6 essential practices for modern teams fostering data literacy across your enterprise

According to Gartner, more than 84% of organizations say less than half of their employees understand how to use the data tools provided.

Fostering a culture of data literacy is essential for driving innovation, making informed decisions, and staying competitive for any modern organization.

A data-literate workforce can analyze and interpret data, leading to better collaboration, innovation, and risk management. It also helps organizations optimize operations, understand customer behavior, and adapt to digital transformations, ultimately driving growth and success in a data-driven world.

Keep reading to better understand the ins and outs of data literacy and the best tips for increasing your teams’ understanding of data projects and products.

What is data literacy?

Data literacy can be defined as the ability of individuals and organisations to read, work with, analyse, interpret, and communicate data in context.

In practical terms, a data-literate workforce knows not just how to view a dashboard or chart, but also where the data came from, how reliable it is, what assumptions underlie it, and how to apply insights to business decisions.

For a company using DataGalaxy as its governance backbone, data literacy means employees understand the lifecycle of data products, the roles of data owners, data stewards, and the governance frameworks in place, and can meaningfully engage with those assets — not just consume them.

Why this matters now:

• According to Gartner, many organisations list poor data literacy among their top five barriers to data & analytics (D&A) success
• We are in the era of AI-augmented analytics. Understanding how to critique AI outputs, evaluate algorithmic bias, and trust data-driven insights is essential
• As data becomes a strategic asset, the workforce that can engage with it intelligently becomes a competitive differentiator.

Why data literacy must be an organizational priority

When teams across the business can meaningfully interact with data, your organisation gains:

• Faster and more reliable decision-making with insights grounded in data rather than intuition
• Improved collaboration and innovation because silos break down when business, operations, analytics, and product teams speak a shared data language
• Stronger governance and trust when employees understand the provenance, quality, and boundaries of data, the value of governance platforms like DataGalaxy becomes clearer.
• Adaptability in the face of disruption. Data literacy enables teams to pivot, experiment, and respond rapidly to new opportunities or risks.

In short: data literacy is the bridge between your data asset strategy and tangible business outcomes.

The hidden cost of low data literacy

According to Gartner, more than 84% of organizations report that less than half of their workforce understands how to use the data tools provided.

When employees aren’t confident with data:

• Technology goes underused
• Data products fail to deliver expected business value
• Decision-making reverts to instinct over insight

This isn’t a tooling problem—it’s a skills, culture, and enablement problem. Data literacy programs are meant to solve these issues, but many fail because they start too late or target only “data people.”

To build a true data-driven organization, data literacy must begin at the foundation.

Why data literacy must start at the ground level

The common misconception: “Data literacy is for analysts.”

Many organizations still believe that only data scientists, analysts, and technical teams need data education. In reality, this mindset creates a bottleneck and reinforces silos.

When only experts receive training:

• Adoption of data tools remains low
• Non-technical teams avoid using data
• CDAO initiatives fall short
• Data investments fail to deliver ROI

Data literacy is everyone’s job—from HR and finance to operations, sales, and customer support.

Two core reasons to prioritize foundational data literacy

Low adoption leads to unrealized potential

If only a small subset of employees feels confident working with data, the organization loses opportunities to:

• Improve day-to-day decision-making
• Identify inefficiencies
• Reduce operational risk
• Innovate with AI and automation

This is why enterprise-wide literacy is non-negotiable.

Unresolved obstacles reduce business value

Misunderstanding data, misinterpreting metrics, or mistrusting data outputs can unravel even the most advanced data programs.

Data literacy removes these barriers by giving everyone the confidence to engage with data responsibly.

Who should drive data literacy?

The leadership role: CDOs & CDAOs

Chief Data Officers (CDOs) and Chief Data & Analytics Officers (CDAOs) are responsible for building a data culture.

Their role is not just technical. It’s strategic and organizational.

Their mission includes:

1. Demonstrating impact

CDOs must show business outcomes, KPIs, and ROI examples to justify investment in literacy and governance.

2. Delegating with consistency

While the CDO defines the strategy, training delivery must be distributed across teams—but always with a unified message.

3. Designing data & AI products that deliver value

Successful organizations build data products and AI products based on:

• Clear governance rules
• Strong data quality foundations
• Shared business context
• Product thinking and iteration

Storytelling for increasing data literacy

The power of a good story is transformative, especially for data literacy.

When woven with a compelling tale, lifeless numbers begin to take on meaning and even provide answers to real-world problems. This point is crucial in our modern, data-saturated world.

Narrative techniques turn dry, complex data into engaging stories for a broader audience.

