Breaking Down Silos: 8 Ways to Build Data Literacy Between Technical and Business Teams

There's a growing divide between what's technically possible with data and what organizations actually achieve. Building data literacy across teams has become essential, as despite pouring money into data infrastructure and specialized teams, many companies struggle to extract real value from their data.

Data literacy—the ability for everyone to access, understand, interpret, and act on data regardless of technical background—has become the missing puzzle piece. As data environments grow more sophisticated, the gap between data teams' capabilities and business teams' needs keeps widening. 

This disconnect creates daily frustrations across organizations, highlighting common collaboration challenges. Smart organizations are building data literacy across teams, creating environments where technical and non-technical teams work in harmony toward shared goals. 

Let's explore eight practical strategies to break down barriers between your business and data teams.

1. Establish a common data language

Technical jargon creates massive barriers between data teams and business users. When data pros talk about "ETL processes," "data normalization," or "regression models," business stakeholders get lost, leading to misunderstandings and missed opportunities.

A common problem is how different departments define basic terms. For example, "customer" might mean:

• For sales: A company that has signed a contract
• For marketing: Any prospect who has engaged with content
• For data teams: A unique identifier in the database with specific attributes

These inconsistencies create confusion when analyzing "customer churn" or "satisfaction" metrics, as teams are talking about entirely different groups.

Building data literacy across teams starts with data dictionaries and business glossaries to create alignment. A data dictionary provides technical metadata about your data assets, whether they are structured or unstructured data, while a business glossary translates technical elements into business-friendly terms.

According to recent Correlation One research, organizations with standardized data terminology collaborate much more effectively, as they don't waste time clarifying terms during meetings and analysis.

For maximum impact, these resources should be:

• Collaboratively developed by both technical and business teams
• Easily accessible where people work (embedded in dashboards, linked in reports)
• Regularly updated as terminology evolves
• Written in plain language that anyone can understand

Implementing these shared vocabularies actively reduces friction in cross-team projects. When everyone operates from the same playbook of definitions—readily available and easily understood—analyses become more consistent, discussions more productive, and the path from data to insight significantly smoother. This foundation of shared understanding prevents teams from talking past each other and fosters genuine collaboration.

When everyone speaks the same language, teams can move beyond basic terminology questions to focus on what matters: extracting valuable insights that drive business decisions.

2. Build trust through data governance

Data governance has traditionally belonged to IT and data teams—a set of restrictions limiting data access. But modern governance frameworks should enable rather than restrict, especially when building data literacy across teams.

Good governance creates guardrails within which business users can confidently explore data without fear of breaking something or causing compliance issues. Think of it as freedom within a framework, where teams can innovate while maintaining data integrity.

When implementing governance to support business users, focus on:

• Quality checks that ensure business users work with reliable data they can trust
• Clear visibility into data lineage so users understand where information comes from
• Standardized definitions of metrics that create a common language across departments
• Access controls that make sense for business roles, rather than creating unnecessary barriers

Data teams often worry that self-service analytics will lead to chaos—multiple versions of truth competing across the organization. Good governance addresses this by establishing centralized truth sources while still enabling decentralized analysis.

When business users see governance as quality assurance rather than red tape, they develop confidence in their data interactions. Each quality check builds credibility, encouraging wider adoption of analytics tools and a more data-informed culture.

Remember that scaling data literacy means creating an environment where everyone feels safe and empowered to work with data. Thoughtful governance builds that trust by balancing data access and governance.

3. Implement the right data access infrastructure

Building data literacy across teams requires a solid foundation—data infrastructure that enables safe, scalable access for everyone, regardless of technical expertise. Modern cloud platforms like Databricks create a unified foundation serving both technical and business users.

The key to bridging the gap lies in choosing the right data strategy and technical architecture. When done well, this infrastructure creates multiple access points for different skill levels while maintaining a single source of truth.

For technical teams, this means direct access to powerful processing capabilities. For business users, it means intuitive visual interfaces that hide the complexity. These abstraction layers don't reduce the power of the platform—they simply make it accessible to those who don't need to see the inner workings.

A well-designed data infrastructure enables both governance and self-service simultaneously. By building governance into the foundation, you provide self-service access that empowers users while maintaining security and quality standards. Implementing modern data integration practices allows organizations to build such scalable and flexible infrastructures.

