16 tips for enhancing data literacy skills for everyone

You've probably heard someone say, "We need to be more data-driven." It sounds straightforward enough — until you realize that being data-driven needs people who actually know how to work with data. But many organizations struggle to close that gap.

Here's what the numbers tell us: 86% of executives tie their career trajectory directly to data literacy. Yet when it comes to actually working with data — i.e., extracting insights, making decisions, and using it day-to-day — fewer than half feel confident they can deliver.

That gap between knowing data matters and actually knowing what to do with it affects everyone from SQL-fluent analysts to business users who just want to answer a question without waiting three weeks. As Tony Avino, who leads analytics engineering at HubSpot, puts it: "To build a data-driven culture, you need four key elements: leadership commitment, data literacy, appropriate tooling, and data accessibility across the organization."

This guide focuses on the data literacy piece, what it actually means and how to build it regardless of where you're starting from.

What is data literacy? And why it matters

Data literacy is the ability to read, work with, analyze, and communicate data in context. In addition to pulling metrics from a dashboard, you need to understand where that data came from, what it represents, and how to explain it to someone who wasn't in the room when you ran the analysis.

Four core competencies make up data literacy:

1. Reading data: Understanding what data represents and how it was collected

2. Working with data: Filtering, pivoting, and exploration

3. Analyzing data: Identifying patterns and drawing conclusions

4. Communicating data: Presenting insights so others can act on them

Most people have some of these skills but not all four. That's normal, and it's fixable.

The disconnect between recognizing data's importance and actually using it confidently shows up everywhere. For data professionals, this looks like constant ad hoc requests from stakeholders who can't self-serve, dashboards that get built but rarely used, and metrics that mean different things to different teams. For business users, it means waiting on answers, working with stale spreadsheets, or turning to general-purpose AI tools that might give you confident-sounding, inaccurate results.

The shift from intuition-driven to evidence-based decision-making is real and significant. But it only works when people throughout the organization can actually engage with evidence because they know how to interrogate data, challenge narratives, and recognize that people ultimately make the decisions.

16 tips for enhancing data literacy skills (no matter who you are)

Building data literacy looks different depending on your starting point. If you're a data professional, you're likely focused on deepening technical skills and improving how you communicate insights. If you're a business user, you might be more interested in self-sufficiency and asking better questions. The tips below cover both paths.

5 data literacy tips for data professionals

Data professionals build literacy by combining technical depth with communication skills and business context. The tips below focus on that combination.

1. Master the art of data storytelling

Communication skills turn good analysis into organizational impact. Technical skills get you the analysis, but storytelling gets you the buy-in.

Before sharing any findings, ask yourself, "What's the one insight that matters the most?" Structure your presentations with context (why this matters), analysis (what you found), and recommendations (what to do about it). Skip the jargon when talking to non-technical stakeholders. Lead with the "so what," not the methodology.

2. Practice with real-world datasets

You build analytical fluency by working through messy, unfamiliar data, not by reading about techniques.

Seek out datasets outside your daily work where you can practice the full workflow, from exploring data structures to cleaning inconsistencies to building analyses and iterating on your approach. Public datasets, open data portals, and side projects all work. The exposure to different data types and problem framings sharpens your ability to tackle unfamiliar datasets when it matters.

3. Build systematic data cleaning habits

Data preparation consumes more time than most people expect, and inconsistent approaches create errors.

Develop a consistent workflow that includes checking data types, identifying nulls and duplicates, documenting every cleaning decision, and writing scripts instead of doing manual operations. Reproducibility matters. Trust us, when you need to rerun an analysis six months later, your future self will thank you.

4. Invest in SQL and Python proficiency

SQL and Python are foundational skills that make every other part of your work faster and more reliable.

For SQL, master window functions, CTEs, and query optimization — challenge yourself to reduce query execution time by 20% to 30% through optimization. For Python, focus on Pandas for data manipulation, NumPy for numerical computing, and visualization libraries like Matplotlib and Seaborn. These technical skills are the building blocks for competitive advantage in data-driven roles.

5. Bridge the gap to business context

Technical excellence without business understanding limits your impact.

Schedule regular conversations with stakeholders. Ask them what decisions they make monthly and what data would make those easier. Understanding how your analytics connects to actual business outcomes changes the questions you ask and the way you frame answers. The data professionals who create the most value are the ones who understand both the data and the decisions it informs.

6 data literacy tips for business users

Business users build data literacy by developing self-sufficiency with the right tools while learning to ask better questions and think critically about the answers they get.

1. Start with specific questions

Specific questions lead to actionable answers — vague exploration leads to wasted time.

Before opening any dashboard, write down exactly what you want to know. "Why did retention drop last quarter?" is a better starting point than browsing metrics without direction. When you know what you're looking for, you can evaluate whether you found it.

2. Learn the four core competencies progressively

Building data literacy works best when you focus on one competency at a time rather than trying to learn everything at once.

