How Can Data Literacy Be Improved? A Targeted Learning Approach

Data is everywhere—and so is the need to understand it. Whether it’s businesses making strategic decisions, healthcare professionals tracking patient outcomes, or individuals managing personal finances, the ability to interpret and apply data has never been more critical.

Yet for many, data can feel overwhelming. The challenge isn’t a lack of interest—it’s a lack of access to the right tools, support, and training to grow data and AI skills in a meaningful way. This is where an AI literacy course or AI literacy training program can make all the difference.

Without a strong foundation in data and AI literacy, even the most motivated professionals can struggle to make sense of complex information. Upskilling programs focused on AI and data literacy help bridge that gap—turning confusion into confidence and empowering learners at every level.

So, what is data and AI literacy? It’s the starting point for navigating today’s digital landscape with clarity—and it’s a skill set anyone can build.

So, how can data literacy be improved in a way that feels approachable, practical, and enjoyable? Let’s look at a structured learning approach that helps people work with data and truly make sense of it.

What Does It Mean to Be Data Literate?

What is data literacy—really? It goes far beyond knowing how to navigate a spreadsheet.

Data literacy is the ability to read, interpret, question, and communicate data in ways that lead to real insight and action. It blends technical skills with critical thinking—analyzing numbers, identifying patterns, recognizing bias, and applying findings to real-world challenges.

This is the foundation of any effective AI literacy training program. It’s not just about tools or formulas. It’s about developing the mindset and confidence to think with data.

An AI literacy course provides learners with more than skills—it builds fluency. Participants learn how to engage with data meaningfully, ask better questions, and make informed decisions.

Whether delivered as a standalone offering or integrated into a broader upskilling program, strengthening data and AI literacy helps individuals become more effective, more strategic, and more empowered in their roles.

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Steps to Strengthening Data Literacy

Improving data literacy—and building strong data skills—doesn’t happen overnight. It requires ongoing education, hands-on practice, and time to reflect. To develop real confidence, individuals need consistent opportunities to engage with data in context—not just isolated workshops or one-off sessions.

That’s why the most effective AI literacy training isn’t a checkbox. It’s a continuous learning experience woven into daily workflows and decision-making. A well-designed AI literacy course helps learners build fluency over time through applied learning, not just theoretical knowledge.

According to Tableau and Forrester, “82% of decision-makers say all employees should have basic data literacy, but only 47% report access to relevant training.” This gap highlights the urgency for a structured, scalable upskilling program that makes data literacy part of the culture—not an afterthought.

So, what is data literacy in practice? It’s a shared language across teams. A foundation that helps people ask better questions, uncover insights faster, and drive smarter decisions.

Here are key steps individuals and organizations can take to strengthen their data literacy journey.

Step 1: Building a Strong Educational Foundation

The first step is ensuring learners have a clear, structured way to build their knowledge. That means starting with the basics:

• Understanding different types of data and their uses. 
• Learning how to read charts, graphs, and tables effectively. 
• Recognizing the importance of ethical data use. 

To scale data skills across the workforce, organizations must invest in training programs that build foundational knowledge and foster applied learning. Internal learning modules, guided workshops, and structured AI literacy training are essential for long-term growth.

For self-motivated learners, curated resources like books, online tutorials, and real-world case studies can supplement formal instruction. But the most effective outcomes come from programs that combine flexibility, real-time application, and strategic alignment—hallmarks of a well-designed AI literacy course or upskilling program.

The impact is clear. According to Qlik, “85% of data literate employees say they’re performing very well at work, compared with just 54% of the wider workforce.” This demonstrates that AI literacy training isn’t a luxury—it’s a performance multiplier.

What is data literacy, really? It’s the shift from passive review to active interpretation. It’s about applying data in real-world scenarios to extract meaningful insights and make confident decisions. And in today’s workplace, that’s not optional—it’s essential.

