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What Is Data Visualization?

To put it briefly, data visualization is analytics storytelling to inform action. A visual is one of the easiest ways to condense enormous amounts of information into a coherent picture that people can read. Think about how often you hear people say “an image is worth a thousand words.” A data visualization can be worth a thousand data points, or, with the growth of Big Data, that same visualization can be a summary of millions, billions or even more data points. That’s a lot of information that can be packaged as visuals that people are already familiar with, such as a bar chart in your business presentation or fitness tracking app.

To start seeing the benefits of data visualization in your work, it’s important to not only understand what data visualization is, but also why it allows teams to make data-driven decisions. If you get a few of the fundamentals, you’ll be a better reader and writer in the language of data.

Let’s look at a bit of the science first. As humans, a significant part of our brain is devoted to processing visual information. Data visualizations leverage this fact to more efficiently communicate trends and patterns. For example, it is easier to glean a trend in production adoption from a line chart than a table. The chart below allows you to quickly see that adoption is not only growing, but also growing at a slightly faster rate over time. The table presents the same information, but acceleration like that can be conveyed more effectively through a visual.

The visualization communicates the insight much more easily and quickly than the table. The chart title provides context to help the audience know what they are supposed to see. Then, the choice of an area chart makes it easy to see the change in magnitude over time. For a helpful guide on the basics of chart selection and the analysis each shows, check out the Periscope Data visualization flow chart.

To take this further, you can highlight specific styles of analysis or trends in the data depending on some of the design choices you make. For example, the table above had the same data points as the chart on the right, but the chart tells a much clearer story. To do so in your own work, you only need a few basic principles to build your own effective visuals.

Data visualization is the language that lets data speak

A well-designed chart can communicate a lot of business critical details with extreme efficiency. A poorly designed chart is the easiest way to misunderstand data and make the wrong decision. With just a few tips, you can avoid data visual pitfalls and become a data communication expert in no time.

To help break the world of data visualization into its first principles, data visualization researchers (yes there are data viz researchers) have created a “Grammar of Graphics.” This grammar works the same way that sentence and narrative structure helps writers create great novels. Chart designers rely on best practices to make sure visualizations preserve data-based insights that are easy to understand and act on. 

The grammar of data visualization can be broken into two types: fixed elements and data mappings. 

Fixed elements are used to give the chart reader a clear guide to the data in the chart and what they should focus on. Common fixed elements are:

  • X-axis: the values on the horizontal dimension, usually labeled and given some description
  • Y-axis: the values on the vertical dimension, usually labeled and given some description
  • Title: a clear description of either the general state of the chart or a specific finding

Data mappings allow the chart to come to life, creating a relationship between the values that you want to show and the ways that they can be read. For example, you can build a simple, information-rich segmented bar chart by mapping the number of leads to the length of the bar and the segment of leads to color. Seeing the trends and the total is easy because length is one of the most accurate ways that people can read magnitude.

For a more complete list of the data visualizer's palette, the items in the spectrum below are the tools that creators use to visualize data. They are ranked by how accurately readers can interpret the difference between the two data points. For example, people are great at reading the difference in length across a fixed axis, but it is harder to interpret the changes in length when the bottom of the bar varies. Take a look at the above chart again and notice how much easier it is to read the change in total leads for the bottom bars compared to the number of leads within the middle or top bars.

Whether you are reading or creating your next chart, keep these tools in mind. If you want to take your data visualization game to the next level, you can start to build more customized charts by identifying the insight or idea that you want people to focus on, then use these data visualization tools to showcase it to your audience. 

Expanding the narrative — dashboard level stories 

Once you’ve mastered creating your first chart or two, it’s time to start building up the visual narrative into a full paragraph or short story. In data visualization, this is often done through constructing dashboards. 

To help you get started, think about the way a sentence works to convey information. The pieces are arranged in a logical order to express as much information as possible. You would never just place words randomly. To communicate effectively, you must arrange the charts in an easy-to-recognize pattern that informs the listener of a subject, an action and the result of that action. 

Making these decisions properly is critical to making sure you leave your audience with the intended insight. Good design can be the difference between sounding like Mark Twain or sounding like Yoda. With these tools, you will be an all star in your next meeting, sharing ”a narrative that is easy to read” instead of “a read to that is narrative easy.”

At Periscope Data by Sisense, visualization is a crucial part of getting business value from your data. Our platform allows people to explore all of their information, find the right dataset for their question and instantly turn a table into a line chart, bar chart, pie chart, scatter plot, word cloud, cohort grid or another visual with one click. The visualization then appears directly in the editor where you can view the chart in the same environment as the query. The charts can be saved instantly to a dashboard where they become part of the bigger story around business critical insights. By translating those stories clearly, businesses can truly start to let data inform their decisions.

If you’re interested in learning more about how to make data visualizations that follow the rules of this new language, Periscope Data can help. We’ve created a free How to Chart Your Data Discoveries guide to help anyone communicate clearly with data.


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Christine Quan
Christine spends a lot of time thinking about data visualization theory and building tools to empower data teams. When she is not constructing SQL queries or building visualizations in R, Python, or Javascript, she can be found dissecting Taylor Swift lyrics through text analysis or analyzing emoji use in surveys.