Basic Rules for Creating Data Visualizations

This is the continuation of the transcript of DM Radio’s program titled “What You See Is What You ‘Get’ – How Data Visualization Conveys Insight,”

Another key is three clicks to any data. A big problem with dashboards is that people think they are just flat displays of information with maybe one level drilling. But the best dashboards are layered delivery systems that bring to the top the most important metrics and filter those by roles.

So usually just bringing up the dashboard allows the viewer to see at a glance exactly what's right, what's wrong, or what they need to work on in the next hour or day.  But if they need to get at any other information to assist in the network or other work, three clicks and they have got it.

That's not easy to do.  Flat dashboards are a lot easier to do.  You also have to balance density and sparseness.  Sparse graphics are easy to absorb. So just in a dashboard environment you can absorb the key information at a glance.

But, also, sparse dashboards may not have a lot of information, which may force you to click around if you are hunting for details.  Dense dashboards have a lot of information but may be difficult for a newcomer to find a way around and understand what's important to view and what's not.Also, you need to have set some standards for visualization techniques. This also makes it easier for users to glance at and understand what the data is trying to say.

So, for instance, you may associate a certain type of data -- say employee satisfaction data -- with a certain type of chart -- say spider chart.  So when a user sees a spider chart and a dashboard to report, they automatically know, “Oh, that's employee satisfaction data.” This accelerates comprehension. So those are just a few techniques that we recommend when designing effective visualizations.

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Eric Kavanagh:  Those are really good rules of the road. I guess maybe, Mark, I’ll turn it over to you. What are some good rules of the road that you’ve come across somewhere to what Wayne is talking about there?

Mark Flaherty:  Well, one of the challenges that I think a lot of people who are supposed to be making dashboard support in order to consume run across is that the look and feel of the visualization does actually matter. The challenge, I think, is because it's not always the case that the maker personally knows the data, or knows the business of that as a good design sense. But I think that it's something that shouldn't be overlooked.

Probably the best advice is for the person who’s building dashboards for others to receive feedback from the users. This way you know if it looks too cluttered. Take that feedback and actually try to make your information access to product look good as well.

Designing attractive and usable data visualizations involves balancing aesthetic appeal with functionality. One of the first pieces of advice is to focus on clarity. Your visualizations should communicate information quickly and effectively, minimizing any potential confusion. This can be achieved by choosing the right type of chart or graph that best represents your data. For instance, use bar charts for comparing quantities, line charts for trends over time, and pie charts for showing proportions. Ensure that your visualizations are not cluttered with excessive details, which can detract from the primary message.

Color selection is another crucial aspect. Colors should be used purposefully to highlight important data points and maintain visual harmony. Avoid using too many colors, as this can make the visualization look chaotic and harder to interpret. Stick to a consistent color scheme that aligns with your organization's branding or the theme of your presentation. Use contrasting colors to distinguish different data series but ensure they are accessible to color-blind viewers by incorporating patterns or shades that provide additional differentiation.

Labeling is essential for usability. Every axis, data point, and category should be clearly labeled so that viewers can easily understand the data being presented. Include a legend if necessary, but keep it straightforward and placed in a location where it doesn't obstruct the data. Tooltips or interactive elements can provide additional details without overcrowding the main visualization. Titles and captions should be concise yet descriptive, giving viewers immediate insight into what they are looking at.

Another important consideration is the context of the data. Visualizations should tell a story, guiding the viewer through the information in a logical sequence. This means highlighting key findings, trends, or anomalies that the data reveals. Provide context by including relevant benchmarks or historical data to make comparisons more meaningful. Annotations can be used to draw attention to significant data points or to explain spikes and dips in the data. This narrative approach helps in making the data more relatable and engaging.

Lastly, test your visualizations with a sample audience before finalizing them. Gather feedback to identify any areas of confusion or misinterpretation. This iterative process helps in refining the visualization to better meet the needs of the target audience. Additionally, consider the medium through which the visualization will be viewed. Visualizations designed for a printed report might need different adjustments compared to those intended for an interactive dashboard or a mobile device. Ensuring your visualizations are responsive and adaptable to different formats enhances their overall usability and impact.

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