7 Data visualization best practices you simply can’t ignore
If you don’t want your dashboards to be just another piece of beauty with no information then stick around and learn about 7 data visualization best practices stated by the data gurus.
Dashboards have become a part of our lives. Data scientists are continually striving to build engaging and meaningful visuals to simplify numerical and quantitative information. But unfortunately, a large number of visuals stand out as bad data visualization examples.
“Clutter and confusion are failures of design, not attributes of information” believes Edward Tufte, a pioneer in the field of data visualization.
According to him, a large number of data churned around us every day presents data scientists with the opportunity for excellent storytelling. But, how to utilize the opportunity?
In the words of another data scientist, Ben Shneiderman, “Information visualization is a powerful interactive strategy for exploring data, especially when combined with statistical methods.”
Without further delay, let’s start with the data visualization best practices that we have mastered over a period, and we strictly adhere to while designing our dashboards.
7 data visualization best practices to never ignore!
1. Knowing your audience
There’s no one size fits all solution in dashboard designing. A dashboard designed for your manager will be too confusing for your customers.
So, as the first step, ask yourself the following questions:
- For whom are you preparing the dashboard?
- What is the purpose?
- Which probable queries need solutions?
- What are you trying to communicate?
Once you have the clarity to the above questions, you will know the dashboard’s purpose and have a brief idea about your audience base. This information will be useful to you while analyzing the data.
2. Knowing the data
“Above all else show the data,” quotes Edward R. Tufte in his book The Visual Display of Quantitative Information
You can present your data most effectively only when you know it thoroughly, starting from where it has been collected to the cleansing process it has gone through. The best way to know your data is by spending time with it, going through the different fields, and cleansing it yourself.
Once you have a hang of the data type, you will precisely know the chart type you need to use. For instance, a line chart is best for trend analysis or time series, bar charts are best for ranking, and column charts for comparisons.
The knowledge of data will also help you prioritize the content, decide on the charts’ placement, and select the story’s flow most engagingly.
3. Maintaining the data-ink ratio
3D effects, background images, too many borders, and gridlines cause distraction in visuals and divert viewers’ attention from the data.
To understand the amount of distraction present in a dashboard, Tuft has introduced the data-ink ratio concept. It’s the proportion of ink used to represent the actual information to the total ink used in the dashboard.
Maximize the data-ink ratio wherever the focus needs to be on the data. And remove non-data-ink (like background image) from wherever possible. Elements like branding and navigation are crucial in visualization, but according to dashboard visualization best practices, these should take a back seat to the data.
Tuft also suggests removing redundant data. For instance, the Bookmarks feature in Power BI helps you navigate between two views of the same dataset. You can use it to declutter your dashboard with repetitive data.
4. Striving for consistency
In Shneiderman’s book, Designing the User Interface: Strategies for Effective Human-Computer Interaction, “strive for consistency” is interface design’s one of the eight golden rules.
Human eyes seek patterns, and random patterns do not make sense to us. Using standard colors, menus, designs, fonts, call-to-action when representing similar situations or similar parameters is a vital data visualization strategy.
Consistency and predictive layouts help users familiarize themselves with the dashboard more conveniently, and they do not need to waste time understanding each visual’s perspective.
5. Following the standard design principles
Visuals speak louder than words, but only when the picture is well-defined.
Designing visuals that quickens decision making and minimizes users’ cognitive load can be achieved by following the standard designing principles.
KISS, i.e., Keep It Simple Stupid, is an example of a designing principle. It merely means that you should design your visuals for non-experts; thus, keep your visuals simple and avoid distracting elements, like an unnecessary use of images or complicated graphs.
The use of colors is another crucial fact in dashboard visualization best practices. It should be done judiciously. For instance, if you want to highlight certain variables, use colors for them, and keep the rest grey. Use contrast colors and sizes wherever comparison needs to be drawn.
Randomly using bright colors when not required will distract your viewers and complicate their decision-making process.
6. Keeping dashboards easy to understand
Data visualization should be clear and easily understandable. So, while designing dashboards, developers should keep in mind the following:
- Use of simple language
The audience knowledge plays a crucial role in selecting the language you use in the dashboard. You have to consider the industry when using specific technical terms and jargon.
- Standard scaling
We often see dashboards that do not start at zero – this, at times, is done to highlight the data. But, it’s an incorrect data visualization practice, and it manipulates the viewer’s perception of the data.
Clear labeling is essential when designing a dashboard. Missing out labels might give your audience the impression that you want to hide the data – and this is not desirable!
- Placement of charts
To tell a story, we need to represent more than one chart on a page. As Tufte says, “It is not how much information there is, but rather how effectively it is arranged,” the placement plays a vital role in your dashboard designing.
7. Less is more
In 1956, George Miller had given the magic number 7. He said that an adult human mind’s short-term memory could store seven pieces of information on average.
So, to make the charts impactful, dechunk your data. Divide the data into different graphs and space them out on different pages.
Presenting the audience with too much information will clutter their minds and not fulfill your desired objectives.
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The ultimate objective of dashboard data visualization best practices is to break down complex information. Using simple visuals for the target audience to understand and make their decisions. The above-mentioned data visualization best practices will help you to communicate effectively through your data.