Storytelling school

Five Data Storytelling Best Practices to Consider Right Now

This is part of Solutions Review’s Premium Content Series, a collection of reviews written by industry experts in maturing software categories. In this submission, Toucan Toco co-founder and CEO Charles Miglietti offers five data storytelling best practices to consider for your next project.

Premium SR ContentData storytelling is the ability to effectively communicate information from a set of data using stories and visualizations. By putting information into context, you can motivate your audience to take action. Data storytelling consists of three components:

  1. Data: The basis of your data story is accurate and comprehensive data analysis. Analyzing data using descriptive, diagnostic, and prescriptive analytics can allow an organization to see and understand the full picture.
  2. Narrative: Using a story, also known as a storyboard, you can communicate insights from data, the context around it, and the actions you recommend and want to inspire your audience.
  3. Visualizations: It is possible to communicate your story in a clear and memorable way through visual representations of your data and your story. In addition to tables and graphs, diagrams, images and videos can also be used.

An organization can use data storytelling internally (for example, to communicate the need for product improvements based on user data) or externally (for example, to convince potential customers to buy your product). In this article, we’ll cover the top 5 data storytelling best practices that are making a real difference in the daily lives of our 100+ Fortune 500 clients.

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Context is everything

A business decision is driven by context when communicating complexities. At Toucan, we believe data storytelling is incomplete without context. You need user context to create your visualizations and story. Similarly, the user needs context in the analysis to understand the data story that has been built for them.

No need to be too technical with actors who are not concerned with the method but rather with the results. There’s an added level of distraction that will cause them to feel less confident about trusting your opinion. This is especially harmful if you have customer relationships!

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Think about who, what and how when considering the context of your data story:

Who:

  • Who receives this type of information and how do they interpret it?
  • What kind of trust have you established?
  • How much detail or brevity do they prefer?

The answers to these questions will help you determine how comfortable you are with the target audience and what tone will be used to convey the main message.

What:

What is the message or action you want to convey? Effective storytelling should prompt action and answer the “so what?” question that arises when presenting data information. To be a successful storyteller, you need to guide your audience to take action and plan their next steps.

How:

What is the most effective way to communicate the data story? Is it better to use a bar chart or a line chart? Do you think animation would be helpful? What effect will your color choices have on your audience? How difficult is your chart to understand, is it too complex or too simple?

Keep visualizations simple

It is an art to present data in a clear and non-misleading manner. When checking for brevity, the litmus test is whether the viewer can understand the main points without being explained.

To do it right, there are two elements.

Choose the right graph to represent the data.

Despite being one of the most popular charts in the world, the Camembert stinks! Easy adoption means easy and quick understanding. For example, which color occupies the largest piece?

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Spending more than a second thinking about it is already a waste of time. If you are comparing more than 2 items, you should use a bar chart instead of a pie chart.

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Much clearer, right?

Next we have 3D graphics. It is all the rage among designers as it provides a different perspective and increases the appeal of a first glance viewing. But is it really useful?

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Can you see the exact size of the yellow rectangle? His value?

Adding dimension to your charts doesn’t necessarily mean they’re fancy. There are many use cases where a data analyst has created completely custom charts with numbers, colors, and dimensions flying all over the place, but their internal clients have struggled to understand the data. Always choose clarity over sophistication, it will take you far.

Placement of the axis

The axis must be set to zero when using a bar graph to demonstrate a difference. By setting the axis to zero, you can display the full width of the bar. For line graphs, since the goal is to display changes over time rather than absolute size, it is common practice not to set the axis to zero. If you are illustrating multiple trends on a line chart, the axis should be consistent.

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In 2016, the White House released a graph showing an increase in high school graduation rates. There are several issues with the bar chart above. To start with, the axis must start at zero, because removing the bottom half of the image would result in a loss of proportion. Also, it is never good to illustrate the elements of a chart. From the viewer’s perspective, five pounds equals 75% and sixteen pounds equals 82%.

Use simple fonts

Incorporating your corporate brand into your dashboard, or having a personal preference as to which is best, is a waste of time because it doesn’t take into account how its font affects its adaptability. Here is an example using the MonoSpace font. Would you be able to tell in less than a second how many orders of magnitude there are?

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The result is that the numbers are not aligned correctly. Through alignment, the user should be able to understand the order of magnitude of a number even before reading the actual value of the number. Each character in the MonoSpace font occupies a different space. We can ensure that the numbers are always aligned by using the table font, which is often present in development software for coding, which makes delivering a message quick and easy.

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It’s about getting a message across as quickly and efficiently as possible to a non-technical user. Data analysts may not consider this level of detail necessary, but it often goes beyond what they would expect.

Ensure team alignment

Enterprise-sized companies are likely to have team members located across the globe, with one having a different background, so a different understanding of KPIs is likely. Nearly 60% of a meeting is spent explaining what the numbers mean, leaving only 30% for discussing facts and less than 10% for making a decision. Having a good data communicator align a team early on allows them to spend 60% of their meeting time discussing their decision, which translates into better decisions and improves the bottom line. This can easily be avoided by including context and having consistency throughout the analysis.

Adding a glossary to your dashboards at your fingertips will affect how your team works. IT will help members understand the meaning of all teams used in specific visualizations. There may be even more context incorporated into Toucan with headings, descriptions, tips, and actions.

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In case that is not enough, it would be useful to have a place in the analytics application to ask questions and resolve doubts before the meeting even begins. That’s why in Toucan we have the integrated comments section or the possibility to export graphs directly to Slack, by email or as PDF with annotations.

The next thing to improve alignment is consistency. You can deploy up to three times faster if you use the same font, structure and location for all your buttons or filters as a data analyst, BI manager or data interpreter, rather than having to redesign your design Everytime.

Our UX is based on analytics that support what works and what doesn’t, and we’re now putting it in the hands of companies that have invested millions in BI tools, trying to translate them into tools Communication. In a Design First approach, the design is created from precise studies, as well as continuous adjustments, always keeping in mind what works best and allows for faster understanding.

Guide your users

It is inefficient to have contextual information that does not lead to actionable insights and effective decision making. You can encourage users to take action by embedding a call to action* in your data so they can see how their decision affects their bottom line. It’s kind of rewarding. This is part of Guided Analytics, which expands to empower users of all technical levels and backgrounds to not only understand but also create insights.

Obviously, some decisions take longer to bear fruit, but you’ve already won if you’ve guided your users to take action.

Toucan Toco’s app has a clear call to action*, which also adapts to the user’s data and the action they need to take.

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Here’s what it should look like, with the button still placed in the upper right corner.

Ultimately, data storytelling is about helping users of any technical level understand the meaning of data in the easiest way possible. The visualizations you create should not only convey the story behind the data quickly and effectively, but also inspire users to make informed decisions that will directly impact the organization. It might sound like a big question, but by following the 5 steps above, you can be sure to create the ultimate data storytelling platform that will empower users across the enterprise and dramatically reduce the time it takes to to analyse.

Charles Miglietti
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