DATA ANALYTICS

Data Visualization Techniques and Best Practices to Tell Your Data’s Story

Julian Alvarado

Sr. Content Marketing Manager, Sigma

Al Shalloway, CEO of Net Objectives, describes the power of data visualization beautifully when he says, “Visualizations act as a campfire around which we gather to tell stories.” Visuals help people process information quickly and remember it better. Companies that take advantage of this fact are benefitting — a study of global businesses revealed that organizations using data visualization are leaders in revenue growth. This post shares the four major categories of data visualization techniques and ten best practices that will make your visualizations more effective.

Data visualization describes any effort to help people understand the significance of data by placing it in a visual context. It can take a variety of forms, from a word cloud to a scatterplot. Its main objective is to distill large datasets into visual graphics that provide a clear understanding of complex relationships within the data.

Evolution of Data Visualization

While humans have been using images to communicate for 35,000 years, the history of visuals in data analytics is much shorter. William Playfair (who is considered the inventor of line, bar, area, and pie charts) advocated for data visualization as early as 1786. Still, it was only about 40 years ago that they became widely-used — when Microsoft introduced visualization features in Excel.

With the rise of Big Data generated by websites, apps, mobile devices, wearables, and IoT, new platforms and tools were developed to handle the sheer volume and velocity of data. This development led to the next era of data visualization. Typically, these visualizations were based on static dashboards that allowed non-technical users to understand and leverage data and business intelligence to make more informed decisions to more effectively grow or pivot. Many of today’s BI tools continue to use static dashboards.

But relying on data teams with SQL skills to generate reports is inefficient. Decision-makers looking at visualizations want to do their own analyses to dig into the underlying data and answer follow-up questions. Now, thanks to the demand for self-service analytics, new cloud-native analytics tools like Sigma offer interactive dashboards that allow non-technical users to manipulate variables to dive deeper into the data. And the visuals update in real-time.

Data Visualization Techniques

There are many ways to visualize data, but we can group them into four major categories of data visualization techniques. The technique you choose will depend on the type of data you’re presenting.

Charts and graphs

Charts and graphs aren’t revolutionary, but they’ve stuck around for a reason: they’re effective. Charts and graphs are best used for univariate data and descriptive analytics. Common types of charts and graphs include line graphs, bar graphs, histograms, area charts, and pie charts.

Diagrams

When your data is hierarchical or multidimensional, a diagram will help you to demonstrate complex data relationships. For example, if you’re trying to get a holistic view of all your company’s interactions with a customer, you might use a network diagram, tree diagram, or block diagram.

Matrices and heatmaps

Matrices and heatmaps are also ideal for multidimensional data visualization. With a heatmap, gradients vary based on the strength of the correlation. With a pairwise scatterplot matrix, you can observe potential relationships or patterns in two-dimensions. Simply introduce color to visualize three dimensions.

Geographic maps

If you want to visualize geographic data by location, of course, a map is an excellent visualization technique. For example, you can use a map to compare your company’s sales across different regions. Keep in mind that when working with geographic maps, you’ll want to limit your data points to effectively tell the story.

It’s important to note that there’s no one best way to visualize data. Your data visualizations should give the viewer insight into the problem your data is aiming to solve.

10 Data Visualization Best Practices

Not all data visualizations are equally compelling. Following these ten best practices will help you better communicate visually.

Start with a clear goal

Begin with the business question your data analysis is answering and determine what you want to achieve with your visualization.

Know your audience

What a visualization leaves out is just as important as what it includes. Identify your audience and learn their level of awareness and experience. What information do they need? What do they already know?

Match the visualization to the data

As discussed above, different visualization techniques lend themselves to different types of data analysis. Selecting the right style can make or break the effectiveness of your visualization. A modern data visualization software solution like Sigma can help make this critical decision for you.

Provide context

Context is essential for understanding. Use comparisons when possible to provide your audience a deeper understanding of the data.

Keep it simple

Data is complex, and the job of your data visualization is to simplify it. By choosing the simplest way to present information, your audience will digest its meaning faster.

Use data visualization colors strategically

Color can help create associations by using common conventions such as orange for safety or flag colors to represent a country. Contrasting colors demonstrate comparison/contrast. And you can use color simply to make important information stand out.

Encourage engagement

Interactive visualizations are especially effective since they enable the user to engage more fully with the data. Allow viewers to manipulate your visualization, highlight key sections, remove what they don’t need, and keep what they do.

Keep it real-time

Data is only as valuable as it is up-to-date. For your visualization to provide actionable insights, it must be based on real-time data.

Gather feedback

When you design iteratively based on user feedback, you can improve your visualizations. Show your visualization to a segment of your audience, gather feedback, and don’t be afraid to make changes before releasing it to a broader audience.

Tell a story

Ultimately, your visualization is about the story the data tells. Consider what narrative you want to convey and what elements you can implement to tell that story more effectively.

Real-time data visualizations go a long way in helping decision-makers process information and gain actionable insights. By thinking through what you want to accomplish with your visualization and following best practices, you can dramatically improve your visuals’ effectiveness. And a modern data visualization tool is invaluable in helping you pick the right method for telling your data story.

Explore Sigma’s data visualization tools to see how our solution can help you quickly and easily create exceptional visuals.