Data visualization literacy can provide an essential service by accelerating the speed and efficiency at which companies can make data-based decisions. As the need for general data capabilities has increased throughout departments, data visualization literacy has become more expected among non-technical roles. 

In this environment, companies must cultivate data visualization competency throughout the organization. Following are a few best practices for getting started on this today:

1. Adopt a Consistent Visual Language 

To start, it’s important to standardize on a consistent application of visual language. For example, a particular shape should always communicate a specific concept. This leads to greater communication clarity and, in turn, faster understanding of the information at hand. 

2. Digitize Metrics 

The more data is collected and organized; the more teams can visualize and collaborate around this data. Part of this step involves defining KPIs and metrics to consistently measure processes and events that may not seem easy to quantify. 

3. Understand the Users 

Another critical component of success is determining who the users are, whether they will use the same data, and how they plan to work with it. Additional considerations include using accessibility standards for fonts and colors so that users with disabilities can easily read the data. Understanding device preferences also come into play. For example, if users are mainly interacting with data via their mobile device, it’s best to start with a mobile-first approach and then extend it to a desktop. 

4. Understand the Business Context 

It’s also critical to consider how the data is presented visually and how the story is told within the overall business context. 

5. Set up a Feedback Loop 

Data analysis and visualization feedback loops can go a long way in improving employees’ skills in both creating and consuming visualizations. For example, at the requirements gathering level, the stakeholder can learn about the methods, provide input, and determine how to interpret the data. At that point, the analyst can adjust the complexity based on this initial feedback.

Head to TechTarget for more ways to foster greater data visualization literacy skills throughout your organization.