5 Essential Data Visualization Best Practices

We’re dealing with unprecedented amounts of data. With advancements in connected devices, data capture technology, increasingly sophisticated analytics, and more, understanding large data sets is more critical for decision-makers than it has even been — regardless of industry.

The big issue now isn’t necessarily the collection of data, it’s comprehending it in a meaningful way to help extract insights and drive decisions. If done correctly, data visualizations can clearly and efficiently present complex data, making it easier to grasp, identify new patterns, and understand data-driven insights. 

This post will break down what data visualization is, why it’s crucial, and introduce five essential data visualization best practices you should follow to ensure you’re able to get the most out of your data visualizations.

What is Data Visualization?

The term data visualization refers to the process of creating visual representations to communicate quantitative data sets. Depending on the data, these visualizations display critical information and insights through line and bar graphs, pie charts, scatter plots, heat maps, area maps, and much more. 

Real Life Use Case – CNN Magic Wall

CNN Magic Wall

It’s always helpful to define something with a real life use case. A prime example of a well done data visualization would be CNN’s Magic Wall, a solution created by InterKnowlogy. This is a XAML-based solution with integrated DirectX controls for mapping. CNN had very specific needs for up-time, customized data visualizations, touch interactions, telestrations, and responsiveness. The Magic Wall needed to be resilient to network conditions as well as handling massive amounts of constantly flowing, “real-time” analytics – all on primetime television in front of millions of viewers. 

In this case, the interactive data visualization was integral in covering the 2016 election results. You can read more about it here

Why is Data Visualization Useful?

Data, especially when presented with large amounts of it, is hard for the human brain to comprehend. Humans have difficulty understanding large numbers without relating to an analogy or abstraction, and data visualizations do precisely this. Big data is meaningless if the people or audience consuming it can’t understand it. Data visualizations help transform complex ideas and large numbers into bite-size pieces of information that are easier to understand.

Below we highlight five data visualization best practices you should adhere to to ensure you present your data in a meaningful way.

5 Essential Data Visualization Best Practices

1. Know your audience 

When creating data visualizations, it is vital to know who you are making it for and what questions they are trying to answer. If you don’t design your data visualization with your target audience in mind, it will fail to communicate information effectively. A data visualization should be compatible with the audience’s expertise and allow viewers to view and process data quickly by presenting information that they understand. Take into account how familiar the audience is with the data being presented and whether they’re likely to be able to comprehend the visualizations being presented. 

2. Use visuals that accurately represent your data 

With so many different visualizations to choose from when presenting your data, knowing which one to select is one of the most critical data visualization best practices. The right visualization will make the data easier to comprehend and ensure accuracy. To make the right choice, consider what type of data you need to convey and to whom it is being given. For example, if you’re comparing performance over time, a line graph would be the best way to present that data. Below we highlight some of the most common visualization types.

Line graph: A line graph shows changes over time and is the visualization of choice for identifying trends, making forecasts, and demonstrating growth. People commonly use line graphs to compare values and better understand relationships between value sets. 

Bar graph: A bar graph compares the values of different categories and aggregate sums for various entities, making them very useful for understanding the distribution between data and corresponding data sets’ values.

Scatter plots: To show how two variables correlate, use a scatter plot. Most often, scatter plots are used to compare values, show the distribution of data, and demonstrate the relationship between value sets. But be careful— scatter plots while useful can also inaccurately suggest a correlation.

Pie charts: A pie chart shows a total divided into categories by percentage and is excellent for showing the distribution of a limited whole like market share or real new users. People use pie charts to compare values and demonstrate the composition of a data set. 

Area map: An area map allows you to see which geographical areas are essential to your business by visualizing data as points of color. People generally use this visual to identify sales regions and follow up on marketing efforts.

3. Keep Data Clean

Another essential best practice for data visualization is to ensure that the data set you plan to use is accurately “cleaned” before converting it into a visualization. Data cleaning refers to the process of filtering out any anomalies, duplicates, and inconsistencies present in your data set. This process is critical to complete before using the data, as the presence of any inaccuracies can skew the interpretation of your data.

4. Use pre-attentive attributes

When creating data visualizations, it’s imperative to consider the most straightforward way for your audience to understand what your visualization is trying to tell them. Pre-attentive attributes can include color, size, shape, and position and can be used to strategically group or differentiate items, making visualizations easier to comprehend. For example, colors can help make patterns easily identifiable and give a qualitative sense by contrasting darker and lighter colors. However, it is essential to keep accessibility top of mind and ensure that whatever color palette you choose is colorblind-friendly. 

5. Keep your data organized and clear

Unfortunately, it is very easy to create misleading data visualizations; thus, it’s imperative to be mindful of this. Data visualizations should enable your audience to identify patterns clearly and accurately, and you should aim to avoid letting beliefs, biases, and mental models distort interpretation. Ensuring you stay consistent by correctly using currency symbols, percent signs, commas within numbers, and scientific notations can combat this. Additionally, here are three more things to keep in mind when creating data visualizations to ensure your visualizations aren’t misleading. 

Language: You have to consider meaning and context and choose industry and culture-specific terminology.

Labeling: Consider time and space. Your audience might be in different places, so be careful with cryptic terms and ambiguous labels. 

Scale: Scale can significantly impact your audience’s interpretation of the data. Check the numeric starting point, and do not break the axis (start at 0). 

Final Takeaways

By adhering to these data visualization best practices when creating data visualizations, you’ll be able to properly help your target audience identify trends and patterns hidden within your data sets. The power of data visualization lies in its inherent ability to make understanding data accessible and meaningful.

We hope these data visualization best practices can help you improve your visualizations and are happy to chat in more detail about creating your own data visualization solution. Contact us at curious@interknowlogy.com.

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