5 Data Visualization Best Practices

Analyzing your data and turning it into value is a crucial factor for business growth and development. An important instrument to help you with this is data visualization. Whether you need to prepare a report, build a sales funnel, or analyze your marketing metrics, presenting your data in a visual format is a great way to work with it more efficiently. When your data is visualized, it’s easier to track changes, spot new trends, interpret metrics, and present your findings to your clients or colleagues. 

From simple graphs to complex self-updating dashboards, data visualizations are crucial for enabling data-driven decision-making. In this article, we’ll explore some data visualization best practices that can help you make the most of your data. 

Why you should use data visualization best practices

Of course, there’s no one-size-fits-all solution, but still, your organization can benefit from the tools and approaches used by other companies for efficient data management. Even if some data visualization best practices might be less relevant for you than others, it’s still a great place to start.

Using data visualization best practices can help you tackle some of the most common data-related challenges facing many companies, like streamlining data analysis processes and making data actionable.

Top 5 data visualization best practices

Data visualizations can be very different – from a simple bar chart showing just 2-3 parameters to a comprehensive self-updating dashboard that allows you to monitor business performance in near-real time. Here are some data visualization best practices that will help you present your data meaningfully.

• Always start with the audience and define the goal first

It might be tempting to dive into data right away and start with deciding how to better visualize one or another process or report. After all, if you have amazing marketing campaign results, shouldn’t you just go straight to choosing between the pie chart and bar chart? It might sound counterintuitive, but this is not the best place to start. 

Always begin with defining your audience and the goal you want to achieve. These crucial parameters allow you to understand how to better present your data. For example, if the visualization is meant for a marketing team, you can provide more details and include a variety of metrics, since your audience is already familiar with them. However, if you need to present the same data to the C-suite executives, you should select more high-level results and explain how they fit into the company’s overall strategy. Select the data that your audience might need to better understand the situation and make informed decisions. Your visualization should be structured around the audience and its end goal. Ask yourself why they need to see your graph or dashboard and use this as a starting point. You can also address this question directly to the people who will be using your visualization – which metrics they want to see and what correlations might be important for them. 

In addition to this, it makes sense to ask for feedback when your graph or dashboard is ready. Your audience may provide valuable ideas regarding what else to include or how to make the visualization even more useful.

• Use simple graphs for stand-alone activities, build dashboards for complex processes

One of the best practices of data visualization is to select the graphical representation that matches the complexity of your data. If you need to visualize a relatively simple process or activity, there’s no need to overcomplicate things. You can use a pie chart or bar chart, and that will be enough to present your data in a clear and concise way. 

However, if you need to visualize several interconnected processes or explore multiple parameters at once, it’s better to opt for a more complex format – a dashboard. This type of visual representation can include many different elements, like scorecards, various graphs, tables, maps, and so on. When you bring all this together and make all the elements interconnected, you’ll get a comprehensive overview of your activities.

Here’s an example of a dashboard built for tracking sales leads. It can be used to analyze where leads come from, assess their quality, monitor changes over time, and make predictions. 

See the interactive version of this sales leads dashboard in PowerBI.

• Make your visualizations interactive and automate data flows

Sometimes it makes perfect sense to prepare a one-time visualization for a yearly report or important meeting with a client, and then forget about visualizing data till the next event. However, to fully benefit from presenting data in a visual format, it’s better to use it for day-to-day data analysis and collaboration. 

Updating your graphs or dashboards manually on a daily basis would be tedious and inefficient. Luckily, you can use a data integration solution to fully automate your data flows. 

For example, here’s a dashboard for tracking ad performance. This dashboard is self-updating, which means you don’t need to refresh the numbers manually. It was created with the help of Coupler.io – a data analytics and automation platform that combines a data integration solution and data analytics services for advanced data management. 

Coupler.io automatically pulls data from LinkedIn Ads, Facebook Ads, and other sources and transfers it to a spreadsheet. The latter is connected to this dashboard in Looker Studio. Every time Coupler.io exports fresh data from the sources, the metrics in the dashboard change according to the new information. We’ll explore this in more detail in the next section, How to implement the best practices in data visualization – an example.

The dashboard is interactive – you can filter data by product, campaign, channel, period, and more. Interactivity is an important factor that turns this visualization into a powerful tool for ongoing monitoring and analysis, not just a mere demonstration. With such a dashboard, you can easily assess your current results and make informed decisions to improve them.

See the interactive version of the ad performance dashboard in Looker Studio. If you are working in marketing and looking for more efficient ways to manage your data, check our guide on marketing data visualization.

