You know what they say: a good data visualization technique answers all the essential questions, while a great one answers questions you didn't even know you had.
Think about it: weather forecasts, medical equipment, vehicles, house utilities were all improved over the years, and data visualization techniques are what contributed to it.
In today's article, we'll dive deep into the visualization methods used by companies to create data reports. If you pick the correct visualization type you can successfully deliver critical information, making the decision-making process more effective.
It goes without saying, but the data visualization type isn’t always interchangeable. The type of data and your audience should influence your choice of data visualization technique. Depending on these two, you should choose the data visualization method. Typically, these methods include charts, infographics, graphs, or reports.
Your approach to data visualization mainly depends on your goals and audience. To present the information engagingly and make viewers stick around, you should go with an infographic.
Meanwhile, if your audience is a board of busy people, you should use a pie or a bar chart – this way, they'll be able to understand the information quickly. With that said, you should get to know your audience first, then decide on the data visualization technique.
At the same time, the goals you set dictate what type of elements you'll be adding to your KPIs report. Also, you should use a color scheme that matches the topic of your data report. For instance, if you're building a data report on carbon emission, it would be effective to use red shades: your reader will feel alarmed.
Furthermore, you should be consistent with the type of your selected color scheme. Utilize contrasting colors to present different or opposing elements: typically, positive values are in a green color and negative ones are in red.
Among all the data visualization techniques, using charts seems to be the most common type. There are different chart visualization sub-categories, each one of them having a specific use.
When it comes to representing large data you need to have a few critical aspects in mind. It would be best if you also create a decision tree analysis.
In general, you should display the data elements in the hierarchy, breakdown the derived results, and provide the method used to obtain the information. Keep in mind that appealing visualizations are as worth as its credibility.
Designers use a few visualization tools to create a presentation for their company or build a road map. The most common visualization tools used are Photoshop or Adobe Illustrator but there are a number of free alternatives to Photoshop too. But, to create a map or a presentation report using one of these design tools, you still need aggregated data.
Luckily, there are many tools for data visualization out there, allowing anyone with little-to-no design knowledge to build a visual data chart.
Whatagraph here is one of the best examples: it’s an effective visualization tool that helps you create multi-source data reports using pre-made templates, so you don't have to do anything from scratch. Here's one of our Facebook data report examples:
The Whatagraph platform provides multiple integration options, including most of the major analytic tools. So, if you're already using some of the tools, building an aesthetically pleasing data report will be a piece of cake. You’ll be able to choose between line, bar, pie charts, and other types of visualizations.
In case the software you're using isn't available on our platform, you can upload a CSV or an Excel file. Best of it, you can use the white-label those reports function to make a custom data report with brand colors and logos, which shows you put extra care into your work.
Published on Apr 05, 2020
WRITTEN BYGintaras Baltusevičius
Gintaras is an experienced marketing professional who is always eager to explore the most up-to-date issues in data marketing. He is always in search of new and unexplored angles to share with his readers.
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