Data Viz: A Must-Have Skill for Global Citizens

The world demands clarity. As internationally-minded professionals consuming news daily, we need information presented in a way that’s not just accurate, but immediately understandable. That’s why mastering data visualizations is no longer optional, it’s essential. Are you ready to unlock the power of visual storytelling?

Opinion: Data Visualization is a Core Competency for Global Citizens

I believe that the ability to interpret and create data visualizations is a core competency for anyone who wants to engage meaningfully with the world today, especially those of us who follow international news. We’re bombarded with charts, graphs, and maps designed to inform – or, let’s be honest, to persuade. Without the skills to critically assess these visuals, we’re vulnerable to manipulation and misinformation. It’s time to move beyond simply reading text and embrace visual literacy.

Think about it: global issues like climate change, economic inequality, and political instability are inherently complex. Raw data alone is overwhelming. Effective data visualizations can distill these complexities into digestible insights, revealing trends, patterns, and anomalies that would otherwise remain hidden. But this power comes with responsibility. As consumers of news, we need to demand better visualizations and be able to spot misleading or biased representations. As professionals, we need to create visualizations that are accurate, transparent, and ethically sound.

From Spreadsheets to Stories: Choosing the Right Visual

The first step in mastering data visualizations is understanding the different types of charts and graphs available, and when to use them. Too often, people default to the charts they’re most familiar with, regardless of whether they’re the best choice for the data and the message. A pie chart, for example, is great for showing proportions of a whole, but terrible for comparing multiple categories with subtle differences. I had a client last year who insisted on using pie charts to show market share data for seven different companies. The slices were almost indistinguishable! We switched to a bar chart, and suddenly the relative performance of each company became clear.

Here’s a quick guide to some common chart types and their best uses:

• Bar charts: Comparing values across different categories.
• Line charts: Showing trends over time.
• Scatter plots: Identifying relationships between two variables.
• Maps: Displaying geographical data and patterns.
• Histograms: Showing the distribution of a single variable.

The key is to start with the story you want to tell. What question are you trying to answer? What insights do you want to highlight? Once you’re clear on your objective, the right chart type will become much more apparent. Don’t be afraid to experiment and try different approaches. And remember, simplicity is often the best policy. A clear, uncluttered visualization is far more effective than a complex, visually stunning one that’s difficult to understand.

Tools of the Trade: Tableau and D3.js

While there are many software packages available for creating data visualizations, two stand out for their power and versatility: Tableau and D3.js. Tableau is a user-friendly, drag-and-drop tool that’s ideal for exploring data and creating interactive dashboards. It’s a great option for journalists, analysts, and anyone who needs to quickly visualize data without writing code.

On the other hand, D3.js is a JavaScript library that gives you complete control over every aspect of your visualization. It’s more complex to learn than Tableau, but it allows you to create highly customized, web-based visualizations that are impossible to achieve with other tools. If you’re a web developer or designer, D3.js is an invaluable skill to have.

We ran into this exact issue at my previous firm. We needed to create an interactive map showing the flow of refugees across international borders. Tableau could handle the basic map, but it couldn’t provide the level of customization we needed to show the complex migration patterns. We ended up using D3.js to create a custom map that allowed users to filter by country, time period, and demographic group. The result was a powerful and engaging visualization that told a compelling story.

Critical Consumption: Spotting Bias and Misinformation

The ability to create data visualizations is only half the battle. We also need to be critical consumers of the visuals we encounter in the news and online. Just because a chart looks professional doesn’t mean it’s accurate or unbiased. In fact, visualizations can be easily manipulated to distort the truth and push a particular agenda.

Here’s what nobody tells you: sometimes the bias is not intentional. Visualizations can be misleading simply because of poor design choices. For example, truncating the y-axis of a bar chart can exaggerate differences between categories, making small changes appear much larger than they actually are. Choosing inappropriate color scales can also distort perceptions, leading viewers to draw incorrect conclusions.

According to a 2024 report by the Pew Research Center, only 37% of Americans can correctly interpret a basic line graph (https://www.pewresearch.org/). That’s a sobering statistic. It highlights the urgent need for improved data literacy education, not just in schools, but also in the workplace and in the media.

A recent example I saw in AP News illustrated this perfectly. A graphic about global inflation used different scales for different countries, making it impossible to compare the magnitude of the problem across nations. While the data itself may have been accurate, the visualization was deeply misleading. To avoid falling victim to these kinds of manipulations, ask yourself the following questions when evaluating a data visualization:

• Who created the visualization, and what is their agenda?
• What data sources were used, and are they reliable?
• Are the axes labeled clearly and accurately?
• Are the scales appropriate, and are they consistent across different charts?
• Are there any visual cues that might be misleading or distorting the data?

Consider this case study: A news outlet published a map showing the distribution of COVID-19 cases across Fulton County, Georgia. The map used a color scale that made it appear as though neighborhoods near the Fulton County Courthouse were experiencing significantly higher rates of infection than other areas. However, upon closer inspection, it became clear that the scale was logarithmic, not linear. This meant that small differences in case numbers were exaggerated, creating a false impression of a localized outbreak. The outlet later issued a correction, but the damage was already done. Many residents in the affected neighborhoods were unnecessarily alarmed.

Some might argue that focusing on data visualizations is a distraction from the real issues. That we should focus on the underlying data and the analysis behind it. But I disagree. Visualizations are the primary way that most people encounter data. If we don’t equip ourselves with the skills to critically assess these visuals, we’re essentially abdicating our responsibility as informed citizens. The rise of trend forecasters means we need to stay informed.

The World Economic Forum’s 2025 Future of Jobs Report identified analytical thinking and innovation as top skills for the modern workforce (https://www.weforum.org/). Understanding data visualizations is a direct application of these skills, enabling professionals to make better decisions and communicate more effectively. For instance, businesses need to be ready to interpret data.

Stop passively consuming information. Start actively engaging with it. Develop your visual literacy skills, and demand better data visualizations from the media and from your colleagues. The future of informed decision-making depends on it.

Download Tableau Public today and create your first interactive dashboard. Don’t just read the news, visualize it.