Have you ever wondered what makes data useful and meaningful? In the beginning, when the only thing before you is a sprawling set of data, it feels simple and honest, but doesn’t exactly say much. However, the moment you analyze or visualize it, it evolves into information and starts conveying message. And it’s important to make sure that message reflects and aligns with what you’re trying to communicate. Why?

Data can be a powerful weapon to be wielded when discussing strategic business directions, work results or negotiating with your managers and teammates. After all, properly analyzed data is what you should be basing your crucial business decisions on.

So make sure your data visualization clearly illustrates what this data stands for and is easily understandable. Because the better you understand your data, the better you can communicate it to others. In this article, we’ll be laying out five rules that can help you maintain clarity in data visualization and communicate your insights to a wider audience.

1. Make sure your data answers a question

And to do that—you need to have specific questions as you analyze your data. That’s why the key to meaningful data visualization is the exploration phase—and making the right connections between the numbers and the real world.

Ask yourself what you want to know about your data. Then try to think of what people reading your graphs or charts will be looking for. The more specific you are, the better. Decide whether you prefer to explore and highlight the best (biggest, highest) or the worst (smallest, lowest) elements, compare specific data points, or maybe examine a trend over time.

If you have a complex data set—like a spreadsheet with multiple rows and columns—it might be difficult to decide what to look for, and where to look first. That’s what Nathan Yau, an expert in statistics and data visualization, calls data drowning in his book Data Points: Visualization That Means Something. To avoid it, you first need to learn to swim. Start at the shallow end and then go deeper.

Nathan Yau suggests asking a few questions first. Let’s make them into a checklist and go through them one by one:

• what data do you have?
• what do you want to know about your data?
• what visualization methods should you use?
• what do you see and does it make sense?

He recommends a very logical way of processing this data. First, choose the right data and only then find the right visualization method for it (like a bar chart, pie chart, treemap, line plot, or scatter plot). Only this way will you be able to find a match and make the answers to your questions clear.

#data visualization #product design #user experience