Start by developing your ability to read data. Understanding what metrics represent and how they're collected. Next, build your capacity to work with data by filtering, pivoting, and performing basic calculations. Then advance to analyzing data by identifying patterns and drawing conclusions. Finally, develop your ability to communicate insights effectively to stakeholders.

This incremental approach prevents overwhelming yourself with advanced techniques while building confidence with each competency before progressing to the next.

3. Adopt self-service analytics tools

The right tools let you answer routine questions independently while maintaining access to governed, trustworthy data.

Look for platforms with visual interfaces, pre-built templates, and natural language query capabilities. Hex is an AI-assisted platform where data teams and business users work side-by-side. Logic View provides a unified workspace for both SQL and no-code exploration, while Threads offers a natural language interface that lets you ask questions in plain English and get accurate results backed by governed metrics. When teams need to ensure everyone uses consistent metric definitions, Semantic Modeling creates a single source of truth that syncs across all analyses.

At Greenhouse, this shift played out firsthand. "Hex has really helped us level up everybody's ability to answer questions using data and decentralize a lot of product analytics." When business users can explore data independently, the whole organization moves faster.

4. Develop better question-asking frameworks

The quality of your questions determines the quality of your insights.

Move beyond "what happened?" to "why did it happen?" and eventually "what should we do about it?" Progress through four levels of inquiry:

• Descriptive questions:

  What happened? When? How often?

• Diagnostic questions:

  Why did this happen? What factors contributed?

• Predictive questions:

  What might happen next? Under what conditions?

• Prescriptive questions:

  What should we do? What's the optimal action?

Before starting analysis, write down a few (at least five) questions and share with stakeholders for alignment. This ensures questions match business priorities rather than pursuing analytical rabbit holes.

5. Build critical evaluation skills

Healthy skepticism prevents bad data from driving bad decisions.

Not all data is created equal. When you encounter any analysis, ask where this data came from, when it was last updated, whether there are known gaps or limitations, and whether bias could affect these conclusions. The goal isn't to distrust everything — it's to understand the limitations of what you're looking at.

6. Focus on role-specific skills

Generic data training often misses the mark — the skills that matter most depend on the decisions you actually make.

If you're in product management, focus on user behavior analysis, funnel tracking, and A/B test interpretation. If you're in finance, prioritize trend analysis and variance reporting. Marketing? Customer segmentation and campaign measurement. Concentrate on the two to three skills most relevant to your role, and go deep rather than broad.

5 data literacy tips for everyone

These practices accelerate learning regardless of your starting point or technical background — they're the habits that separate people who talk about data from people who actually use it.

1. Practice translating between technical and non-technical language

The ability to move fluidly between technical and business audiences creates more impact than technical skills alone.

If you're technical, explain concepts using analogies and plain English. If you're non-technical, learn enough vocabulary to ask precise questions and understand answers. This translation skill is what makes collaboration between data teams and business users actually work. Practice by explaining your most recent analysis to someone outside your team and asking them what was unclear.

2. Join learning communities

Learning accelerates when you're surrounded by others working on similar problems.

Whether it's Reddit communities like r/datascience, Stack Overflow, local meetups, or internal communities of practice, peer engagement creates accountability and exposes you to approaches you wouldn't discover on your own. Start by lurking, then answering questions you know, then sharing your own work for feedback. This progression builds both skills and confidence.

3. Pair self-service access with governance

Freedom to explore data without guardrails leads to chaos — report proliferation, inconsistent metrics, and decisions based on different versions of the truth.

Work with data teams to identify certified datasets and learn your organization's metric definitions. Before distributing any custom analysis widely, validate it against established standards. Self-service works when paired with shared standards, not when everyone invents their own.

4. Treat learning as continuous, not one-time

Data literacy isn't something you check off as complete — tools evolve, business contexts shift, and the questions worth asking change over time.

Allocate 30 minutes daily to structured learning through courses, tutorials, and hands-on practice. Complement this with quarterly goal-setting to ensure purposeful progression. Master time series analysis one quarter, learn a new visualization technique the next, then deepen understanding of a new data source. This deliberate approach turns learning from passive consumption into strategic skill development.

5. Use AI as an accelerator, not a replacement

AI-assisted tools work best when you understand what they're doing. They speed up your work, not replace your judgment.

Technical users get the most value by reviewing AI-generated SQL and code rather than accepting it blindly. Business users benefit from natural language interfaces that connect to governed data rather than general-purpose chatbots that might hallucinate answers. The goal is faster iteration, not less understanding.

Enable data-driven decisions by improving data literacy

People across organizations need to speak data as a second language. Organizations investing in building data literacy skills (across both technical and non-technical roles) report stronger financial performance driven by better decision-making, smarter investments, and more efficient use of data and analytics. By shifting from intuition-driven to evidence-based decision-making, these organizations make better decisions and waste less time on misaligned metrics and redundant analyses.

Hex's approach to analytics (combining code and AI, in one collaborative workspace) reflects this reality. Whether you're a data scientist building models or a product manager exploring user behavior, the path forward involves working with data more directly and depending less on handoffs and waiting.

If you're ready to see what that looks like in practice, you can sign up for Hex or request a demo.