Glossary term

Data Visualization

What Is Data Visualization? 

They say a picture is worth a thousand words, and while the saying typically references a piece of art, the same can be said of data. Our eyes are immediately drawn to colors and patterns, and by transforming datasets into something we can see, we’re easily able to interpret it and identify trends and outliers.

Data visualization allows storytelling through data. Rather than looking at a spreadsheet of numbers, employees (even those in nontechnical, data-agnostic roles) can understand, analyze, and communicate its impacts. It’s essential for enterprises who want to make data easier to understand and gain buy-in on major initiatives from executives, stakeholders, and decision-makers who are less in the weeds.

Data visualization is meant to be easy-to-read and interactive, often presented in a graph, chart, or map format. With an interactive map, for example, users can click on a location and discover top electricity sources in the US by area. This allows users to not only see the data, but to connect with and explore it as well.

Why Is Data Visualization Important?

The biggest benefit that data visualization has is quickly, effectively, and simply communicating complex data and information. In a business context, this might mean that a business analyst is able to succinctly present their findings to management without needing to go into too much detail. With data visualization, there’s no specialized training needed, and the insights are accessible to anyone in the organization.

When put into practice, the advanced analytics derived from data visualization enable enterprises to notice emerging trends and patterns that might have otherwise gone unnoticed in a raw dataset. Management can quickly react to this information, gather insights, and make faster business decisions.

Data visualization can also help with audience engagement and improve an organization’s bottom line. Take Google Analytics, for example, which allows users to see their data in the forms of charts and graphs. It’s also interactive, allowing users to determine what type of data they want to see in their visualization. By showing users information that’s helpful to them, your brand builds more credibility and authority. 

Examples & Types of Data Visualization

When you think of data visualization, you’re probably imagining simple bar graphs or pie charts. While these are the most commonly used techniques, there are even more visualization methods used to present data in a simple, effective manner.

Here are a few common formats and types.

Bar charts or graphs

These are used to compare data along two axes (x and y). One axis represents numerical units, while the other stands for the variable being measured. They can also be used to display trends and data over a specific period when bars are placed along an axis that represents time.

Pie charts

These are the second most common data visualization technique. They are circles divided into several “slices” with every slice representing the percentage it contributes to the whole. Pie charts are used to visualize the percentages of a whole.

Line charts or graphs

A line chart uses different points connected to form a trend that typically either goes up or down, though is not always linear. They are used to display changing data over time.

Scatter plots

Also known as scattergrams, scatter plots are used to analyze the relationship between variables. They display two variables, one on the x-axis, and the other on the y-axis, of the same data point.

Tree maps

They use shapes, normally rectangles, with each rectangle representing a data point while the size corresponds to a specific value. Tree maps are used when multiple categories are present to compare different datasets to the whole.

Other Variations

How to Approach Data Visualization

Data visualization is an art, but the main goal is to provide valuable information and insights. Fortunately, there are simple guidelines you can follow to make your data visualization technique appealing and captivating.

Define your purpose

Start by defining the purpose of your data visualization project. Decide what your priorities are, what you want to convey, and what your audience expects from your data visualization. It’s important to have this information defined from the onset to ensure you’re presenting the most valuable information in your final product.

Speak to a specific audience

The purpose of your data visualization is to communicate valuable insights to a specific audience. Therefore, consider the customer’s personality and determine what context you can present around your visualization to help your audience understand it better. Also, consider challenges they might be currently facing and what hurdles they must overcome to achieve their top-of-mind goals.

Keep it simple

Data visualization should be clear and easy to digest. This means getting rid of unnecessary data or information while presenting key insights clearly and concisely. The idea is to make the presentation straightforward so that viewers can quickly understand it and use it to help them make the right decisions.

Data visualizations work well to establish baselines. Future data can be compared at a glance to make anomalies stand out.

Provide context

Data trends and patterns are best demonstrated in the context of metrics and high-level goals. By presenting your data visualizations in context, stakeholders and decision-makers can understand the story you’re telling and draw meaningful conclusions. A few ways to show context including strategic use of color, clearly indicating data periods, and comparing data to goals or metrics.

 

Choosing The Right Data Visualization Tool

An effective data visualization tool does more than simply display a specific data set. It creates a narrative, providing a clear and creative way to communicate complex data to a broad audience and gain the favor of your organization’s leadership. The end goal is to ensure your data visualization is interpreted correctly and turned into actionable insights that have a lasting business impact.

Want to get started with your data visualization process? Download RapidMiner for free today and experience the full potential of the data science lifecycle for your business.

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