Name: Communicating Clearly with Data Visualizations
Uploaded: 2023-03-13T20:18:59Z
Duration: 4 min 42 s
Description: In this video, you will learn how to identify the best practices for communicating effectively with data visualizations. Data in a vacuum is nearly useless until it is used to tell an effective story that addresses a business need.

How data is presented can be as important as how it is collected. These videos explore foundational strategies and best practices to ensure that your data visualizations are meaningful and accurate.

Data Visualization

In this video, you will learn how to identify the best practices for communicating effectively with data visualizations. Data in a vacuum is nearly useless until it is used to tell an effective story that addresses a business need.

Communicating Clearly with Data Visualizations

Data visualization is a form of storytelling. Data in a vacuum is nearly useless, until it is used to tell an effective story that addresses a business need. Then it can be used to create a coherent narrative that answers strategic and operational questions, that in turn, provide insight to drive business decisions and the appropriate actions for carrying them out.

At its heart, telling a story using data visualization requires using good data.This requires understanding that good data comes down to group selection and comparison. Say a group of people see an ad. How would an organization rate the effectiveness of that ad? If the group consisted of those already interested in the product before seeing the ad, this would be bad data and its story would provide little insight. There's bias involved. However, if the people were randomly selected, the resulting data might be considered good and provide actionable information.

Next, consider whether the purpose of the data visualization is declarative or exploratory. Declarative visualizations are used to show what you know. These are the typical line and bar graphs, pie charts, and scatter plots that present the story of a specific data set in an easy-to-digest format. Declarative visualization is commonly used to present demographics, a breakdown of departmental spending, or survey results, for example.

Exploratory visualization, on the other hand, is used to try to learn something through visualizing, either to confirm a hypothesis or to try to get the data to reveal some pattern, connection, or other useful information. For example, if a sales manager simply presents a bar chart of quarterly sales information to the board, that would be declarative visualization.

However, if the chart shows a lag in sales and the board questions why there is a lag, further exploration of the data may be required. If the sales manager has an idea as to why there's a lag, she may be able to use exploratory visualization to either confirm or disprove it. Perhaps she suspects there's an increase in customer shopping via their smart devices during certain times of the year and the organization's existing marketing programs aren't set up to take advantage of that. Using different types of charts and changing the variables over multiple iterations, she may be able to confirm that hypothesis. However, if she has no idea what she's looking for, she may decide to use more open-ended data visualization, which typically includes and examines more data than a visualization based on a hypothesis.

As with telling any story, you should always consider who the audience is, whether they're decision makers, technical staff, or the public. Each of these audiences may require a different visual format. Regardless of format, though, a ratio of 80% graphics and 20% text is generally ideal. Keeping the presentation simple is a good rule. For everyday data visualizations, bar and line charts are often sufficiently clear. To show a decline in annual growth in healthcare spending, compared to other areas, for instance, a simple line chart conveys the message with more clarity than a chart detailing 20 years of historical data.

A chart with too many measures or even too many colors can be hard to read, so it's often best to break down complex points into multiple, smaller, more concise charts to avoid confusing viewers. When there is a lot of data to illustrate, using small multiples of the same basic graphic to display different slices of a dataset can make things clearer. For these large data sets you could also consider adding interactivity. Highlighting someone's performance in comparison to everyone else's is an example of using interactivity. Regardless of where that person's performance falls in relation to others, the highlighted results standout.

Another type of interactivity is filtering. This helps when working with multiple levels of data. Data visualization can help you tell your data story with clarity and understanding. It can let your audience make decisions based on what your data truly shows about your customers, your business operations, and your company's performance.

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