• Don’t forget about the basic design rules and data visualization color best practices 

Speaking about the best practices of data visualization, it’s impossible not to mention the basic rules for using colors and other visual elements. Here are some best practices that will help you make your visualizations clear and compelling:

– Contrasts. Use contrasting colors to compare different categories. If you need to differentiate between the maximum/minimum values within one data type, you can use gradients or shades of the same color. Colors should make it easy for the audience to identify what is where. If you select a particular color for a specific product or metric, it’s better to stick to it, otherwise your visualization may be misleading (see more misleading data visualization examples).

– Patterns. To make your visualization easier to understand in a glance, you can apply various patterns to your bar and pie charts. For example, a dotted pattern or even texture can make the elements of your chart more contrasting and easier to read.

– Palette. Try to use as few colors as possible. Less important data can be shown as white or gray, and for more meaningful metrics, you can select vivid colors. It’s better to stick to just several main colors. If you use too many, your visualization may look too colorful and difficult to work with.

– Proportion. When you visualize numbers, for example, in a bar chart, make sure that the bar length corresponds with the number this bar represents. It may sound obvious, but still, many people fail to do this and end up with graphs where a bar representing 200 K can be twice as long as a bar depicting a 70 K value. This doesn’t make sense mathematically, and such visualization will be misleading.

– Labeling. Don’t forget to label your graphs or dashboards properly. Use clear titles to show what is where and always accompany numbers with units so that your audience can easily understand whether each number represents thousands of dollars, kilometers, milliseconds, or something else.

• Keep it minimalistic – focus on data, not design

Whatever format you choose, one of the best practices in data visualization is keeping it simple and avoiding visual clutter. However tempted you might be to create beautiful eye-catching infographics, it’s necessary to stay focused on your data. The form shouldn’t overshadow the essence, and data should remain the main character in your visual story.

Use visual elements, such as charts or icons, only if they make the numbers easier to understand. If you feel that a particular data set should be visualized as a table without any additional graphic elements – don’t hesitate to use this format. Design elements are of secondary importance, using them only makes sense when this serves the purpose of presenting your data as clearly as possible.

How to implement the best practices in data visualization – an example

Now, let’s see how to use some of the ​​best practices in data visualization that we’ve discussed above. In this example, we’ll explore how to create a self-updating interactive dashboard with automated data flows, like this one:

We’ll do this with the help of Coupler.io, a comprehensive data analytics and automation platform. In particular, Coupler.io provides a useful data integration solution that allows you to export data automatically from 30+ sources. This data can be imported to BigQuery, Excel, or Google Sheets. After this, you can build a funnel in Looker Studio or create self-updating graphs and dashboards directly in a spreadsheet. Alternatively, you can connect your destination file or database to Looker Studio or Power BI and build more complex self-updating visualizations there. Coupler.io will keep refreshing data in your dashboard according to the specified schedule.

In addition to the data integration solution, Coupler.io also provides data analytics services – this can be helpful if you want to build an advanced custom dashboard that will help turn your data into action points.

To create a self-updating interactive dashboard yourself, you’ll need to complete the following steps.

• Sign up for Coupler.io and add a new importer.
• Select the data source and the destination. In this example, we’ll export data from Google Ads to Excel, but you can also extract information from dozens of other data sources and import it into Google Sheets or BigQuery.

• Connect your Google Analytics account, select what data to extract, and specify your preferences.

• Once this is done, you can add another data source to the same importer. Coupler.io will export data from several sources into one destination file. You can connect as many sources as you need.
• Then, connect your destination account and select where to import your data.
• When this is ready, you can schedule automatic updates. Coupler.io will keep refreshing your Google Ads data in the selected spreadsheet. You can choose the update interval (from every 15 min to every month), as well as select your time zone and specify other preferences. The Automatic data refresh function allows you to set a flexible schedule that fits your goals and processes.

• Save your settings and run the importer. Here’s an example of the exported Google Ads campaign data in Excel.

• Create a new tab in the spreadsheet, connect it to the sheet with raw data, and get your information ready for visualization. Decide which parameters to include in your graph or dashboard, calculate custom metrics, etc.
• When your data is ready, you can build a dashboard directly in a spreadsheet. As an alternative, you can connect the Excel file with the exported data to Power BI and build a dashboard there. If you import data to Google Sheets or BigQuery, you can use Looker Studio to visualize your data.
• If you want to use your dashboard as a professional data analysis tool that helps you make informed decisions on a daily basis, you can request help from Coupler.io’s data analytics team. See some examples of the pro-level dashboards they have created.

Which best practices of data visualization should you use?

Some best practices of data visualization are universal, like focusing on the audience and using colors wisely. Others may be more or less relevant depending on your circumstances. For example, automating data flows and building self-updating dashboards might be less useful for small companies that don’t accumulate much data. At the same time, an interactive visualization like this can become an invaluable tool for organizations that need to track and analyze many interconnected processes or activities.
In general, the best practices in data visualization that we’ve covered in this article are easy to implement, so you can try it out and see how this works for your organization and your specific goals. We hope this article was useful to you!