Designing a Career in Data Visualization in the AI Era

Why curiosity, creativity, and community matter more than ever.

Introduction: What Did You Want to Be When You Grew Up?

When I was growing up, I never imagined I would work in data visualization.

In fact, I didn't even know it was a career.

Like many people working in analytics today, my path wasn't linear. It wasn't carefully planned. It was shaped by curiosity, unexpected opportunities, a willingness to keep learning, and the people who helped me along the way.

Recently, I had the opportunity to speak to the Women in Data Cincinnati community about designing a career in data visualization in the AI era. As I reflected on my journey, I realized that the lessons I've learned extend beyond dashboards and tools.

They're lessons about growth, resilience, creativity, and community.

There Is No Single Path Into Data Visualization

One of the biggest misconceptions about analytics careers is that there is a "right" path.

There isn't.

My own career has included analytics, leadership, consulting, community building, mentoring, and continuous learning. Looking back, the path appears much cleaner than it felt at the time.

The reality was much messier.

Behind every promotion, project, and opportunity were moments of uncertainty:

• Learning new tools while using them on real projects

• Navigating imposter syndrome

• Saying yes before I felt fully ready

• Building relationships and finding community

• Learning through trial and error

  
  

Career growth rarely feels as linear as it looks on LinkedIn.

The Part We Don't Talk About Enough: Imposter Syndrome

If you've ever looked around a room and felt like everyone else knew more than you, you're not alone.

For much of my career, I felt like I was still figuring things out while everyone else had some secret playbook.

The truth is that many successful professionals feel the same way.

What changed for me wasn't suddenly becoming confident.

What changed was realizing that confidence often comes after action, not before it.

You don't eliminate uncertainty before moving forward.

You move forward despite it.

Where Curiosity and Creativity Meet

What ultimately drew me to data visualization wasn't the technology.

It was the intersection of logic and creativity.

Data visualization combines:

• Analysis

• Design

• Communication

• Storytelling

• Problem solving

  
  

For the first time in my career, I found a space where both my analytical and creative sides could coexist.

Data made sense.

Visualization made it meaningful.

That realization changed everything.

Creating to Learn

Some of my greatest professional growth didn't happen because a manager assigned me a project.

It happened because I created things simply to learn.

Through Tableau Public, community challenges, and personal projects, I experimented without the pressure of stakeholder expectations.

Those projects taught me:

• Design principles

• Storytelling techniques

• New technical skills

• Creative problem solving

  
  

Ironically, many of the skills that improved my business dashboards were developed while creating visualizations purely for curiosity and exploration.

Sometimes the best way to grow professionally is to create something that isn't tied to work at all.

Curiosity Is a Career Superpower

People often ask what skills matter most in analytics.

While technical skills are important, one of the most valuable skills I've developed is curiosity.

Curiosity drives better questions.

And better questions lead to better insights.

One framework I frequently use is the "5 Whys."

Rather than immediately solving the first problem presented, keep asking why until you uncover the underlying issue.

The best analysts don't start with charts.

They start with questions.

Designing With Purpose

A common mistake in data visualization is focusing on the chart before understanding the goal.

Every visualization should answer questions like:

• Who is this for?

• What decision needs to be made?

• What action should follow?

• What information is truly necessary?

  
  

The purpose should drive the design.

Not the other way around.

Whether you're building a dashboard for executives or creating a personal project, clarity should always win.

What AI Changes—and What It Doesn't

It's impossible to discuss careers in analytics today without discussing AI.

AI is already helping professionals write code, generate SQL, summarize information, and accelerate workflows.

That's exciting.

But it doesn't replace the skills that make great analysts valuable.

AI can help generate answers.

Humans still need to:

• Ask meaningful questions

• Provide context

• Apply judgment

• Communicate insights

• Understand people

  
  

In many ways, these human-centered skills are becoming even more important.

The future of analytics won't be defined by who can write the most code.

It will be shaped by those who can connect data to decisions.

Community Changed My Career

If there is one factor that accelerated my growth more than any tool, certification, or training program, it was community.

Through user groups, conferences, mentorship, Her Data, and countless conversations with peers, I found opportunities I never would have discovered alone.

Community provided:

• Encouragement

• Feedback

• Inspiration

• Learning

• Friendship

Most importantly, it reminded me that growth doesn't happen in isolation.

Design Your Career

The biggest lesson I've learned is that careers aren't found.

They're designed.

One project at a time.

One skill at a time.

One connection at a time.

Stay curious.

Keep learning.

Share your work.

Find your people.

And don't wait until you feel completely ready.

Because the career you're building may look very different from the one you imagined—and that might be the best part of all.