Building a Data-Literate Team: A Comprehensive Guide

Defining literacy as only “understanding how reading works” borders on problematic.

Broadly, literacy empowers individuals and enables societal participation. While it begins with acquiring basic skills, literacy requires ongoing learning and upskilling to evolve into an experiential foundation—one that enables individuals to navigate increasingly complex social, cultural, and relational landscapes. As such, it plays a critical role in fostering individual growth and collective progress.

For data leaders, literacy is a concept worth detailing in this way because it reminds those who trade in data engineering circles (and their data-adjacent peers) that data literacy constitutes far more than simply “understanding data.”

Data literacy, much like its conceptual older cousin, is foundational for personal empowerment and professional success in data-driven organizations. It also begins with acquiring basic skills—understanding, analyzing, and manipulating data—and evolves into an experiential foundation for data professionals.

In the world of data engineering, this subfloor enables the data-literate to navigate increasingly complex organizational, strategically focused, and technologically complex data environments (and the organizations that increasingly depend upon them). As such, data literacy plays a critical role in fostering individual growth within the profession, helping both data professionals and leaders improve their decision-making while advancing collective progress within data-driven cultures.

It's no wonder that many data leaders prioritize data literacy and the upskilling needed to support it. Fortunately, achieving this goal is simpler than it seems. First, one simply needs to determine how data-literate their organization is at the moment. That information forms the foundation for operationalizing an organization’s data culture and ensuring that it supports ongoing learning and upskilling to bolster data literacy.

A four-point process for assessing your organization’s current data literacy

To improve data literacy skills within an organization, one must first understand the current level of those skills skills. However, assessing an entire company’s data skills can seem daunting, especially for data leaders operating at the enterprise level.

First, don’t panic. Start by assessing key teams or departments based on the business’s needs and time-to-impact. Second, consistently follow a structured approach to ensure you can repeat your data literacy assessments as needed and scale to your needs over time. 

As such, here’s a simple but effective approach you could follow to assess your company's needs.

1. Define what data literacy means in your organization and industry

Using our broad definition above as a starting point, tailor your definition of data literacy to your organization’s specific goals, challenges, and context, as well as its industry.

For example, the chief data officer of a B2B software startup might focus on product usage metrics, customer behavior, and market trends. In contrast, a chief technology officer at a mid-sized bank might emphasize financial data interpretation, risk management, and regulatory compliance. Meanwhile, the head of analytics at a healthcare wearable devices company could decide to invest more time focusing on healthcare data quality, interoperability, and the ethical use of patient data.

As a rule, data leaders should treat their data literacy definitions as minimum viable products. These definitions should include just enough detail to capture key organizational realities—such as business operations and stakeholder priorities—while balancing general data knowledge (understanding, interpreting, and communicating data) with role-specific competencies.

2. Conduct an initial assessment

Armed with a clear data literacy definition, data leaders can conduct an initial assessment to establish a baseline of the organization’s current data literacy levels. In most cases, leaders use one or more methods to gather this information: surveys and self-assessments, quizzes and tests, and practical assessments.

Each method offers unique advantages and drawbacks which require leaders to balance their approach based on organizational needs.

Surveys and self-assessments

Surveys typically involve structured questionnaires to gauge individuals’ perceptions of their data skills. Self-assessments allow employees to evaluate their own competencies based on predefined criteria or scales.

Pros:

• Quick to deploy across large groups with minimal resources
• Captures employees' confidence levels, attitudes toward data, and perceived skill gaps
• Suitable for organization-wide assessments

Cons:

• Potential for bias or inaccuracy from self-reported results
• May not fully capture actual technical skills or application abilities

Ideal for:

• Initial benchmarking of organizational data literacy levels
• Identifying perceived gaps to guide further assessments or training programs

Quizzes and tests

Standardized tests or quizzes measure objective knowledge of data concepts, tools, and processes. Formats may include multiple-choice questions, theoretical concept tests, or scenario-based problem-solving.

