” says Manzocchi. “To facilitate this approach, you could pair non-technical employees with data mentors to foster informal, task-based learning. Task forces that mix data experts with other team members can also be effective. And use engaging methods like trivia challenges, data hackathons or dedicated platforms to gamify the learning process. Ultimately, organizations must foster a culture of curiosity where asking data-driven questions is not only accepted but encouraged.”
Rohit Choudhary, Acceldata
Rohit Choudhary, founder & CEO at enterprise data observability company Acceldata, says data literacy needs to be a core job skill woven into everyday decision-making.
“This means understanding basic data types and quality metrics, interpreting common visualizations correctly, and recognizing the context and lineage behind any dataset to assess its reliability. It also involves translating insights into action, from spotting trends to asking the right questions about anomalies or biases,” says Choudhary.
Non-technical teams also need to be aware of privacy laws, ethical considerations (like data bias) and the principles of fair use. In addition, data professionals and non-data professionals need a shared language for discussing data-driven challenges and opportunities.
“Organizations can embed data literacy into daily operations and culture by making data-driven thinking a core part of every role,” says Choudhary. “Beyond formal training, this includes requiring teams to back decisions with data, providing intuitive dashboards for role-specific insights, and encouraging leadership to model data-centric behavior. Designating data champions in each department helps bridge technical and non-technical teams, while mentorship and recognition programs reward strong data use.”
He also says it’s important to Integrate data concepts into onboarding so new hires can see how data informs their specific responsibilities and that ongoing snackable learning opportunities keep everyone’s skills current. In addition, organizational leadership should share key metrics and success stories to encourage curiosity, so employees are empowered to explore data, question insights, and collaborate across functions to drive better outcomes.
“Looking ahead to 2025, data literacy will be a key driver of innovation and competitiveness. Over time, organizations may progress from ‘literacy’ to ‘fluency,’ where employees actively shape data-driven strategies rather than just consuming insights,” says Choudhary. “Those that foster transparency, accessibility and a culture of continuous learning in their data ecosystems will be best positioned to thrive in this new era.”