June 16, 2020

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

Nazim Foufa
Marketing Content Specialist
A Step-by-step Framework to Build a Data Literacy Program

In an age of on-demand customer expectations, constant market vacillation, and rapidly evolving competition, the clarity and reassurance data provides is critical to running a successful business.

But as pressures to be data-driven build, line of business teams find themselves ill-equipped to uncover business insights without relying on their company’s data experts. A whopping 39% of business domain experts admit they’re “not totally sure” what being data-driven means, according to a recent survey report.

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. More than three quarters (76%) of data experts say between 20 — 49% of their time is spent preparing these reports for line of business needs. This inhibits their ability to put their expertise to use to surface truly transformative business insights.



Nearly 40% of teams say it takes upwards of 2 weeks to fill an average data request.

The data skills gap does more than stoke frustration between teams — it restricts an organization’s ability to compete. Nearly 40% of teams say it takes upwards of 2 weeks to fill an average data request. Delays like these have caused 1 in 4 (25%) business experts to give up on getting an answer they needed because it simply took too long. These companies are quickly losing market share to organizations that make generating and taking action on data insights a key priority — insight-driven companies are growing 8x faster than global GDP.

Data literacy is no longer just a nice to have skill for business teams, it’s key to growth. And bridging the gap between data and business teams to improve data literacy across the organization starts 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 insights, and drive 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 — and the confidence to present new 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 growing need to make data-driven decisions, companies trying to cultivate data literacy can encounter significant resistance. Roadblocks to data literacy can be categorized in three ways:


Harnessing the full value of data requires infrastructure and tools that are accessible to all. Organizations need the capability to collect, store, analyze, and action 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.


 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 ultimately 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.


Making data-driven 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 around 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

This ebook examines each of these barriers at every step of the data competency pyramid. It provides leaders with proven tactics, tools, and techniques to effectively overcome these barriers and move their organizations forward on the path to data literacy.

Step 1: Understanding data

For people to use data effectively in their roles, they must first understand it. This includes making it clear why they should care enough to understand. Business users need to fully grasp the tangible benefits of incorporating data into their work for the program to have any chance for success.

Getting a read on what your employees already know and understand is a critical first step to building an effective data literacy framework. At the same time, leadership must secure data experts’ support in educating their colleagues by conveying the positive impact true, organization-wide data literacy will have on their day-to-day jobs.

Barriers at this stage:


Better understanding data means learning the basic definitions and functionalities of data types, tools, and processes like JSON, SQL, data modeling, ETL/ELT, and data warehouses.


Assumptions about the level of data understanding that exists in the larger org may be perpetuating knowledge gaps and fueling a lack of empathy or patience on the data team’s side.


Learning new skills often feels intimidating, overwhelming, and confusing. Without proper context and understanding, some may wrongly conclude data is irrelevant in their roles. Others may consider themselves too advanced to need training.

Key action items:

1. Conduct an assessment

Determine existing and missing knowledge and establish a baseline through an employee survey. Use this information to figure out what people want to learn and how they envision being able to use data so you can customize your program to match their needs and expectations.

2. Set clear and measurable goals

How will the company know if the program is successful? Since the purpose is to bring everyone together for the good of the business by enabling them to create and use data, try setting a “BHAG” (or big, hairy, audacious goal). This could range anywhere from each participant eventually creating and maintaining their own data dashboard to reducing the time the data team spends generating high-level reports for business teams by a certain percentage.

3. Segment your audience

Organize your employees into groups based on their role, job level, or current data understanding (as determined by your assessment). This is key to avoiding a “one size fits all” program and ensuring everyone gets the level and/or type of training they need.

4. Use the “buddy system”

Depending on the size of your audience segments, it may be necessary to break them into smaller groups of 3-5 people who help one another and discuss key topics throughout the program. Be sure each group contains at least one data team member to help provide guidance and answer questions. This also encourages data experts to feel more invested and involved in the program.

5. Build tailored training

As you begin to formulate training modules for each segment, clearly focus on:

  • Terms or phrases they will need to understand and use. A dictionary of common data terminology and acronyms helps reduce confusion.
  • A brief overview of the types of data being collected on a regular basis.
  • An explanation of how data fits into their workflow and will help them improve and contribute to the business.

We are Sigma.

Sigma is a cloud-native analytics platform that uses a familiar spreadsheet interface to give business users instant access to explore and get insights from their cloud data warehouse. It requires no code or special training to explore billions of rows, augment with new data, or perform “what if” analysis on all data in real⁠-⁠time.