Discover the Secret Data Visualization Tips That Will Level up Your Skills

All data tells a story, and a story is only as good as its narrator. Data visualizations identify trends, insights, and relationships within a sea of information. It represents numbers through effective visual cues like charts, shapes, and colors, simplifying its understanding.

Representing information is no easy task as it requires one to get their point across clearly, concisely, and accurately. With the many visualization options available today, presenting your story can be quite challenging. 

But don’t worry, we’ve gathered some of the most crucial tips for data visualization. Before diving into the data visualization tips, let's understand why effective visualizations are essential. 

Importance of Effective Data Visualization

Data visualizations have various uses, from college presentations to business meetings. In all situations, one thing that remains common is the need for effective communication. Your audience has a limited attention span that must be properly utilized. These visualizations must be tailored to capture their attention and communicate the key information within their time window.

A complex, ineffective diagram is too taxing on the mind and proves counter-productive, i.e., instead of being self-explanatory, readers have to figure it out step-by-step. It defeats its purpose if the visual leads to time wastage, confusion, or disengagement.

Moreover, businesses use visual dashboards to gather actionable business insights and make timely decisions. A messy visual can often misrepresent the information, leading organizations in the wrong direction. Such poor decision-making can adversely impact overall growth and business financials. This is why data visualization experts use certain practices to improve the overall outlook. These best practices for data visualization create a cleaner interface that is easy to read and communicates the message.

Useful Tips for Data Visualization

Here are ten crucial data visualization tips for creating a data visualization for maximum impact.

1. Know Your Audience

Your audience should influence your choice of visualization and its representation. Working in an organizational setting, you are most likely dealing with two kinds of people: technical leaders and business managers. Each of these expects different stats in their dashboard and has different requirements for the minute intricacies expected from the chart.

A technical leader would be interested in hard numbers regarding system performance or errors fixed in an application. The business leader will expect trends and insights to produce actionable results for revenue growth. 

The difference between the audience will determine the type of graph or chart, color scheme, and technical depth of your visualizations.

2. Pick the Right Visualization Type

There are several ways to visualize data, such as line charts, histograms, and spider plots. Selecting the right chart type ranks very high amongst the best practices for data visualization. You must choose the right narrator to tell your story since not all of these visuals fit every scenario. 

For example, a line chart is best suited for time-varying data since it displays high-level trends. Similarly, a bar plot is great for a comparative analysis between different elements.

The illustrations above demonstrate that the line chart neatly displays the sales trend over the year for the categories. It also provides a trend comparison for the three categories. For the same data, the barplot is much harder to read, forcing users to focus on individual stacks of bars rather than the overall trend.

Another important tip is to avoid using pie charts where possible. The design of the piechart can often make it difficult to distinguish between different data points if they are similar in magnitude. Additionally, it is easy for them to feel cluttered if there are too many categories.

Pie Charts also force users to switch between the pie slice (data magnitude) and the legend, making them challenging to read. You might at this point be interested what types of data visualizations are out there for you to implement so don't hesitate to check our extensive list.

3. Optimize Color Usage

Colors define the visual appeal of the visualization and help understand the data in certain cases. A good visualization must be easy on the eyes and look clean. This is why you must avoid using flashy colors that may distract the reader from the data.

Colors are also used as visual indicators for specific values. Our brains are used to associating specific colors with sentiments, e.g., green is good and red is bad, or blue is cold and red is hot. Such associations must be utilized carefully, as any misrepresentation can change the entire meaning of the illustration.

Also, choose a color palette covering different shades of the same color. The entire color spectrum provides a clean and sophisticated look and is easier on the eyes. It also prevents the risk of undesired sentiment association, as all colors are identical.

4. Pay Attention to Chart Placement

Optimal placement of elements is a critical part of User-Interface (UI) design. Human beings have a tendency to read interfaces in a certain order. Either going top to bottom or left to right, our mind starts forming a map of all the information it consumes.

The same principles apply when reading a dashboard containing multiple graphs and charts. The visual elements of the dash view must form a linear progression, and the information must transition from one element to another. 

For example, when displaying system metrics, it only makes sense to place system performance metrics, network bandwidth, and user traffic side-by-side. User traffic will denote unexpected increased activity, causing system performance degradation on the metrics chart. Network metrics will further complement this scenario to see if internet disruption affected the delayed responses.

If linking charts like these are placed far apart, it will confuse the readers, forcing the audience to divert their attention towards all other elements in search of answers. Optimal chart placement also increases the intuitiveness as users do not have to move back and forth between far-apart elements.

