Data Literacy vs Data Fluency: What’s Changing for Analysts
As an analyst, do you know the difference between data literacy and data fluency?
Five years ago, everyone was talking about data literacy. But that’s old news.
As an analyst, we need to be more than data literate. Today, we’re competing with software companies promising AI agents that make analytics accessible to everyone. We’re competing with data scientists training models to automate insight generation. And we’re working to catch the eye of our executives—to show them how our unique blend of skills drives real business value.
Up until now, people saw us as wizards—people who could turn vast, disconnected data into insights, visualizations, and dashboards. But now, we need to go further. We need to connect those analyses to business strategy. We can do what AI cannot.
We need to be data fluent.
So what does that mean in an analyst role? Here are 8 key learning areas to help us upskill and take our careers to new heights:
1. Lead with Data
Leaders don’t just want dashboards—they want direction. We need to:
Earn the trust of decision makers in the business.
Balance data insights with business context and intuition.
Use data to tell a compelling story that drives action.
2. Know your data ecosystem
Being fluent means knowing:
Where data comes from and how it’s collected.
Which data sources are most reliable and relevant.
Knowing when to trust the data—and when to question it.
3. Write data stories
Data storytelling is a superpower. It involves:
Structuring analyses like a narrative: setup, conflict, resolution.
Using visuals to enhance the message.
Ethically tailoring the story to the audience, whether it’s technical or executive.
4. Practice soft skills to uncover insights that drive innovation
Don’t just answer questions—ask better ones. Data fluency means:
Proactively identifying opportunities in the data.
Encouraging curiosity and critical thinking in our teams.
Pushing beyond surface-level metrics to uncover root causes.
5. Collaborate and network
Data fluency thrives in community. We should:
Work cross-functionally within our roles.
Share our knowledge and learn from others.
Build relationships that help us understand the world better.
Care for ourselves and our communities
6. Be an industry leader in understanding the benefits and drawbacks of new tools and technologies
Stay ahead of the curve by:
Evaluating new tools (like AI, LLMs, and automation platforms) critically.
Understanding their limitations and ethical implications.
Knowing when to adopt, when to wait, and when to say no.
7. Foster a data-driven culture where everyone feels empowered to use data in their daily work
True fluency means enabling others. This includes:
Creating self-serve tools and documentation.
Teaching others how to use data ethically.
Encouraging a mindset where data is part of every decision.
8. Stay Curious, Stay Healthy
Data fluency starts with our habits and personal goals. We can’t be great in our careers if we’re not caring for ourselves first:
Build resilience by caring for our personal needs.
Book dedicated time for learning and personal growth.
Know our strengths, work with our weaknesses.
Final Thought:
Data literacy is knowing how to read data.
Data fluency is knowing how to lead with data.
Post conceived of by Justeen Gales and written with the support of Microsoft Copilot