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5 Best Practices for Data Visualization

As our world is increasingly filled with data, both enterprises and small businesses alike are looking for ways to analyze massive amounts of information. Data visualizations present data in a pictorial or graphical format so that business stakeholders can better understand complex data findings.

However, there are such things as good and bad data visualizations. In order to not distract or mislead viewers, here are five best practices to keep in mind so your data visualization is useful and clear.

1. Know your audience.

The initial and most crucial step in creating a great visualization is knowing what you want to say.

To ensure that your visualization is effective, start by asking who your audience is. What kind of questions do they care about, and what answers are your visualization delivering? What other issues does it inspire?

Keep in mind that that not all end-users will perceive the same information in the same way. For instance, a chief financial officer and a sales manager will have different ways of understanding profitability on a probability dashboard, so it’s important to make sure that you’re answering the question from the appropriate perspective.

2. Follow a methodology.

Define a process by which you obtain your design requirements, design visuals, gather your data, and release them. Only a perfectly defined methodology will ensure continuous quality improvement and consistent data visuals quality. On our end, we use UD³—Unilytics’ Dashboard Design and Development—an agile methodology that supports data preparation, integration, data governance, and dashboard development all at the same time.

3. Classify your dashboard.

There are three distinct types of dashboards: strategic/executive, analytical, and operational.

Let’s take look at each of these:

  • Strategic/Executive: This type of dashboard provides a high-level view of the question or inquiry line that is usually answered in a specific, routine way and presents KPIs in a minimally interactive way.

  • Analytical: This type of dashboard provides a highly interactive view and offers a distinct variety of investigative approaches to a specific central topic with limited corollary contextual views.

  • Operational: This type of dashboard is a regularly updated answer to the question or inquiry line, which frequently monitors all operational concerns in response to events on an ad-hoc basis.

4. Profile your data.

Different visual features work better with different data types. For instance, scatter plots function well with two bits of quantitative information, while line graphs and line diagrams are a good fit for ordinal information.

Here’s a brief look at each type of data.

  • Ordinal: Data that has a logical sequence (e.g. silver, gold, and bronze medals).

  • Categorical: Data that belongs in the same category (e.g. North America, Europe, and Asia).

  • Quantitative: Data that defines “how much” of something there is (e.g. $1 million in sales, 20° Celsius, 150 defects).

5. Design iteratively.

Don’t wait until all of your requirements are fulfilled. Visualization should be done in a way that is understandable to customers with the ability to deliver best services. In order to accomplish this, get a large chunk of requirements and start designing concept proofs and prototypes right away. Then, elicit feedback in an interactive setting and revise accordingly. Make sure that you are avoiding analysis paralysis that tends to happen to those who are familiar with old project management approaches.


Data visualization can create an engaged data-driven business culture that enables employees to access BI capabilities that drive the business forward. You can further encourage this engagement by sending metric-driven notifications and scheduled email reports. Remember, people usually respond better to visuals that tell a clear story than long tables or descriptions that needs interpretation.

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