Pros:

• Provides quantitative results that are easy to compare and track over time
• Can assess specific skills like data visualization, statistical analysis, or tool proficiency
• May use platforms with auto-grading functionality, increasing efficiency

Cons:

• Focuses on theoretical knowledge rather than real-world application
• Potential misrepresentation of skills from test anxiety or format confusion

Ideal for:

• Assessing baseline technical knowledge
• Tracking progress after training programs

Practical assessments

Involving hands-on tasks that require employees to apply their data skills in real-world scenarios, these assessments focus on common yet critical situations such as analyzing datasets and creating dashboards.

Pros:

• Measures how well individuals can apply knowledge in practical contexts
• Highlights critical thinking, problem-solving abilities, and tool proficiency
• Enables targeted coaching based on observed performance gaps

Cons:

• Requires significant time for setup, evaluation, and feedback
• Difficult to administer across large teams without automation tools

Ideal for:

• Evaluating team readiness for specific projects or roles
• Identifying high-potential employees for advanced training

3. Categorize and analyze your assessment results

After completing the assessment, data leaders should categorize the gathered information to facilitate analysis. Although additional steps may be necessary based on business needs, initial assessments typically focus on three key actions: grouping employees, identifying gaps, and evaluating behavioral indicators.

First, group employees into categories (novice, intermediate, or advanced) based on their skills and roles. Next, identify gaps in knowledge or skills at both individual and organizational levels. Finally, assess behavioral indicators, such as how employees apply data insights in decision-making or validate data sources. 

These steps provide a clear picture of your organization's current data literacy and its key areas for improvement.

4. Blueprint a data literacy roadmap

Ultimately, once categorization and analysis are complete, data leaders should apply a competency framework—a structured model that clearly defines the data knowledge, competencies, and skills employees need to succeed across roles and proficiency levels, from beginner to expert.

Leaders should start by adopting existing frameworks (like Databilities or Future Skills) or templates to prioritize effective implementation, foster internal adoption, troubleshoot challenges, and build valuable feedback loops. This approach actively engages organizational culture, driving long-term data literacy growth.

Putting a data-centric culture to work: Increasing knowledge and improving skills

Perhaps the most impactful cultures begin with a clear understanding that no culture is innate. They are living constructs that, like data literacy, organizations define, invest in, and enrich through collective action and individual contributions.

Additionally, culture isn’t just a “vibe.” No amount of talent, skill, experience, or budget can create culture on its own—especially not a data-centric one. In this sense, a three-person startup in a garage is just as capable of building a strong culture as a global technology giant.

Data leaders—and leadership as a whole—must actively shape a culture they can put to work, knowing that the ongoing impact of any data literacy program by way of organizational culture depends on how effectively the latter fosters action, learning, and growth.

That said, putting one's culture to work to improve data literacy and related skills should begin through a strategic focus on seven specific steps:

1. Embed data literacy into the core of organizational culture

Leaders must set the tone by using data to guide their decisions at every opportunity. When executives and managers consistently rely on data quality, data quality rules, and data-driven insights, they send a clear signal to all levels of the organization that facts and figures matter. By leading through action, leaders encourage everyone—regardless of function—to consider how data can inform and improve everyday tasks.

In addition, managers should openly encourage experimentation and provide employees with the freedom to test new hypotheses and learn from any shortcomings. This trust-forward environment drives creativity and builds employees’ confidence in applying their data literacy skills. 

Storytelling can also play a key role in this cultural shift. When leaders share success stories highlighting specific ways that data has improved processes or driven tangible outcomes, they reinforce the value of data-centric thinking and show how it directly benefits both individuals and teams.

2. Build a supportive corresponding data infrastructure

A culture that values data literacy needs to provide employees with all the necessary tools, systems, and policies to succeed. Robust data repositories, for example, help employees access accurate, reliable information whenever they need it. Investing in user-friendly, self-service analytics platforms further promotes independence by allowing employees to explore data and generate insights without waiting on specialized teams.

Equally important, data governance policies must be clear, transparent, and actively enforced. These policies ensure that data remains accurate, secure, and trustworthy—building organization-wide confidence in the information employees use to make decisions.

When employees believe in the quality and security of their data, they are far more likely to adopt data-driven practices across different roles.

3. Provide tailored training and upskilling opportunities

Organizations should design training programs that align with various teams’ unique data needs, to ensure that each employee learns the specific skills that are most relevant to their role. 

