20 Reasons Data Literacy Efforts Fail (And How To Address Them)

Data now fuels everything from daily operations to long-term strategy, making data literacy—the ability to read, understand and leverage insights from data—a core business skill for professionals across industries and specialties. But even with growing awareness, many companies still struggle to make their data literacy efforts stick.

The most effective programs integrate data into everyday workflows, tailor training to specific roles, and foster a culture where people trust and understand the data they’re using. Below, members of Forbes Technology Council share common pitfalls they’ve seen in data literacy initiatives and offer advice on how companies can build stronger, more data-fluent teams.

1. Training Focuses On Generic Concepts

One of the biggest reasons data literacy efforts fail is that they feel disconnected from day-to-day responsibilities. When training focuses on generic concepts—like data types, statistical terms or how to read a chart—without grounding it in actual decisions employees make, data literacy becomes just a “nice to know” or “nice to have” skill. – Asif Mujahid, Quartz Health Solutions

2. Employees Are Asked To Understand Every Data Point

One reason data literacy fails is that companies overwhelm employees with the complexity of the data landscape, asking them to understand every data point, many of which they’ll never use. The solution is simple: Stop fighting the data war on complexity’s terms. By narrowing the focus to what’s truly relevant, companies can simplify literacy and empower better decisions. – David Redekop, ADAMnetworks

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3. No Thought Has Been Given To The Company’s Real Needs

I feel not enough time is given to the preparatory work—the assessment of the “real” needs of the company. It all starts with understanding company culture, individual profiles and expectations, and overall data management maturity. Too often, we see companies running into training solutions and courses without the required understanding of the scope and business objectives to be achieved. – Nino Letteriello, FIT Group

4. Data Literacy Is Treated As A One-Time Training

Data literacy efforts fail when they’re treated as a one-time training program rather than embedding data into the way people work. Even trained teams struggle if insights aren’t accessible or actionable in the flow of work. Success comes from designing systems where relevant insights and next-best actions find the user, ensuring data is intuitive, timely and a natural part of decision-making. – Sarah Edwards, Kantata

5. Motivation And Time Investment Are Lacking

Math—particularly statistics—is hard, and it needs to be learned in a systematic environment to avoid falling into pitfalls and logical fallacies. Teaching it in an industry setting requires extremely motivated individuals and a lot of time investment. Companies can motivate employees by showing a real preference in performance reviews for evidence backed by statistics, such as A/B tests. – Harvey Hu, General Agency

6. Companies Don’t Build Systems To Act On Insights

Data alone doesn’t drive change; action does. Too often, companies stop at insights without building the systems to act on them. By using AI to surface and prioritize actions and breaking down silos between teams, you transform insights into real impact and create a culture of momentum. – Cheryl Johnson, Betterworks

7. Training Isn’t Prioritized Across The Organization

A common reason data literacy efforts fail is that organizations have access to vast amounts of data, but only a few people truly know how to read, analyze and use it. The key to addressing this gap is building a culture where data fluency is prioritized: Train employees at all levels, not just analysts, so they can confidently interpret and apply data in decision-making. – Todd Fisher, CallTrackingMetrics

8. Common Platforms Don’t Provide Structured Data Models

The main reason data literacy efforts fail is because organizations are using data platforms that don’t provide a structured data model. As a result, it becomes quite difficult to understand the data and the relations therein. Having a structured data model and a data dictionary to accompany it is a great first step. – Bill Bruno, Celebrus

9. Employees Lack Grounding In Fundamental Statistics

One reason data literacy efforts fail is due to a lack of numeracy and fundamental understanding of statistics. This needs to be caught at the interview stage. If an employee doesn’t understand statistics 101 after a semester in school, they aren’t going to learn it in a two-hour training session. – Sam Glassenberg, Level Ex