This approach can suit a company’s non-technical units and be an effective tool for any audience.

Targeting narratives to specific roles

Role-specific storytelling targets data narratives to specific roles and functions, enhancing understanding and application.

• For instance, a marketing manager in a retail company might focus on customer engagement metrics and market trends, which are crucial for their role.
  
  They may begin with an overview of a recent marketing campaign’s impact on online engagement, revealing how different demographics responded. This approach indicates, for example, that younger audiences are more engaged on social media, and older demographics show higher interaction rates via email.

Through such a narrative, the marketing manager comprehensively understands the raw data and its underlying trends, turning abstract numbers into meaningful insights and enhancing their data literacy and utilization.

Strategies like role-play scenarios or case studies specific to business functions are invaluable when transitioning into broader team onboarding.

• For a sales team in a data governance program, narratives could revolve around customer acquisition trends and sales performance. Conversely, the focus for a supply chain team might be on inventory management data and logistics efficiency, making data governance concepts directly applicable to their daily tasks.

Therefore, contextualization is key to making data meaningful by transforming sterile numbers into engaging stories and scenarios. Let’s explore how these strategies can boost knowledge and decision-making across various business units.

Fostering data literacy through narrative engagement

Incorporating narrative into data literacy initiatives can significantly increase an organization’s effective use of data.

Let’s explore actionable ways to leverage narrative through literacy initiatives:

• Integrating storytelling in training modules: Use realistic case studies.
• Interactive data narratives: Create interactive experiences where learners can navigate through stories at their own pace.
• Role-playing and scenario-based learning: Encourage learners to participate in role-playing exercises.
• Use of visual storytelling: Leverage visual storytelling tools like infographics and data visualizations. These tools can turn complex data sets into clear, engaging stories that are easy to understand and remember. For example, an infographic on patient recovery rates offers healthcare managers a visual record of healthcare outcomes rather than a dry table filled with numbers.
• Storytelling workshops: Conduct workshops to develop data narrative proficiency.
• Cognitive influence of storytelling: Acknowledge and employ the mental aspects of storytelling. Narratives help form mental models, making it easier for the brain to process and remember information. By framing data within stories, learners better comprehend and recall complex information.

Narrative engagement dynamically enhances data literacy. It renders data understandable, memorable, and actionable, transforming numbers into informative, persuasive, and inspiring stories.

6 best practices for driving organizational data literacy

Here are six key practices to build a culture of data literacy.

1. Cultivate a data-driven mindset

Having a data-driven mindset means equipping your workforce to see data as a strategic customer-centric asset rather than just rows and charts. Encourage behaviours like:

• Asking data questions: “What does the data tell us?” rather than “What do I assume?”
• Using evidence rather than gut feel to make decisions
• Viewing experimentation as a learning process: using data to validate ideas

Celebrate wins driven by data-based initiatives and reward curiosity and intelligence rather than the status quo.

2. Establish a robust data governance framework

A high-functioning governance framework is vital for data literacy because it provides the structure, roles, processes, and tools that make data understandable and trustworthy.

For example:

• Define roles such as Data Owner, Data Steward, and Data Consumer (refer to your DataGalaxy glossary for precise definitions)
• Establish guidelines for data collection, storage, cataloguing, quality, lineage, usage, and retirement
• Ensure the governance tool (e.g., DataGalaxy) provides visibility into data lineage, definitions, data-product inventories, and compliance status

When employees know who owns which data, what purpose it serves, and how it’s managed, their confidence climbs and data becomes actionable rather than abstract.

3. Promote data sharing & cross-functional collaboration

Data silos restrict your organisation from harnessing the full potential of data literacy. Encourage:

• Shared platforms and dashboards accessible to multiple teams
• Cross-discipline workshops where business users and data professionals collaborate on real-world use-cases (e.g., marketing + data science + operations)
• A “data catalog” culture where data assets are discoverable and described in business terms (again — DataGalaxy helps here)

Such collaboration enhances domain understanding, leverages diverse insights, and fosters a shared responsibility for data quality and interpretation.

4. Democratize data access

Democratising data means giving non-technical employees the tools and permissions to explore, analyse, and act on data while still maintaining governance and control.

Key tactics include:

• Self-service analytics platforms (with governance guardrails)
• Business user training sessions on intuitive dashboards, not just Excel sheets
• Ensuring data products (cataloged and defined in DataGalaxy) are accessible to the right personas with appropriate documentation

Democratisation helps scale data literacy beyond the “data team” and embeds it deep into the functions that drive value.