For example, marketing departments that are looking to strengthen their data skills might focus on mastering data analytics tools that track customer engagement, while finance teams could specialize in predictive modeling and forecasting. This is important, as employees who clearly understand how data literacy can enhance their everyday tasks become more invested in their own learning.

To support this growing interest, organizations must offer diverse learning opportunities that align with different roles and learning styles. Combining workshops, online courses, and hands-on projects provides a blended learning model that appeals to various learning preferences. Gamification techniques, such as monthly “hackathons,” can also boost engagement. These initiatives not only reinforce newly acquired skills but also promote camaraderie and friendly competition, further embedding data literacy into the workplace experience.

4. Establish a continuous learning ecosystem

To maintain momentum, data leaders need to lead by example, treating data literacy as an ongoing journey rather than a one-time initiative. Integrating data literacy into onboarding programs sets the foundation early, ensuring that new hires understand the organization’s commitment to data-driven practices from day one. Regular refresher courses reinforce this culture of learning and prevent knowledge gaps from growing over time.

Providing resources like data glossaries and shared vocabularies also helps employees navigate complex datasets confidently and consistently. When an individual can easily reference standardized terminology and access self-service analytics tools, they feel empowered to deepen their data literacy skills independently.

5. Incentivize data literacy adoption

Organizations should align incentives with data-centric behaviors and reward employees who apply data-driven insights to solve business problems or improve processes. Recognizing these contributions through performance bonuses, public acknowledgment in team meetings, or promotions linked to data competency sends a strong message about the value of data literacy.

When employees see tangible rewards for embracing data, they are more likely to experiment, learn, and share successes with their colleagues. This positive reinforcement loops back into the culture, perpetually motivating teams to raise the bar on how they use data.

6. Foster collaboration across departments

Encouraging cross-functional teams to tackle data-focused projects promotes knowledge exchange and breaks down organizational silos. When employees from marketing, finance, operations, and other areas pool their expertise and insights, they can uncover powerful new ways to use data. These collaborations help non-data teams see firsthand how data-driven insights benefit their work.

Designating “data champions” within each department can accelerate this process. These champions mentor peers on data best practices and advocate for continued adoption. By creating formal and informal forums for sharing insights, organizations reinforce the idea that data literacy is everyone’s responsibility, not just the domain of specialized teams.

7. Monitor progress, demonstrate ROI, and keep literacy aligned with business goals over time

Organizations should regularly assess data literacy, just as they did to establish their operational baseline, by measuring improvements in data literacy with tools such as surveys, skill assessments, or analytics tool usage statistics. By continuing to track these key metrics, leaders can identify gaps, refine training efforts, and maintain momentum.

Measuring the impact of increased data literacy on business outcomes—such as operational efficiency, customer satisfaction, or revenue growth—also highlights the tangible value of these initiatives. Further sharing success stories and metrics organization-wide energizes employees and reinforces the importance of continuously refining data skill sets. 

When everyone sees real-life examples of data-driven projects that achieved or exceeded their goals, it inspires broader participation and strengthens the organization’s data culture.

By combining these strategies, organizations can ensure that their efforts to build a data-centric culture not only enhance employee skills but also deliver tangible benefits, such as improved job performance, more career growth opportunities, and greater organizational efficiency.

Keeping a strong data culture focused on what matters

The hidden benefit of culture is how, in a short time, it aligns more of the organization toward a single operational direction. Add a culture that fosters and rewards continuous learners, and the true power of data quickly comes to light, regardless of industry or business type.

In a data-literate organization, treating data as a product becomes the norm, and edge cases turn into use cases that highlight ROI. Skill levels overshadow titles and pay scales, empowering employees to advance within the organization.

However, this transformation doesn’t happen on its own. Data leaders must actively champion these changes by embedding data-driven decision-making and innovative data strategies into daily operations. This hands-on leadership ensures these practices become standard—data-literate normalization at its finest.

Over time, data literacy can begin to drive itself, shifting data leaders’ roles from pushing and supporting to guiding and challenging. At this stage, it becomes more important to focus on where a strong data culture is going, not where it has been. This is where the concept of shifting data left and the role of the next generation of data contracts come into play.

By integrating data quality and governance earlier in the development process, organizations can accelerate innovation and reduce costly errors—unlocking the full potential of their data strategies.

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