A Step-by-step Framework to Build a Data Literacy Program

To navigate the intense demands of on-demand customer expectations, constant market shifts, and changing competition, your business needs the clarity and reassurance that data provides to overcome obstacles and secure a path to success. However, due to pressure to be a data-driven build, many line-of-business teams find themselves ill-equipped to discern business insights without relying on their company’s data experts.

While business teams grow increasingly frustrated and self-conscious about their lack of data skills, data experts become overloaded and annoyed with never-ending ad hoc reporting request queues. Many data experts report that a substantial amount of their time is dedicated to preparing these reports for various business needs. This inhibits their ability to put their expertise to use to surface truly impactful business intelligence.

Changing the data skills gap

The data skills gap creates significant tension between teams and hinders an organization’s ability to effectively compete. Many teams report substantial delays in fulfilling typical data requests. These delays often lead business experts to abandon their need for crucial information due to the excessive time required to obtain it. Companies experiencing these challenges are losing ground to organizations that prioritize generating and acting on data-informed strategies.

Data literacy is no longer just a nice-to-have skill for business teams, it’s key to growth. Improving data literacy across the organization requires bridging the gap between data and business teams, a process that begins at the top. 

Leaders of both of these groups must band together and lead the charge to break down the data-language barrier. A comprehensive data literacy program prepares everyone to participate in the data conversation, surface impactful findings, and support exponential business growth. Here we propose a step-by-step framework for building a data literacy program. 

What is data literacy?

Data literacy is the ability to read, understand, analyze, manage, and act on data. An effective data literacy program must focus on developing a framework of technical, communication, and analytical skills, with the confidence to present discoveries with others.

Dr. Francisco Javier Calzada Prado and Dr. Miguel Angela Marzal Garcia-Quismondo, two professors of Information Science in Spain, developed a useful, universal framework for teaching data and information literacy. They divide the instruction of data literacy into five core competencies that build on each other and culminate in data competence, as shown in the pyramid below.

3 types of barriers to data literacy

Despite the existence of such valuable frameworks and the growing imperative for data-informed decision-making, organizations frequently encounter significant obstacles when cultivating data literacy. These roadblocks to achieving widespread data literacy can be categorized in three ways:

Technical limitations of current infrastructure

Utilizing the full value of data requires infrastructure and tools that are accessible to all. Organizations need the capability to collect, store, analyze, and act on data efficiently. However, many data analytics tools require specialized coding knowledge that’s prohibitive to non-technical users. Additionally, growing data compliance, security risks, and requirements cause many companies to keep data assets siloed or restricted entirely.

Organizational silos and communication barriers

In most companies, the language of data is spoken only by the data team, forcing them to take on the responsibility and burden of acting as “keepers of data.” This leaves all other employees totally reliant on their time and expertise, stirring feelings of inadequacy and frustration that lead to a tense culture of division. What’s more, line-of-business teams often work in silos, with no single source of data truth and no mechanisms for sharing insights or building on each other’s work.

Personal comfort levels with data

Making informed decisions and using data insights to influence strategy is relatively new for many non-technical employees. Introducing new tools and methods too quickly can be overwhelming and cause significant fear and anxiety. Organizations must work hard to combat any misconceptions about what data can or can’t do and build confidence in data-driven decision making for people to engage.

The 5-step data literacy framework

Let's explore each level of the data literacy pyramid in depth, examining the common barriers organizations face and providing leaders with proven tactics to overcome them. This framework offers practical guidance to accelerate your organization's progression toward comprehensive data literacy.

Step 1: Understanding data

The foundation of data literacy begins with understanding what data is, its types, and its relevance to business operations. Employees must grasp basic statistical concepts and recognize how data can add value to their roles and the broader organization. 

Yet, technical jargon and complexity often intimidate newcomers, while siloed knowledge and unclear governance prevent a shared understanding across departments. Many employees experience data anxiety and misconceptions about required mathematical skills, leading to resistance or avoidance of data-driven approaches altogether.