5. Secure executive & leadership support

Leadership sponsorship is essential. When senior executives commit to data literacy, they:

• Signal that data-driven decisions matter across the enterprise
• Allocate budgets for training, platforms, and governance
• Lead by example (use dashboards, ask data questions, make decisions based on data)

In 2024/25, thought-leadership pieces highlight that a foundational understanding of data will be as critical for C-suite executives as reading a P&L.

6. Implement comprehensive training & skill-building programs

Training must be continuous, tailored to levels (from business users to analysts to data scientists), and aligned with the organisation’s data strategy. Effective programs include:

• Role-based learning paths (e.g., “Business User Data Literacy”, “Data Steward Certification”)
• Hands-on use-cases anchored in real data-products governed in DataGalaxy
• Workshops, peer-learning, dashboards, walkthroughs, and gamified modules
• Measurement of engagement, skill growth, and business outcomes (Gartner recommends this)

Multilingual AI: Breaking language barriers for effortless data collaboration

By integrating advanced translation and multilingual search capabilities into DataGalaxy, we’re breaking down barriers in understanding to foster a truly global, data-driven culture.

Learn more

How DataGalaxy supports your data literacy journey

DataGalaxy plays a pivotal role in advancing your data literacy culture. Here’s how:

• Data-product catalog: It helps catalog all data assets, making them discoverable and described in business language — enabling easier onboarding for non-technical users.
• Lineage & impact maps: Visualising data flows and dependencies helps users understand provenance, which builds trust and literacy.
• Governance workflows: By embedding governance, roles, and documentation, DataGalaxy ensures data literacy isn’t just training, but part of everyday operations.
• Role-based access & training links: Users know who to ask, where to find definitions, and how to consume data safely.
• Analytics & metrics: Track adoption of data products, engagement in training, and link literacy initiatives to business outcomes.

In short: DataGalaxy acts as the structural backbone that supports the culture, processes, tools, and roles needed for enterprise-wide data literacy.

Find. Trust. Request. Use. Repeat.

Give business teams a dedicated space to explore, understand, and request trusted data without relying on support tickets.

Discover the marketplace

Measuring success & maintaining momentum

To ensure your data literacy efforts stay effective and evolve with your organisation:

• Define KPIs: e.g., percentage of employees certified in data literacy, number of data products consumed, number of “data queries” resolved, and reduced decision-cycle time
• Conduct maturity assessments: Use frameworks (see Gartner’s model) to benchmark your population across literacy levels
• Link to business outcomes: Show how improved literacy led to cost savings, innovation, and faster time-to-market
• Make it iterative: Data environments evolve (AI, ML pipelines, data products), so literacy programs must evolve too
• Celebrate wins: Recognise teams who made data-informed decisions, share success stories, and maintain leadership visibility

In conclusion, driving organizational data literacy is essential for staying competitive and fostering innovation.

By cultivating a data-driven mindset, establishing strong data governance, promoting data sharing and collaboration, democratizing data access, securing leadership support, and implementing comprehensive training programs, organizations can empower their workforce to leverage data effectively.

These strategies help ensure that data literacy is not just a skill but a core part of the organization’s culture, enabling better decision-making, enhanced collaboration, and sustainable growth.

FAQ

How do you improve data quality? –

Improving data quality starts with clear standards for accuracy, completeness, consistency, and timeliness. It involves profiling, fixing anomalies, and setting up controls to prevent future issues. Ongoing collaboration across teams ensures reliable data at scale.

Can I build my own data catalog? +

You could, but you shouldn’t. Custom solutions are hard to scale, difficult to maintain, and lack governance features. Off-the-shelf platforms like DataGalaxy are purpose-built, continuously updated, and ready for enterprise complexity.

Do I need a data catalog? +

If your teams are struggling to find data, understand its meaning, or trust its source — then yes. A data catalog helps you centralize, document, and connect data assets across your ecosystem. It’s the foundation of any data-driven organization.
👉 Want to go deeper? Check out:
https://www.datagalaxy.com/en/blog/what-is-a-data-catalog/

Can a data catalog scale with my team as we grow? +

Absolutely. A robust catalog supports multi-domain growth, role-based access, and metadata from an expanding tech stack. DataGalaxy is designed to grow with your needs — across teams, geographies, and governance maturity.

Can I compare DataGalaxy to other data catalog tools? +

Yes. We provide detailed comparisons vs. Alation, Collibra, Atlan, and others — or you can request a personalized assessment.

About the author

Jessica Sandifer

With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.

• Share on LinkedIn
• Share on X