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Charting the Course: How to Choose the Perfect Visualization for your Data in Five Steps

When done right, data visualizations tell a story.

Data visualizations have a beginning, a middle, and an end. There might even be characters, like a hero or a villain! But to tell a great story, you need to set the perfect scene. And setting the perfect scene with data starts with choosing the best visualization for your needs and goals. Luckily, when it comes to choices, there’s no shortage of visualizations to choose from.

Which specific visualization you use depends on several key factors, including the amount of data you’re evaluating, what you need to understand from the data, and who will be interacting with the visualization. Depending on how you answer these questions, you may opt to use a Pivot Table, a Bar chart, a Line chart, or a Heatmap, among many others.

Since it’s crucial that your visualization tells your data story in a way that’s clear and engaging, we’ve developed a set of questions that you can use to determine exactly which one works best for you.

Five Steps to Choose the Right Visualization for Your Data

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Step One: Identify what story are you trying to tell with your data

Do you need your data to demonstrate progress over time? Compare categories? Show a trend or pattern? Maybe your data needs to show the relationship between variables and outcomes. Whatever story you need your data to tell, it’s important to identify your goals up front, and think about how you’re going to craft your narrative.

A good story is engaging and interesting, and your data story should be no different! If you’re using data to track progress – like the success of a recently launched product – you can start by revealing the predicted success of the product, before highlighting its current performance, and concluding by predicting how the product will perform moving forward.

By clearly defining a beginning, a middle, and an end, you’ll take people on a journey and keep them engaged with your story, and ultimately, your data.

Step Two: Identify how much data you’re working with

When it comes to visualizing data, size matters. If you’re working with a vast amount of information, there are certain charts that will work well, like a Pivot Table, and certain charts that won’t work at all, like a pie chart. Similarly, if you’re working with a smaller universe of data, a Donut chart might be the perfect choice, while a scatter plot might be less effective.

As a rule, you should use a chart that presents your data in an uncluttered way. Crowded charts are confusing, so it’s important to consider whether and how your data overlaps, flows, and shifts. When you’re working with larger amounts of data, it’s particularly important to keep things clean and clear.


Step Three: Identity what type of data you’re working with

This is where things get a little more technical. You’ll need to establish whether you’re working with categorical, quantitative, continuous, or ordinal data, to name a few. For example, categorical data (like age or education level) lends itself well to visualizations like Pie or Donut charts, as they both clearly show identifiable, discrete chunks of data.

If you need to visualize continuous data (like temperature change over time), you’re better off using a Line chart, as continuous data – by definition – can’t be visualized in discrete sections or chunks.

Once you understand the nature of your data, you’ll be able to eliminate the chart types that won’t work for you, and have a better understanding of what your real choices are.

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Cars Workbook

Step Four: Identify who will interact with your visualization

Presenting data to business professionals during a boardroom meeting is a different task than presenting data to the general public. And presenting business data to an internal sales team may look different when that same data is presented to an internal finance team.

Presumably, people who have interacted with your data before will be able to understand more sophisticated visualizations in less time, whereas people who are seeing it for the first time will require a more basic visualization to more easily appreciate the story you’re trying to tell.

The bottom line is that your data is only as important as someone understands it to be.

Your audience matters, and if you don’t take them into consideration when building your visualization, your story won’t be as effective as it should be.

Step Five: Ask yourself how people will use the visualization

This question is one that’s often overlooked when choosing a visualization, but it’s a key component in getting your point across and building an effective story with your data. When it comes to how people will interact with your visualization, you should be asking yourself questions like “will someone view this on their own time, or will it be presented to them?” or “will this data be viewed at-a-glance, or in-depth?” and “does the viewer need the ability to manipulate the visualization?”

Without the answers to these questions, you can’t predict which type of chart will be the most effective. For example, a Pie chart might be simple enough for someone to view and understand without an accompanying presentation, but a Pivot Table might require additional context to be useful.

Astrato: for an ideal creation experience

Ultimately, even beautifully presented data isn’t effective if people can’t interact with it intuitively and with relative ease.

Astrato for the Snowflake Data Cloud

With Astrato, creating beautiful, effective data visualizations is comfortable and fun. From Tables to Pie Charts – and every viz in between – this cloud-native, next-generation BI solution empowers anyone to tell their story and take meaningful action driven by a deeper understanding of data. Whether you’re a skilled developer who wants to use a sophisticated yet streamlined tool, or a total dataviz novice, Astrato’s low code solution provides a seamless creation experience that helps you create the most compelling visualizations possible.

Start creating today!