Step 2: Gaining Hands-On Experience

Think about learning a new language. You can study vocabulary and grammar all day, but until you start speaking, you won’t truly grasp it. The same is true for data literacy. Hands-on experience is critical. Here’s how to obtain and refine these skils:

• Work with real datasets: Try analyzing a public dataset. 
• Clean and organize data: Learn how to structure messy data sets effectively. 
• Use visualization tools: Turn raw numbers into compelling stories. 

Organizations can grow data skills by embedding hands-on learning into daily work—not just formal sessions. This means integrating more data-focused tasks into everyday workflows, encouraging employees to ask data-driven questions, and creating mentorship opportunities with experienced analysts.

These real-time applications are what make AI literacy training stick. They turn abstract concepts into habits that drive better decisions. A robust AI literacy course should always include opportunities to apply knowledge in context—because that’s where true learning happens.

What is data literacy if not the confidence to use data in decision-making, not just analysis? When organizations support these everyday moments, they’re not just teaching skills. They’re building a culture of fluency, curiosity, and action.

This is the heart of any successful upskilling program—one that doesn’t just inform, but transforms.

Step 3: Cultivating Critical Thinking Skills

Data literacy isn’t just about working with numbers, it’s about thinking critically. A data-literate person should always ask:

• Where is this data coming from, and how reliable is it? 
• What assumptions might be influencing this analysis? 
• Could biases be affecting the interpretation of these results? 

Developing a healthy skepticism toward data is a critical part of AI literacy training. It helps individuals move beyond surface-level analysis and start asking deeper, more strategic questions—about data sources, context, and impact.

This kind of critical thinking is what separates basic data familiarity from true fluency. A high-quality AI literacy course encourages learners to challenge assumptions, recognize bias, and apply insights thoughtfully—not just crunch numbers.

What is data literacy if not the ability to think deeply, interpret data with clarity, and act with confidence? These habits elevate everyday decision-making and are essential to any effective upskilling program.
By building this mindset into how teams work with data, organizations don’t just grow skills. They create a culture of informed, empowered, and data-literate professionals.

Step 4: Making Data a Part of Daily Work and Decision-Making

For organizations, the ultimate goal should be to create a culture where data isn’t just something used by analysts, but rather that it informs decisions at all levels. Here are some ways to ensure this happens:

• Make data tools accessible to all employees, not just IT teams.
• Encourage employees to incorporate data-driven insights into reports and presentations.
• Provide feedback and foster discussions on how data is used in decision-making processes.

For individuals, one of the most effective ways to build AI literacy is by weaving data analysis into daily life. Whether it’s tracking personal finances, reviewing fitness stats, or exploring trends on social media, these everyday interactions help reinforce the core skills taught in any AI literacy training or AI literacy course.

What is data literacy if not the ability to interpret and apply information in real-world contexts? These personal experiences offer practical, low-stakes opportunities to grow data skills over time.

Regular practice builds more than just technical ability—it builds lasting confidence. And when that individual growth is supported by a broader upskilling program, it creates a ripple effect that strengthens data literacy across teams and organizations.

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Why Improving Data Literacy Matters

Data literacy isn’t just for analysts or tech professionals—it’s a universal skill. It affects every team, every decision, and every level of an organization. When approached through AI literacy training or a thoughtfully designed AI literacy course, it becomes a shared language—one that breaks down silos and empowers collaboration.

Data-literate teams are more confident, more aligned, and more effective. They can interpret and act on information with clarity, no matter their function or role. That’s the true power of a strong upskilling program focused on AI and data: it equips individuals to navigate complexity and contribute meaningfully across the organization.

What is data literacy if not the bridge between raw data and informed action? With the right support, structure, and consistent practice, anyone can build the confidence and competence to thrive in today’s data-driven world.

So whether you’re an organization looking to train your workforce or an individual hoping to level up your skills, taking small, consistent steps toward better data literacy will pay off immensely.

At Data Society, we offer comprehensive training programs to help professionals and businesses establish data literacy. More learning and insights can be found here.

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