Strategies for building data literacy

Organizations can overcome these challenges by developing accessible learning resources with relevant examples from employees' daily work. Creating a common data vocabulary eliminates confusion, while designated champions can translate concepts across teams and departments. When data concepts connect to familiar workplace scenarios, employees more readily embrace them and begin to see the value data literacy brings to their everyday responsibilities.

Step 2: Finding and obtaining data

Once employees understand data fundamentals, they must learn to identify relevant sources, access datasets, and understand collection methods. This stage focuses on determining what data is needed, locating it internally or externally, and acquiring it through proper channels. 

The journey is often complicated by dispersed storage systems and inconsistent access protocols that hinder discovery. Restrictive policies and unclear ownership further complicate acquisition, while employees frequently struggle to articulate their data needs or feel overwhelmed by complex request procedures.

Improving data accessibility through structure

The path forward involves creating intuitive data catalogs with clear ownership information and streamlined request processes. Templates can guide users in defining requirements precisely, while balanced access guidelines protect sensitive information and enable appropriate use. 

When organizations remove unnecessary friction from the data discovery process, employees spend less time searching and more time making discoveries.

Step 3: Reading, interpreting, and evaluating data

With data in hand, employees must bridge technical understanding with practical application by learning to read visualizations, extract insights, recognize patterns, and assess quality. Critical thinking becomes essential as staff learn to question sources, understand confidence levels, and distinguish correlation from causation. 

Complex visualizations and insufficient context often create confusion in this phase, while inconsistent analytical approaches and limited training impede development. Cognitive biases affect interpretation, and statistical anxiety prevents many from engaging deeply with quantitative analyses.

Developing data interpretation skills

Progress comes through guided exercises using real organizational data and establishing thoughtful peer review processes. Focused training on role-specific statistical concepts builds confidence, while resources explaining common chart types improve visual literacy. 

Organizations that normalize questioning data and encourage healthy skepticism develop employees who can identify flawed analyses before they lead to costly decisions.

Step 4: Managing data

The fourth step encompasses organizing, documenting, storing, and maintaining data throughout its lifecycle. Employees apply governance principles, ensure compliance, maintain quality, and create meaningful metadata that enhances organizational data assets. 

Fragmented infrastructure and unclear stewardship responsibilities typically make systematic management difficult. Many view data management as administrative overhead rather than value creation, resulting in perpetual quality issues and duplicated efforts across teams.

Fostering sound data management practices

Successful organizations establish clear ownership frameworks with defined responsibilities for different aspects of the data lifecycle. By integrating management practices into existing workflows rather than adding separate processes, they reduce resistance and improve adoption. 

Automation reduces manual documentation while connecting good practices to tangible outcomes like faster decision-making and reduced rework demonstrates the value of proper data management to skeptical stakeholders.

Step 5: Creating and using data and data sets

At the pinnacle of data literacy, employees effectively use data for decision-making, create informative analyses, combine diverse datasets, and generate substantial business value. 

Advanced tools often present steep learning curves at this stage, while integration challenges halt analysis. Established decision processes may resist data-driven approaches and limited resources for exploratory projects can stifle innovation.

Upskilling employees for advanced data literacy

Organizations can nurture this advanced literacy by creating collaborative spaces for experimental projects that connect analysts with domain experts. Templates for common analyses accelerate the creation process while mentoring relationships transfer specialized skills efficiently across the organization. 

Training in data storytelling techniques helps employees translate technical findings into compelling business narratives that motivate action and demonstrate the full potential of data-informed decision-making.

Measuring success

Success in this data literacy journey manifests through cultural and operational change. Organizations should observe increased frequency and quality of data-related questions in meetings, greater diversity of employees confidently working with data, and more decisions explicitly referencing insights. The time from question to data-informed answer should decrease, while the depth and sophistication of analyses should increase.

As data literacy matures across the organization, a self-reinforcing culture emerges where employees continuously expand their capabilities, mentor colleagues, and advocate for data-informed approaches. By investing in this framework, organizations turn data from an underutilized asset into a strategic advantage that powers innovation and competitive differentiation.