Data Visualization: 5 Key Best Practices for Better Analytics

Case Study: Digital Marketing

In today’s world, digital agencies are inundated with massive amounts of data, making it challenging to extract information from them. This is where “the art of visualization” comes in as a critical tool to present insights clearly and in an engaging way. Data visualization is essential in simplifying complex information and transforming it into a visual format that is easier to understand, helping businesses improve their decision-making processes through clear reports and presentations.

However, there is a disparity among visualizations, with the distinction between a decent and an exceptional one frequently being linked to specific characteristics. When it comes to data visualization, Alberto Cairo is a highly regarded expert. As a University of Miami professor specializing in visualization, Cairo has pinpointed five essential attributes that all outstanding visualizations should embody: accuracy, practicality, aesthetics, discernment, and illumination.

Complexities of Data Visualization

Data visualization has become crucial to decision-making processes in various industries and departments, including digital marketing. Nonetheless, data visualization can come with some challenges. One of these is the limited attention span of the audience. People are bombarded with information from various sources, and it takes time to capture their attention and convey a message effectively. Effective data visualization can simplify complex information, making it easier for the audience to understand.

The problem is that only some have the necessary skills to interpret complex data, which is where data visualization comes in. However, creating compelling visuals is complex, and many businesses need help designing clear, concise, and accurate visualizations. 


Let’s apply Cairo’s five principles of effective visualizations to the number of Form Submissions in a given time period, as reported by Google Analytics, and see how each principle helps us to gradually get a better version, to ultimately have a great visualization.

Let’s start with the following visual:

Bar chart of form submissions separated by quarters used as a data visualization bad example.

1. Truthful

The first principle of effective data visualization is truthfulness. The visual graphic should be based on accurate data. It’s essential to base visual graphics on reliable and thoroughly researched data to avoid any misleading visual techniques, such as distorted scales, selective data filtering, or cherry-picking data points. Misleading visuals can lead to disastrous consequences. A truthful visualization should be honest and transparent, clearly and accurately representing the data.

For the visualization above, form submissions have been summarized by quarters and grouped by each of three different marketing channels. Although it is possible to distinguish an increase in form submissions coming from Paid Media, disaggregating this channel could show exactly which “sub-channels” composing it are responsible for this increase–thus leading to a more ‘truthful’ visualization. Additionally, looking at data by month instead of quarters could give a clearer picture of the trends while staying away from sampling noise if we were to show data by week, for example. In this way, we avoid misleading the users and show them an honest picture, closer to reality, by getting the data “right”.

Improved version of a bar chart separated by months as a more effective data visualization example

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2. Functional

The second principle is functionality. A visualization should be designed with a clear purpose in mind. It should convey your message effectively, and your audience should be able to understand it with ease. For example, a specific type of visualization could make it challenging for some users to interpret the information while others don’t. As Cairo says, “choosing graphic forms to encode information is not mainly a matter of personal taste but can be based on rational thinking.”

In our case, having a stacked bar chart in the first place would not be appropriate for this time series data because it is difficult to easily detect trends for each channel across time. One alternative is unstacking the bar chart and having five bars for each month. However, a better way for users to interpret the data and detect trends, in this case, would be a line chart:

Data visualization format based on a line chart of form submissions

As one last housekeeping item: according to the previous principle of Truthfulness, we may want to scale the data such that the chart starts at zero for the Y-axis; otherwise, the apparent increase and decrease for Paid Search and Direct, respectively, would appear larger than they actually are:

Line chart with points indicating the beginning and end of each month

A functional visualization should be intuitive and efficient, allowing the audience to quickly grasp the meaning and insights behind the data. It should also be designed with the audience in mind, considering their needs and expectations.

3. Beautiful

The third principle is all about looks – beauty. A visually appealing chart can capture your audience’s attention and make them more likely to engage with the information presented. Here, the style, the color, and even the font are also situational and contextual factors, depending on the audience, the context in which the visualization is presented, and so on.

In the case of a line chart, there are several opportunities to improve its visual appeal. Just to name a few:

  • A clean and readable font should be used for the axis labels, title, and legend.
  • Another color palette departing from default options could be more appealing to the eye
  • Discarding the Y-axis label, and improving those for the X-axis simplifies the visualization, and leverages information that is already available in the title
Colorful line chart used as an example of the best way to visualize data

As Cairo says: “what matters isn’t if the objects of our creation are beautiful or not per se, but if they are experienced as beautiful by as many people as possible” this is what should guide our decisions for this principle. However, keep in mind that beauty should never come at the expense of functionality or truthfulness.

4. Insightful

The fourth principle is insightfulness. A great visualization should not only present the data clearly, but it should also provide insights that go beyond the numbers.

Let’s go back to the line chart. While it may display the data, it might not reveal any trends, patterns, or insights. To improve this, it is essential to highlight key points or trends in the data. This can be achieved by identifying seasonal patterns, relationships between the channels, or additional context through annotations.

In our case, a piece of information was added to indicate that levels of spend increased in Q4 for the Paid Search & Video channels, thus clarifying that the associated increase in form submissions that can be seen most likely was not due to seasonal patterns or other factors.

Line chart with all the data visualization best practices applied.

5. Enlightening

The final quality of a great visualization is its ability to be enlightening, providing the audience with new or unexpected insights into the data. This requires going beyond the obvious and exploring different angles and perspectives to reveal hidden insights. According to Cairo, this is accomplished by complying with the other four principles, while keeping in mind that “great visualizations change people’s minds for the better.”

Enlightening visualizations require thoughtful analysis, effective communication, and creative storytelling. By applying these principles to the line chart, we can create an insightful and compelling visualization that reveals hidden insights and engages the audience’s curiosity.


Ready to take your data visualization to the next level? Our B2B Marketing Services are the solution you need. Check out our analytics page to learn more about how we can help you turn your data into actionable insights. Our team of experts can help you make sense of your data and drive better decision-making. So contact us today and see how we can help you visualize success!

Carlos Chunga

Analytics Reporting Manager | Economist, deeply passionate about the fields of Data Science, Statistics, and Computer Science. Loves to learn something new every day and interested in finding analytics tools and solutions to complex problems that companies may have.


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