Explanatory vs. Exploratory — Which Data Visualization Is Right for Your Organization?

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In this special guest feature, Chad Reid, VP of marketing and communications at Jotform, argues that data visualization remains one of the best tools you can use to highlight relevant information for stakeholders, but to use it to its full potential, it is key to understand the difference between explanatory versus exploratory data analysis and know when to use each. He provides three strategies for better data visualization. Chad is a frequent contributor to various tech and business publications — and an absolute wizard with a Vitamix.

The human brain is powerful, but it has limits — one being the ability to multitask effectively. According to Cleveland Clinic research, a scant 2.5% of the population can focus on more than one responsibility at a time. As a result, people faced with huge amounts of data struggle to process it well.

This can create difficulty for tech leaders trying to use raw data points to justify budget expansions or push a client deal over the finish line. Too much unstructured data won’t make sense to these stakeholders — but data visualization can help.

The Value of Data Visualization Techniques

In basic terms, data visualization involves turning data into graphics. Pie charts, graphs, maps, and tables are all examples of data visualization.

 When data is structured this way, it makes more sense and becomes significantly more digestible. People can see context and patterns while noticing trends. They can also make sense of seemingly dissimilar data points and mountains of information much more easily.

When people understand what they’re seeing, they also become less hesitant to make decisions based on that information. In one study, 64% of participants were ready to make binding choices after seeing data in a mapped-out format.

Data visualization remains one of the best tools you can use to highlight relevant information for stakeholders. To ensure that you use it to its fullest potential, it’s key to understand the difference between explanatory versus exploratory data analysis and know when to use each.

Explanatory vs. Exploratory Data

When planning your data visualization — whether it’s a chart, an infographic, a table, or some other form — you need to determine if it’s designed to explain or explore the information you’re presenting.

While digging through massive data stores, you’re looking for interesting tidbits that might have connections. You likely have some raw data. though it might not yet make a lot of sense. Consider an archeological dig that brings up hundreds of pieces of glazed ceramics. Are these pieces from one item or several? And how was the item used? The only way to answer those questions is to determine the context, or the “how” and “why.” The same is true of your many pieces of data.

If you hope to bring your data to life, you’ll need to create an explanatory data visualization. You’ve already done the exploring — with your explanatory data visualization, you can offer actionable insights based on what you’ve collected.

Ready to get started? Here are a few tips for better data visualization.

1. Add text and labels to visuals.

Label your charts. Use colorful keys. Make annotations. Do what it takes to help your viewers grasp what they’re seeing. If you only plot out numbers, you aren’t providing any context or explanation. Don’t assume that everyone will draw the correct conclusions without help.

2. Opt for a clutter-free experience.

User experience (UX) matters when it comes to data visualization. Avoid letting your charts and tables get too messy or over-complicated. Ideally, you’ll want to temper the number of complex designs. Follow a simple adage: “When in doubt, take it out.” Unless something’s absolutely necessary, it doesn’t belong in your visual.

3. Ensure your visuals support your conclusions.

You’ve made a conclusion based on your exploratory data research. Does your visualization buoy that conclusion and help others understand it? You can’t assume anything. Review your graphic to ensure it tells the right story. If it doesn’t, it could be time to choose a different format.

For example, infographics might look pretty, but they’re rarely laser-focused on one specific point. Unless you’re short on time, play around with a few data visualization types to see which best communicates your data discoveries.

The next time you plan to share data, consider putting it into a digestible and understandable explanatory visualization format. You’ll improve your chances of educating your audience while helping others see why the data matters and what it means rather than just a bunch of numbers.

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