How to Combine Data for Bespoke Visualizations



In the modern business world, data is everywhere. Data can be used to connect more efficiently with consumers, provide better transparency, and garner clearer insights into big picture ideas. But data can be messy and confusing, especially when presented in esoteric formats. Offering up raw data for anyone to attempt to translate is ultimately going to lead to misunderstandings and misinterpretations.

Data on its own has a hard time telling a story or making a point, which is where data visualizations can translate numbers into meaningful metrics. An accurate, easy to read graph or chart is an incredibly effective way of presenting complex data in a way that is easily digested and understood, especially since about 65 percent of people are visual learners.

But, of course, not all data are created equal, and neither are all data visualizations. Most of us are familiar with some standard forms of data visualization, such as pie charts, line graphs, scatterplots, and timelines, among others. And these visualizations are highly impactful ways of presenting data most of the time. Occasionally though, a more customized approach is necessary to truly display your data in a way that tells the story you want it to tell.
Fortunately, it is easier than ever to create combined novel data visualizations, especially when using data visualization software instead of building them yourself in Excel. But, in order to create a combined data visualization, there are a few things to keep in mind first:

Make sure a combined visualization is the right fit

Sometimes a more traditional data visualization is really all you need. Anything else might seem cluttered or be confusing for consumers who are trying to understand fairly simple and straightforward information.

One example of a good opportunity to incorporate a combined data visualization is when you are creating a complex timeline. Timelines can be combined with line graphs and even multi-axis charts and other annotating options to provide and more detailed and precise overview of the information. Think about pulling data from your project management tool into your data visualization tool to better understand how your internal resources are being used over time, and plot the number of projects or tasks completed over the timeline. Combining these data sets means you’ll need a project management tool that allows you to export your data or that integrates directly with your data visualization tool.

Another popular and useful pairing is a scatter plot overlaid with a regression line, which is handy for showing relationships and correlations in an easy-to-read way. Similarly, column, line, and pie graphs can be combined in one to allow lots of information to be displayed in a concise way. This is especially useful when analyzing trends and comparing different types of data.

Creating a combined data visualization can even be as simple as adding an additional y axis, which allows you to show a comparison of two different trends with different metrics.

Look for notable insights

As with any good data visualization project, you need to constantly be on the lookout for useful new insights when creating your combined data visualizations. Data on its own can be a little tough to chew; putting your data into a more accessible visual format can provide you with a clearer understanding of what your data really means.
When deciding on the best visualization option, think about what you already know (or think you know) about the data before you. What are some key findings you expect to see? What results do you feel fairly confident you can predict? Look for those things first, but remember that there are stories within your data you didn’t even realize you should be looking for.

Combined data visualizations allow you to look at your data in brand new ways and from multiple new angles. Take advantage of this and see if you can gather any new insights from displaying the data you already have in innovative ways. Even if you try something as simple as synchronizing some charts that you wouldn’t normally associate with one another, combining data into unconventional, more niche visualizations is a great potential opportunity to see your data in a way you didn’t see it before. You can also try looking into national or global data from publicly available sources to help you benchmark your internal data.

Highlight your key findings

Once your insights have been identified, you want to be sure you are using your combined visualizations to support your story. Again, data can be tricky and a little dry on its own. That’s why visualizations are so important for its presentation. But it isn’t always easy to optimize your visualizations in a way that is conducive to the story you want it to tell.

Knowing the type of visualization you really need is an important factor in this process. During the creating and analysis phase, it is valuable to create many different visualizations and experiment with various options. And while these options are good tools for gaining a better understanding of your raw data, they don’t all need to be shared with your customers, clients, or internal stakeholders.

Instead, make sure you really understand how your visualization will be perceived. Know your audience. Comprehend your data. Look at your visualization as an outsider to ensure that you chose the right options based on the findings you want to highlight, and get an outside opinion on what their takeaways are before you commit.

Provide context always

Context is key when it comes to data visualizations. While this is true with any visualization, it is especially pertinent to combined visualizations since they do offer so much information in such a small space.

When combining data, it is easy for certain details and explanations to get a little lost in the fray. Providing plenty of labels, titles, and other annotations might seem a little clunky, but this is a necessary step when working with combined data to guarantee that the visualization is easily understood and the potentially complex data can be interpreted accurately.