By: Felix Pan
“EGGS YOLKS ARE SAFE TO EAT! CAN NUTRITION ADVICE BE TRUSTED?”
Do you ever see headlines like these? Quick, controversial, and tantalizing! How do these headlines ensnare our attention for hours on end? To help us answer this question, we need to understand informatics: the use of data, knowledge, and information to perform actions such as decision making and problem solving. In dietetics, nutrition informatics professionals collect nutrition-related information and use it to make decisions, create content (like the headline), and find evidence-based strategies to address personal and public health challenges. Recently, the Nutrition Informatics Dietetic Practice Group (NI DPG) hosted a webinar “Infographics: A Mental Model To Increase Engagement,” in which Alli Torban, an Information Design Consultant, discussed the importance of identifying the targeted audience for graphics, factors that affect audience engagement, and methods to engage different audiences with data visualization.
A large part of how we engage with information is how we see it — a field called data visualization. As IBM describes it, “Data visualization is the representation of data through common graphics such as charts, plots, infographics, and even animations.” While these graphics are the most common, other forms can also be used. When there is a lot of data, what is the best format to present it? How can we show all the data cohesively? After all, more information means better decisions… right? Before deciding on a graphical approach, it’s important to check-in and ask “who is the information for?”
Factors Affecting Engagement
People engage differently with data. While some may immerse themselves in graphs and charts, others may simply skim through them. Several factors that can affect one’s level of engagement include: purpose of engagement, interest, focus, time and graphic’s aesthetics. But one factor always diminishes the viewer’s experience: friction, or in other words, the elements of the graphic that interrupt engaging and understanding its content. Have you ever looked at a chart once, twice, or even more and found it difficult to understand? Let’s take a look at an example.
What makes this graphic difficult to read?
- Many lines are intersecting and it is difficult to follow a single line through the graph.
- Sales items are not grouped by any category.
- Similar colors can intersect (e.g. yellow and darker yellow) making it easy to confuse one line for another.
- The legend is inside of the graph, making it hard to see points.
- A lack of gridlines makes it difficult to compare the number of units sold of a particular item with the reference point on the y-axis.
These factors can make the graph harder to read and interpret. As a result, there is more friction to engaging with the graphic and readers are less likely to continue reading. When visualizing data, it is important to consider how friction will affect the audiences’ level of interest and understanding.
Identifying The Target
Before beginning the data visualization process, it is important to recognize the target audience. Questions such as “What kind of audience do you want to attract and interact with?” or “Do you think our audience needs more incentive to interact with this chart?” can be used to identify the consumer. By defining the readers early on, the data visualization process can be more deliberate and allow the consideration of factors such as the level of detail that needs to be included.
Types of Readers
From reading a magazine at the laundromat to reviewing monthly reports about a company, people engage with information for different reasons. These reasons affect their levels of interest and focus. This criteria can be used to divide people into four types of readers:
While interest refers to the relevancy of the topic to the individual, focus describes the effort the individual is willing to exert to learn about the topic’s materials. High-interest/high-focus and low-interest/low-focus groups can be easy to picture: those specifically searching for information and those who look past the information, respectively; however, recognizing the other groups’ characteristics can be more elusive.
High-interest/low-focus individuals can vary in their occupation, but share a similar theme: they manage lots of information. They have limited time to review large amounts of information but are invested enough to seek the knowledge. It’s important for them to consider “What’s in it for me?” For example, a CEO may just want the main message of a chart and is uninterested in the details of data collection and analysis. The data needs to be pertinent and adequately placed to bring their eyes to the center of the diagram. Because they are interested in the information, the visual does not need to be flashy. For example, this line graph clearly shows the trend of sales for three products over twelve months. Graphs like this bar graph should be avoided for this group because it takes longer to read and interpret the trend.
On the other hand, low-interest/high-focus readers may include individuals who are leisure readers where they may not be searching for anything specific. It can be hard for them to find the right topic, but they are hooked once they do! For example, this can be a student searching through the news to find a topic of interest for a school project. When building a graphic, it’s important to make it welcoming with a strong visual introduction. For example, Megan Lautz’s (MS, RD, LD, TSAC-F) website has an effective visual introduction that clearly states how she can help and below it, an easy to understand breakdown of information.
Diagrams, such as this pie chart, that introduce the reader to the information and invite them to learn more are best.
Data visualization is a vast field, with many different methods to develop graphics, purposes and audiences. To become immersed, each type of reader requires a tailored visual format based on their level of interest and focus. The process of designing successful, attractive graphics starts with first identifying the target audiences and then creating visual content which meets the needs of those audiences. Identifying who the audience is, what level of information needs to be conveyed, and how the data will be presented can help improve the efficiency and outcomes of the data visualization process. With this in mind, can you guess who the headline at the beginning of this article is trying to reach? Does it engage you?