Jul 26 2021
Data Analytics

3 Best Practices to Consider when Building a Data Dashboard

Visualization is more than just a pretty picture; your audience must understand the context of the information as well.

In an environment where politics and special interests often control the agenda, good data-based decision-making offers an objective way to allocate resources, respond to complaints and comments, and deliver services to constituents.

Federal IT teams can use visualization tools and dashboards to help both managers and end users pull meaning out of data previously locked away.

However, choosing the wrong statistics or graph style could lead to bad decisions. IT teams should ask for guidance to make data visualizations accurate and useful. Here are three best practices.

RELATED: New data analysis tools help agencies move fast amid crises.

1. Know Your Agency Data at a Deep Level

Data visualization brings together different data sources in the same image to show correlations. But without context, a raw data stream is open to misinterpretation and false conclusions.

For example, someone using COVID-19 vaccination data without documentation could easily come to incorrect c­onclusions about whether the data covered individual injections (even if they were for one person) or counted individuals needing two injections as one. Trouble could also arise if the ­vaccine data only included vaccinations administered by state programs and didn’t include multistate programs.

Knowing your data is the first step to avoiding errors in graphs. Low ­storage costs and inexpensive, fast database engines make it easy to save raw data from multiple sources.

Documenting the metadata that makes the raw data useful can prove more time- consuming. Still, that documentation is a critical first step to any data visualization and dashboard project. Providing context for graphics or the dashboard is equally important. 

Various tactics can put information into context for the viewer, including finding the minima/maxima, creating comparisons to previous data and overlaying related data.

2. Identify the Users of Agency Data 

Next, make sure you know your audience, whether it’s agency managers and other decision-makers or end users. You have a lot of options when using data visualization tools from vendors such as Microsoft, Oracle and IBM that can help pull together data from disparate databases and quickly build a wide variety of graphics to present the information.

Picking the right information and presenting it in the right way requires more than knowing the data, however. It requires knowing the audience that will use the visuals.

Does this audience use this data to make decisions, or simply to monitor? Do users dynamically adjust the visualization, or would a static presentation be more appropriate? Does the audience make short-term financial decisions or decide long-term strategy? Are the visuals for internal consumption, or will they be available for agency users to see on the public internet?

DIVE DEEPER: How AI tools allow agencies to analyze vast amounts of data.

Choosing graph types, data overlays and even color depends on knowing what type of decision-making will come out of the data visualization.

When a dashboard supports ­short- term operational decision-making — such as determining whether to e­scalate a potential problem or to approve a request — data visualization can speed the process and improve accuracy through the use of colors, graphical elements such as arrows, and tabular data on the same visualization. 

Although it’s tempting to be transparent and deliver the same information in the same format to agency end users as to the public, doing so can lead to ­significant misunderstandings. The ­general public won’t have the same co­ntext and subject-matter expertise as internal users, and will require a ­presentation in a different format with significant ­additional explanations.

EXPLORE: How can chief data officers help agencies? 

3. Use Accurate Stats When Presenting Data 

For a simple dashboard with time-based data on a graph, there are no statistics involved. If a visualization tries to show the relationship between ­different data sets, however, you’re in the world of statistics — and that’s not always a good place for amateurs.

It’s important to ensure that the math is right when you are using correlation, summaries, averaging and other similar statistical manipulations. A full-time data scientist can help ensure the accurate use of statistics. If a full-time data ­scientist is not available, some short-term help can ensure appropriate use.

A similar concern lies in the ­graphics themselves. Many business intelligence and visualization tools have a huge v­ariety of graphic options.

Selecting the right graph type and the right axis scales is not just a matter of aesthetics; it’s the best way to focus the viewer’s attention and deliver the right information as accurately as possible. 

For IT teams that have learned to graph using Excel, some training on choosing and presenting information in graphics is a requirement. A good place to start is with Edward Tufte’s classic book The Visual Display of Quantitative Information

They say a picture is worth a thousand words, but the right picture can be worth so much more. It can be deceptive and confusing — or it can explain, ­persuade, clarify and convince.

Dan Page/Theispot

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