Let’s be honest: most data initiatives don’t fail because of bad tools; they fail because people don’t know what to do with the data.
It’s not that your teams aren’t smart or motivated. It’s that the data feels too technical, too siloed, or just plain confusing.
That’s where data literacy makes a difference. It’s the key to helping everyone, not just the data team, ask better questions, spot real insights, and make confident decisions.
In this guide, we’ll walk through what data literacy is, why it’s worth your time, and how to actually build it into your day-to-day operations.
Table of contents:
Data literacy is knowing how to read, understand, and communicate data in a way that’s genuinely useful. It’s a core skill for making thoughtful, evidence-based decisions in any role.
Picture this: you’re reviewing a customer feedback dashboard. That’s helpful, but what is it really saying? Is satisfaction dropping because of long wait times? Is a new feature confusing users?
Data literacy is what lets you discover the real story behind the numbers and figure out what to do next, like reducing complaints or prioritizing the improvements that matter most to your customers.
With the right skills and tools, you can explore further and ask important questions like “Why did sales drop last month?” or “Which products are selling the most?”
Why should you care about data literacy? Here’s how it can truly make a difference for you and your team:
Make better decisions
Everyone wants to make smart, strategic calls—but too often, those decisions are driven by gut instinct or outdated assumptions instead of solid evidence. In fact, Gartner found that 83% of business strategies fail because they’re built on flawed assumptions.
Data literacy gives you the tools to question those assumptions early. You’ll know how to dig into the data, test ideas, and back up recommendations with solid evidence. That helps you avoid surprises, justify your choices to stakeholders, and steer your team with confidence.
Take Wellthy, for example. They adopted ThoughtSpot to give their care team direct access to data. They improved patient care and reduced the load on their data team by helping everyone find the answers they needed themselves. The team now has 75 active users on ThoughtSpot, which is a 281% increase over their legacy BI tool. This has resulted in at least $200K in direct savings by reducing analyst efficiency.
Avoid bias and bad assumptions
We all have habits, biases, or past experiences that shape how we see things. Data literacy helps you question those assumptions with clear intent instead of accepting them at face value. You’ll be able to test ideas and make sure you’re not building plans on shaky or outdated beliefs. This reduces the risk of expensive missteps down the line.
Work faster
Waiting around for the data team to pull reports slows everyone down. When you know how to find and interpret the data yourself, you can answer questions on the spot, keep projects moving, and avoid those endless back-and-forth cycles. It frees up your data experts to focus on bigger, more complex problems while you get what you need right away.
Improve communication
It’s one thing to understand data yourself, but it’s even more valuable to explain it clearly to others. With better data literacy, you can turn complex numbers into simple, meaningful insights that make sense to everyone. That kind of clarity helps align your team and makes it easier to get buy-in on your ideas.
When Alanna Roesler brought in ThoughtSpot at Schneider Electric, her team improved data access to identify effective recruitment channels and build a clear, inclusive narrative. The quality management team achieved a 75% reduction in overdue derogations. Technical customer issues dropped from 60 to 8 year-to-date. The manufacturing quality department also saved approximately 60,000 FTE hours annually.
Build trust in data
Data doesn’t do much good if no one trusts it. When you and your team know how to analyze and evaluate data, you’re less likely to dismiss it as confusing or unreliable. Instead, you can have more productive, fact-based discussions and make sure everyone feels confident using data to inform their work.
💡Learn how to prevent AI bias and hallucinations. Watch the webinar to help your team build trust in AI-driven insights.
Stay competitive
In a world where things change fast, your ability to learn from data is a huge advantage. Data literacy means you don’t have to wait to react, you can spot trends, address challenges early, and seize opportunities before your competitors do. It’s a key part of building a culture that’s adaptable, proactive, and ready for whatever comes next.
Data literacy doesn’t look the same for everyone - the skills you’ll focus on depend a lot on your role.
For example, if you’re a business leader, you might want to use data to shape strategy and make big-picture decisions. If you’re an analyst or engineer, you’ll lean more on technical skills to dig deep and build models.
But broadly, data literacy skills fall into two categories:
1. Technical data literacy
This is for people who work hands-on with data every day, like analysts, engineers, and data scientists. It’s about having the tools and techniques to really dig in.
Data management: Collecting, storing, cleaning, and organizing data so it’s reliable and ready to use.
Data analysis: Using methods and tools (like SQL, Python, or statistical tests) to explore patterns and answer questions.
Data visualization: Turning raw numbers into clear, meaningful charts and dashboards that tell a story.
Data modeling: Structuring data to answer complex questions and support data-driven decision-making.
Data science: For more advanced teams, this includes building and training predictive models to forecast outcomes.
2. Non-technical data literacy
This is for everyone—from marketing and HR to finance and operations. It’s about understanding and using data without needing to code or run analyses.
Research: Knowing how to find the right data sources and check their quality.
Interpretation: Understanding what the data is really saying, beyond just the surface numbers.