Cloud data platforms remove friction between teams by providing appropriate interfaces for different skills. Data engineers can work with raw data, analysts can use SQL-based tools, and business users can interact through dashboards and natural language queries—all working from the same trusted data source.

4. Reimagine the data request process

The traditional data request process creates needless bottlenecks. Think about how this typically works: Marketing needs customer segments, so they create a ticket, wait for the data team to review it, attend clarification meetings, wait again for data preparation, and finally receive results, often days or weeks later.

This handoff model turns data teams into gatekeepers rather than enablers. By the time business users get answers, the opportunity may have passed or the original question has changed, starting the cycle again.

This disconnect between business needs and data delivery timelines creates fundamental frustrations. As Sanjeev Mohan, former Gartner Research VP and data architecture expert, explained in our recent webinar, "Business teams don't want to get blocked by a data engineering team that takes forever to respond to their request. They want comprehensive access to data from different sources that they can trust, especially when time-sensitive decisions are on the line."

A better process shifts this relationship. Instead of submitting tickets and waiting, business users access self-service platforms to explore, prepare, and analyze data themselves. Building data literacy across teams transforms the data team's role from processing requests to:

• Building robust data infrastructure
• Ensuring data quality and governance
• Providing training and support
• Handling only the most complex analyses

CZ, a healthcare insurance platform, has successfully implemented this approach, empowering employees across departments to make quicker, data-driven decisions without constant IT involvement. This transformation reduces dependency on specialized teams, reducing IT strain, eliminates bottlenecks, and speeds up the insight-to-action cycle, giving your organization a competitive advantage.

5. Choose the right self-service tools

Self-service data tools need to balance power and accessibility—they must handle complex data tasks while remaining usable for non-technical business teams. When designed well, these tools become the foundation of building data literacy across teams and democratizing data across your organization.

The best self-service tools use intuitive visual interfaces that shield users from technical complexity. Rather than requiring SQL knowledge, they offer drag-and-drop functionality and visual workflows. Tools like ThoughtSpot provide search-based analytics with natural language capabilities, allowing users to ask questions in plain language instead of writing queries.

These visual interfaces should present data transformations as logical building blocks. A marketing professional should be able to visually join customer demographics with purchase history without understanding JOIN syntax in SQL.

This democratization of data transformation capabilities is particularly crucial as our survey indicates that 47% of data teams cite excessive time creating pipelines as their primary data processing challenge. 

AI is transforming self-service analytics by automating repetitive tasks and providing smart suggestions. Look for tools that can:

• Automatically detect data types and anomalies
• Suggest relevant transformations based on data patterns
• Recommend visualizations appropriate for specific data
• Explain complex relationships in plain language

These AI assistants act as virtual data experts, guiding non-technical users through best practices they wouldn't otherwise know.

Good self-service tools connect seamlessly to enterprise data while allowing users to incorporate their own data when needed. This flexibility lets business users enrich company data with their specialized knowledge without compromising governance.

The most effective tools are designed around business workflows rather than technical capabilities. A sales analyst should be able to follow a guided process for creating a territory analysis that automatically incorporates the relevant data steps without requiring technical knowledge of each component.

By focusing on business outcomes rather than technical features, these tools make data preparation accessible to everyone who needs it.

6. Embed quality controls within self-service tools

The fear that self-service analytics leads to data chaos is common. When business users work with data independently, there's a legitimate concern about inconsistent results and governance problems. However, modern approaches provide effective guardrails while still empowering non-technical users.

Today's self-service platforms include built-in quality monitoring that automatically checks data before it's used for decisions. These systems flag anomalies, missing values, or inconsistencies that might cause faulty analysis. This means business users can work confidently with data without deep technical expertise in validation.

One effective approach to maintaining quality is through reusable, pre-validated components. Data engineers can create standardized templates, visualization libraries, and analysis patterns that business users simply plug their data into. This combines the technical rigor of data engineers with the business knowledge of domain experts.

Version control has been reimagined for business users. Modern platforms like Prophecy offer intuitive interfaces that track changes, allow comparisons between versions, and enable easy rollbacks without requiring technical knowledge. This means business users can experiment confidently, knowing they can always return to a trusted state.