5. Keep a Consistent Scale

Your dashboard will most likely consist of multiple graphs and charts, and many will be compared to the others. It is important to ensure that all these charts are dimensionally consistent, i.e., they follow the same aspect ratio.

Having a consistent layout has two key benefits, firstly, it provides a clean interface that is easy to understand and get used to and provides equal focus to all elements. Secondly, having similar dimensions makes comparing the two visuals point-by-point easy.

Additionally, stretching charts can create a misleading outlook. For example, a line chart stretched vertically might give the impression of an increasing trend, even if that is not the case.

Moreover, consistent dimensions should also apply to the axis scales. If a chart is to be displayed on a logarithmic scale, then all related charts should follow the same pattern. This will make it easier to compare the trends.

6. Keep All Text Simple and Readable

Visualizations are more than just lines and bars. They include titles, data labels, axes labels, and sometimes brief text explanations or disclaimers. Text can be vital to the overall graph but must be used wisely as it can easily become a distraction.

Ensure all necessary labels, such as titles and axes, are clear and to the point. The labels must be concise and mention all necessary details, such as the unit of measurement for the chart. The text must also be readable, so avoid using fancy font or unnecessary alignments and animations.

Moreover, the chart space should skillfully and efficiently represent data instead of forcing users to seek additional explanations. Hence, avoid large chunks of text on the graph interface. If text must be placed, ensure the font is plain, and the text color is not flashy such that it distracts the reader.

7. Avoid Distracting Elements

As a rule of thumb, anything that doesn't add value to the graph or provide additional context should be treated as a distraction. You will be tempted to be creative and make your visual stand out. However, tread carefully, as overdoing creativity can adversely affect the chart's readability.

Some common creativity errors are adding patterns or designs to bars in bar plots, unnecessary images or shapes, and bold colors and images in the background. Not only are these elements distracting, but they also look unprofessional, especially if used all at once.

Logos and colors can be great for visual appeal but must be carefully placed. Distractions divert user attention away from the main information, and they lose all interest.

8. Highlight Key Insights and Patterns

Displaying multiple trends or categories on the same plot creates quite a rush in the chart area. Sometimes, these data points are unavoidable, such as in automated dashboards displaying real-time system metrics.

But what good are these lines if they introduce chaos rather than clarity? Dashboard users are interested in patterns that are unusual or provide actionable insights. As the visual creator, it is your responsibility to highlight these key insights and patterns so that they stand out from the rest.

This can be achieved by using different colors, line types, weights, and transparency. These elements will help block redundant data and focus on the desired pattern.

9. Keep It Simple

As a data analyst, you will be bombarded with information left and right. There is so much to show and such little time and even more little space. It is natural to want to create the ultimate source of truth and cram every bit of information where possible. However, all that information can be found in the data itself. You must learn to differentiate between great charts and useless charts and pick the most necessary ones.

The whole point of creating data visualizations is to enhance strategic decision-making with the help of simple yet informative data visuals. 

For example, imagine you have the sales data for a retail store in multiple regions. The data is granular per day, and the overall data length is four years. One way is to throw all this information on a single chart and hope the end user can make sense of it. 

The better approach is to simplify things and drill down the information to an understandable level. Group the information yearly to get the annual sales trend or aggregate store-wise sales to understand which stores are underperforming. 

In short, engineer data to extract insights that provide value to the reader while simplifying the interface.

10. Complete the Story

Data insights are often derived from multiple sources, and the result is achieved after analyzing various intermediate trends. In this case, the conclusion is just a part of the overall picture and is an incomplete representation. An incomplete visualization limits users' understanding of the situation and leaves them with many questions.

For example, if the sales of the optical disk drive have dropped, the first question that comes to mind is ‘Why?’. The increasing demand for other storage devices, such as USBs, answers this question. This can further be explained by a comparative analysis of the performance comparison of the two devices. This is how you paint the entire canvas.

Connecting dots this way will require stacking up multiple trends and charts in a single view. It may seem counter-productive and cumbersome, but presenting complete information is vital. All the trends involved must be refined using the key points discussed in this article to ensure the presentation is clean and focused.

Ready to Take Your Data Visualization to the Next Level With Our Tips Now?

A data visual communicates bulk information in a simple, clean, and easy-to-read manner. A good visualization is self-explanatory and provides an overall picture of the situation without mentioning details or specifics.

Data visualizations have multiple elements contributing to their look, feel, and overall effectiveness. These include the colors, layout, selection of charts, and text labels and descriptions. Following the best practices for data visualization helps the visual stand out and provide complete information without inviting additional questions.

If you’re interested in how to create a data visualization, Vizzu has the perfect solution for you. Vizzu offers a suite of tools for building professional data visualizations and animations.

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