Communication: Sharing data insights clearly so others can act on them.
Critical thinking: Asking smart questions, spotting gaps, and challenging assumptions.
Domain knowledge: Applying your understanding of your industry or business area to make insights truly relevant.
Building data literacy in your team isn’t always easy. If you’re trying to make this happen, here are a few common hurdles you might face and want to plan for:
Your data is stuck in silos
If your data lives in ten different systems that no one can access easily, you’re dealing with data silos that make it impossible for people to use the information even if they want to. Make sure your team has access to the right data in one place so they’re not wasting time chasing it down.
The tools feel too complicated
Ever tried to use a platform so confusing that you gave up? Your team feels the same way. If the tools aren’t intuitive, people will avoid them. Look for solutions that make exploring data as simple as asking a question.
There’s a trust gap
If people don’t trust the data, they won’t use it. Maybe they’ve seen errors before, or the source isn’t clear. You’ll want to focus on data quality and transparency so they feel confident making decisions with it.
Training is one-and-done
A single workshop won’t cut it. People need ongoing support, easy access to help, and time to practice. Make learning part of the workflow so skills actually stick.
Building data literacy takes planning, commitment, and the right approach. If you want to do it well, here’s a simple framework you can follow step by step:
Step 1. Lead by example
If you want your team to use data, you need to show that you do too. Use data in your own decision-making. Ask thoughtful questions in meetings. Celebrate when others back up ideas with evidence. When leaders model data-driven thinking, it becomes part of the culture.
Step 2. Assess where you are today
You can’t fix what you don’t understand. Take time to evaluate your team’s current data literacy levels. Who feels comfortable using data? Who needs more support? This will help you design training that meets people where they are instead of taking a one-size-fits-all approach.
Step 3. Set clear goals
Make sure everyone knows what success looks like. Do you want more teams' self-serving data? Faster decision-making? Fewer errors from bad assumptions? Define these goals clearly so you can measure progress and keep everyone aligned.
Step 4. Design practical, ongoing learning
Avoid generic training that bores everyone. Make it practical and relevant to people’s roles. By providing different formats like workshops, videos, and accessible guides to suit different learning styles, you can focus on real business questions so people see immediate value. Also, build in regular refreshers, advanced sessions for those ready to go deeper, and easy access to help.
For example, Matillion used ThoughtSpot to help non-technical teams explore data on their own. Adoption jumped 60% because people had tools they understood and the support to keep learning. That kind of momentum doesn’t come from a single training session, it comes from making learning stick.
Step 5. Choose the right tools
People won’t use tools that are too complicated or slow. Invest in intuitive, self-service analytics platforms that let users get answers in plain language. The easier it is to use, the more likely people will make data part of their daily work.
For example, with Spotter, ThoughtSpot’s AI Analyst, your teams can search for insights just like they would online, making data exploration easy for everyone, not just the experts.
Step 6. Address resistance head-on
Change is hard. Be upfront about why you’re investing in data literacy. Show how it will make people’s jobs easier, not harder. Listen to concerns and share success stories that make the benefits real.
Step 7. Embed data in daily workflows
Don’t make people switch between ten tools to find insights. Bring analytics into the apps and systems they already use, whether that’s Salesforce, Excel, or Slack.
ThoughtSpot makes this even easier with embedded analytics, so insights show up exactly where your teams work. When data is part of the flow of work, it actually gets used.
Step 8. Celebrate and reinforce success
When teams use data well, highlight it. Recognize individuals who are championing data-driven decisions. Share wins across the company so people see the real impact of data literacy in action.
💡Leverage T-Mobile’s playbook on data democratization to build a data-driven of your own.
Data culture is the environment that makes data literacy stick.
A strong data-driven culture means that using data isn’t an extra step or a special project. It’s simply part of how your organization operates every day. When you focus on building this culture, you make it easier for data literacy to grow and thrive.
Here’s what it looks like:
Data use is expected and routine
Curiosity and questioning are encouraged
Silos break down as teams share insights
Decisions are transparent and evidence-based
Ongoing learning is the norm
If you want your organization to truly use data well, focus on creating a culture that values evidence, questions, and shared understanding. When that mindset is in place, data literacy becomes part of how everyone works. And with tools like ThoughtSpot, it’s easier than ever to make that culture a reality.
💡Discover from industry leaders the best practices for developing a culture around data literacy
A data-literate team is a more confident, more capable team. When your users know how to interpret and communicate data, they make smarter decisions, avoid costly mistakes, and stay ahead of your competitors without bottlenecks.
But that only happens when you pair a strong learning framework with intuitive tools that people actually want to use. That’s where ThoughtSpot comes in. With Spotter, your AI Analyst, your teams can ask questions in plain language and get instant, actionable answers, making data exploration easy for everyone, not just the experts.
If you’re ready to turn data literacy from a training goal into an everyday habit, schedule a demo and see how ThoughtSpot can help you build a truly data-driven culture.