Successful organizations have found that governance should enable rather than restrict. Data engineers establish the technical foundation—data models, security protocols, and quality standards—while business users leverage these foundations through simplified interfaces. Data profiling tools that traditionally required coding can now be accessed through visual interfaces that highlight potential issues without overwhelming users with technical details.

By utilizing self-service data preparation, organizations can balance quality control with the benefits of self-service analytics.

7. Define success metrics across teams

Measuring the effectiveness of data literacy initiatives is critical to ensuring continued investment and support. The most successful organizations track both operational and business impact metrics, creating a holistic view of how building data literacy across teams affects their entire organization.

To track the day-to-day impact of your data literacy program, consider measuring:

• Time to insight: How long does it take for a business question to be answered with data? As data literacy improves, this timeframe should decrease significantly.
• Data request backlog: Monitor the number of pending data requests to the analytics team. As self-service capabilities improve, this number should decline.
• Self-service adoption rate: Track the percentage of data questions answered through self-service versus those requiring analyst intervention. Aim for an increasing trend over time.
• Tool usage metrics: Measure the frequency and depth of use for self-service analytics platforms across different departments.

Numbers tell only part of the story. To get a complete picture, you need to assess the cultural impact of data literacy:

• Trust index: Regularly survey teams to gauge trust levels between technical and business departments.
• Collaboration frequency: Track cross-functional data projects and collaborative sessions between data teams and business units.
• Data confidence score: Measure how confident employees feel when interpreting and using data in their daily work.

Remember that the most effective metrics are those that matter to both data teams and business stakeholders. When selecting your measurements, ensure they reflect improvements that both groups value—technical teams want to see reduced ad-hoc requests, while business teams want faster insights and better decisions.

By consistently tracking and communicating these metrics, you'll maintain momentum for your data literacy initiatives and demonstrate their ongoing value to the organization.

8. Create effective feedback loops between teams

Building data literacy across teams isn't a one-time event but an ongoing process that requires continuous refinement. The most successful data literacy initiatives create structured feedback loops between technical and business teams to ensure tools, processes, and training evolve to meet changing needs.

Scheduling dedicated time for cross-functional retrospectives after major data initiatives helps identify what worked and what didn't. These sessions should be safe spaces where both technical and business users can share their experiences openly.

For example, a marketing team might share that a dashboard built by the data team contained all the right metrics but was difficult to navigate, leading to a redesign that dramatically increased adoption.

Organizations seeing the best results often establish data communities of practice where business and technical teams can regularly exchange ideas. This helps in overcoming data silos and allows business users to share use cases and challenges while data teams can introduce new capabilities and gather requirements. This two-way exchange helps ensure data tools evolve based on real business needs rather than technical assumptions.

Appointing data champions within business departments creates a vital bridge between teams. These individuals understand both the business context and have enough technical knowledge to effectively communicate with data teams. They can translate business needs into technical requirements and help colleagues understand the capabilities and limitations of data tools.

Creating formal channels for collecting feedback ensures insights don't get lost. This might include:

• Regular surveys that measure tool adoption and satisfaction
• Office hours where data teams are available to help and learn from business users
• Feature request systems that allow business users to suggest improvements
• Usage analytics that reveal which data tools and features are actually being used

By consistently gathering and acting on feedback from both technical and business users, organizations can ensure their data literacy programs remain relevant, effective, and aligned with evolving business priorities.

Bridge the data gap with self-service data integration platforms

Building data literacy across teams requires both a cultural shift and a strong technological foundation to succeed. Prophecy helps organizations implement governed self-service data preparation that democratizes data while maintaining control.

Here’s how Prophecy enables teams to work together effectively:

• Visual interface and code equivalence: Visual data pipelines that automatically generate production-grade code, allowing both technical and business users to work in their preferred environment
• Git integration: Built-in version control that manages changes and enables collaboration across teams with different technical backgrounds
• Metadata management: Comprehensive tracking of data lineage and impact analysis to build trust in data across the organization
• Low-code/no-code options: User-friendly interfaces that reduce barriers to entry for business users while maintaining data governance
• Enterprise-grade security: Role-based access controls that protect sensitive data while enabling appropriate access for different team members
• Seamless cloud integration: Native compatibility with modern data platforms like Databricks

To break down data silos that prevent cross-team collaboration and slow innovation, explore how Prophecy can deliver self-service data prep without the risk to democratize data access while maintaining governance and accelerating insights across your organization.