Data Literacy: Definition, Skills, and Importance

Data access is one thing. But if you don’t understand how to read and interpret that data, it can’t be useful.

Data literacy is the ability to analyze, interpret, and apply information in a meaningful way. It’s the core of any process that requires lots of data to function, like sales and marketing, because it helps teams make informed, traceable decisions.

Here’s a guide to why data literacy is important and how to implement a successful data literacy framework.

What Is Data Literacy, and Why Is Data Literacy Important?

Data literacy is your team’s ability to analyze data accurately and apply it meaningfully. It’s about how well they can derive insights from (often large, complex) datasets to produce informed decisions.

Team members don’t have to be experts to be functionally data literate. It starts with understanding basic concepts (like where data comes from), interpreting what it means, and knowing how to put those insights into action. And as employees upskill, they should develop deeper competence in inspecting, sorting, visualizing, and ethically applying data.

11 Data Literacy Skills Every Team Needs

The definition of data literacy skills spans technical and non-technical competencies, which play equally important roles. Let’s explore each area.

Technical Data Literacy Skills

To be fully data literate, people generally need to be able to:

1. Access data: This means locating and retrieving data from various sources, using appropriate tools or queries.

2. Inspect data: They should know how to examine a dataset’s structure and contents to understand what it includes and catch any glaring issues.

3. Clean data: Finding and fixing errors keeps the dataset accurate and prepares it for analysis.

4. Filter data: This refers to narrowing a dataset to only the records that meet specific criteria, focusing on relevant information while excluding irrelevant data.

5. Sort data: Arrange data in a meaningful sequence for analysis.

6. Visualize data: Software platforms can convert data into visual representations so patterns become clear at a glance.

7. Calculate basic descriptive statistics: This means computing fundamental summary metrics, like mean, median, mode, and standard deviation.

Non-Technical Data Literacy Skills

Think of data literacy’s non-technical skills in terms of the following four areas: research, critical thinking, communication, and ethical reasoning.

1. Research: Data-literate team members should be able to effectively identify and validate relevant data. This broad skillset spans from cross-checking sources to recognizing potential biases.

2. Critical thinking: The ability to analyze raw data critically is what lets team members derive meaningful insights and conclusions. This includes scrutinizing data, questioning underlying assumptions, and rigorously verifying results.

3. Communication: Effectively communicating with data is about translating technical findings into compelling narratives so everyone can understand the results.

4. Ethical reasoning: Whether proactively anticipating algorithmic bias or mitigating data privacy violations, data-literate team members uphold rigorous ethical standards at each step of the data lifecycle.

Implementing a Successful Data Literacy Framework

While data literacy is important, it’s still common for businesses to experience challenges putting it into practice.

Amazon Web Services (AWS) and the Data Literacy Program have each developed data literacy frameworks to help people learn how to read and analyze data with ease. These aren’t the only options, but they offer a framework to make education easier. Let’s explore each one.

Amazon Web Services

AWS outlines a four-step framework for building a successful organizational data literacy program:

1. Plan

During the plan phase, leadership defines clear program objectives, identifies initiative owners and champions, and assesses existing skills gaps. They use these insights to define the target skills and form a development plan with clear success criteria.

2. Curate

In the curate phase, initiative owners identify learning resources that fill skills gaps. They select targeted content from reputable educational or industry providers, choosing a mix of formats — from brief instructional videos to full certification pathways — to meet everyone’s learning needs.

3. Engage

The engage phase focuses on building momentum and securing broad support for the data literacy initiative. It involves:

• Securing executive sponsorship

• Prompting visible leadership support

• Implementing a clear communications plan

• Promoting inclusive learning opportunities

• Integrating continuous learning habits into company culture

4. Measure

AWS advises businesses to measure program success across three areas:

• Engagement (program enrollment, participation rates, and feedback)

• Skills proficiency (increased competence in previously identified skills gaps)

• Agency outcomes (increased retention rates, improved forecasting accuracy, and higher sales productivity)

These metrics inform continuous improvement. The goal is to routinely track success indicators and quickly iterate based on what’s working — and what isn’t.

The Data Literacy Program

Qlik created the Data Literacy Project consortium, and its Data Literacy Program operates within this initiative. The initiative guides organizations through a product-agnostic, six-step data literacy framework:

1. Planning and Vision

To implement data literacy initiatives effectively, the program’s framework emphasizes three key components:

• Participants: Select an initial cohort to take part in the program. This may be a small firm’s entire workforce or a select group of motivated individuals within an enterprise.

• Funding: Secure executive sponsorship and integrate the program into existing budgets or establish a dedicated budget.

• Timeframe: Set a clear program launch date. Before launch, aim to establish a plan, build a communications strategy, and assess members' data literacy — all within three months.

2. Communication

This step is about launching the data literacy initiative on the right foot with an effective communications strategy.

The framework advises businesses to:

• Clearly explain what data literacy means, why it matters, and the initiative’s objectives.

• Announce the initiative with visible executive sponsorship.

• Address the entire organization early to establish expectations, resolve concerns and uncertainties, and emphasize the program’s benefits.

• Keep those directly involved in the program continuously updated. They should be clear on what the program’s stages are, timelines, and expectations.

Communications should let everyone involved know that the data literacy initiative is an ongoing effort — not a one-off — and that leadership has woven into the company’s long-term growth strategy.

3. Workforce Assessment

During this step, the program team evaluates each participant’s data literacy level to identify individual skills gaps and inform personalized learning paths. The Data Literacy Program’s framework advises businesses to take this one step further by categorizing members into personas based on their level of data literacy.

The personas are:

• Data aristocrat: These are the most data-literate employees. They may work as data scientists or just have extensive experience in data management.

• Data knight: These highly motivated team members are already proficient in working with data and driven to upskill.

• Data dreamer: Beginners in data literacy might recognize and value data’s benefits, but are still developing fundamental competencies.

• Data doubter: Doubters are skeptics who don’t understand the importance of data literacy and lack basic training. They may prefer intuition over data-driven analysis and are hesitant to shift their mindset.

Using personas helps tailor training programs to each competency level.

4. Cultural Learning

This step is about evolving company culture to embed data-driven thinking organically. Organizations should approach data literacy as a change management exercise, gradually weaving data practices into everyday workflows.

To achieve this, businesses should:

• Integrate data literacy training into new-hire onboarding systems.

• Encourage the use of data in regular work activities.

• Provide protected time during working hours for employees to upskill in data management.

• Maintain transparent, ongoing communication about the data literacy program and emphasize wins.

5. Prescriptive Learning

Instead of a one-size-fits-all curriculum, participants should follow personalized learning programs (or roadmaps) based on their individual skill levels, which correspond to the personas above. The Data Literacy Project provides roadmaps based on the four personas.

6. Measurement

This step is all about identifying what is working, what isn’t, and determining how to refine the program to improve future outcomes. It should lead back into step one. Cycle through the steps, and intentionally iterate to drive continuous improvement and strengthen the program’s impact over time.

Rox: Empowering Modern Sales Teams With AI

Data literacy is an important part of sales. But with agentic CRMs like Rox, AI does the work for you.

Rox provides sales reps with cutting-edge AI swarms, which automatically complete tasks for you and make even your most data-literate team members just that much more capable. Rox automatically consolidates relevant sales data from multiple sources to provide your reps with actionable business intelligence, from automated pipeline generation to proactive renewal forecasting.

Gain actionable pipeline intelligence, faster — with AI. Watch a demo and learn how.