10. Team Members Don’t Understand How It Benefits Them

Companies jump straight into teaching hard skills without first answering the fundamental question every employee has: “What’s in it for me?” Instead of starting with the technical details, organizations should begin by showing how data can solve real problems in each role. Once people see the personal benefit, they’re much more likely to engage with and retain the technical skills. – Pawel Rzeszucinski, Webpros

11. It’s Not Embedded Into Everyday Workflows

One common reason data literacy efforts fail is that companies address data literacy in standalone training rather than focusing on integrated capabilities. When companies fail to embed data skills into daily workflows and decision-making processes, they miss the chance to make data-driven thinking a core competency. Continuous, context-specific training, paired with real-world application, turns data literacy into a strategic asset. – Andrey Kalyuzhnyy, 8allocate

12. Company Data Is Inaccessible Or Unreliable

Organizations overlook the need to build trust in their data; issues like siloed information, inconsistent definitions and weak governance erode confidence and stall adoption. Companies can address this by prioritizing transparent data practices and embedding trust into every layer of their data ecosystems, making data both accessible and reliable for all teams. – Darshan Kapashi, Socratic

13. Data Tools Come With A Steep Learning Curve

I’ve seen professionals struggle with data literacy because the tools have a high learning curve. This complexity is necessary because creating insights often requires sophisticated algorithms to select, filter and group data. To help with data literacy, organizations can run a self-service initiative where “data champions” work alongside others to help them create the insights they need. – Tobin Harris, Pocketworks

14. Employees Are Pressured To Become Technical Experts

One best practice is to shift the burden from people to the data infrastructure by embedding trust, automating governance and ensuring explainable, self-serve AI tools. This empowers employees to make confident, data-driven decisions without becoming technical experts, closing the gap between insights and action. – Anusha Nerella, State Street Corporation

15. Access Context Is Overlooked

Data literacy efforts often fail because they overlook access context—teaching people how to read data without clarifying what data they should (or shouldn’t) access. Embedding identity governance into literacy programs ensures employees understand not just how to interpret data, but also how to handle it responsibly, reinforcing both insight and trust across the organization. – Craig Davies, Gathid

16. The Education Isn’t Engaging Or Practical

Data literacy fails when it’s just a boring workshop everyone forgets. For real change, weave data into everything you do each day. Give teams practical tools they’ll actually use, and ensure leaders walk the talk and make data a natural part of the workflow—not some extra corporate thing. That’s how data becomes second nature, not another task to ignore. – Mohit Menghnani, Twilio

17. Companies Take A One-Size-Fits-All Approach

Data literacy efforts often fail due to a one-size-fits-all training approach that overlooks varying roles, tools and skill levels across the organization. To address this, companies should design role-specific, hands-on learning experiences that tie data concepts to real job functions, empowering employees to confidently use data in their everyday decision-making. – Govinda Rao Banothu, Cognizant Technology Solutions

18. Employees Resist Training Efforts

One of the many reasons why data literacy efforts often fail is due to employee resistance—28% of leaders cite fear of change, low motivation or perceived irrelevance as barriers. Companies can address this by fostering a strong data culture, clearly linking data skills to role-specific success, and incentivizing continuous learning and application of data-driven insights. – Jagbir Kaur, Google

19. Companies Emphasize Tools Over Context

Data literacy efforts often fail because they emphasize tools over context. Instead of just teaching dashboards, try to embed real business scenarios into training, pair teams with data translators and foster curiosity through leadership modeling. This will transform passive data users into strategic thinkers who can question, interpret and act on insights confidently. – Raghu Para, Ford Motor Company

20. People Don’t Know What ‘Data Literacy’ Means

Even the name “data literacy” sounds daunting. As with so many things data-related, there’s a problem in the way we articulate value. If you say “data literacy,” people think of learning to code. Ultimately, when we talk about data literacy, what we’re talking about is the ability to understand and glean insights from information. This can be an exciting journey for even the least technical person. – Lewis Wynne-Jones, ThinkData Works