Data Visualization Trends for Millennials
T.J. DeGroat | 5 minute read | August 14, 2018 
The accepted age range for millennials has been tough to pin down, but by all definitions, the older members of the group are firmly in their 30s and have begun taking over the reins in business. Despite this change, the way we create data visualizations of essential data remains, for the most part, exactly the same.
Yet, whether the purpose is marketing or delivering vital business information to company employees, data visualization continues to be of the utmost importance in the workplace. Perhaps even more so to millennials, as they value the principle of “Show, Don’t Tell,” choosing to use visuals rather than pure text to share information. In one study, a whopping 85 percent of students noted that creating meaningful presentations was a big part of their job descriptions.
The reality is that millennials are rather different from older generations. They have vastly different needs, habits, and interests. As such, in order to capture their attention, the same tactics cannot be reused. Instead, the way we present data must be revolutionized to become not only information-packed, but also visually exciting and appealing to this audience.
Data Visualization Trends
Beauty Is in the Eye of the Beholder
One thing that many companies overlook when dealing with millennials is their distinct view of aesthetics. The look of a set of data visualizations may not appear important, but to the average millennial, aesthetics rank pretty high in terms of their priorities when it comes to data visualizations.
A good way to emphasize this idea is through the use of sleek and simple designs that streamline information while staying on trend with the visual tastes of your average millennial. This not only creates data visualizations that are appealing to the target audience, it also improves the scannability of the data.
Scannability is an important concept as millennials greatly prioritize speed and ease of reading. By allowing a particular visualization of data to become much more scannable, viewers can avoid wasting time trying to pick out important information. This allows them to understand a great deal at once, all in a single glance.
(Image source)
With data as complex as the example above, however, there is no one single way to create this visualization in a sleek yet straightforward manner. Instead, Mason Currey, the creator of this data visualization, has chosen to use a distinct color palette that allows viewers to easily distinguish one point of data from another.
The additional function of interactivity that allows viewers to filter the categories they want to focus on also adds functionality to the sleek design. Despite the complexity of the data, by leveraging technology and the platform that these data visualizations are presented on, data visualizations can become both aesthetically appealing as well as practical and effective.
Get To Know Other Data Science Students
Garrick Chu
Contract Data Engineer at Meta
Sunil Ayyappan
Senior Technical Program Manager (AI) at LinkedIn
Hastings Reeves
Business Intelligence Analyst at Velocity Global
External Factors
Another important aspect that creators often miss out on relates to the external factors of creating and delivering data visualizations–namely in terms of the transparency of the data itself and the performance of the data.
A typical millennial is very technologically savvy. They are up to date on the latest trends and are often used to a certain level of technological and informational sophistication.
Transparency, in this case, refers to how misleading a particular visualization is. For a long time, certain companies and individuals have used tactics like adjusting a graph’s scale or axis in order to create an impression that that particular set of data reveals something that it doesn’t actually support. Over time, customers in general and millennials, in particular, have become much more aware of such tactics and have a keener eye when it comes to spotting these kinds of tricks.
In other words, it is of the utmost importance that data visualizations (a subset of data science) made for millennials are trustworthy and created in an objective, unbiased manner so as to avoid leading viewers toward a particular point of view.
Another big issue relates to the performance of a set of data visualizations on whichever platform it’s being shared through. The speed at which the visualization loads on a website, for example, is of particular importance. This is an aspect that often goes unnoticed as it tends to be subtle. However, a few seconds of lag, while seemingly trivial, can greatly impact the attention span of a millennial viewer.
Be Creative With Your Elements
Above all, millennials value a touch of humor and a dash of creativity amid the dryness of the complex and unrefined sets of data that they have to view. Data visualizations that flip the genre and deliver information in a markedly distinct way, therefore, tend to hold a certain appeal that attracts millennials like bees to honey.
(Image source)
This particular visualization of “bus bunching” data by the folks at Setosa is a poignant example of how powerful visualizations can be in keeping a viewer’s attention.
One big point of this data visualization is its “gamification.” By using animation and game-like minimalist graphics, this visualization of a complex set of data regarding bus-delay times easily becomes something like a video game.
The addition of the similarly game-like features that allow people to directly interact with the buses in the simulation, affecting the delay of each bus by pressing and holding a button, further adds to the impact of the data visualization on a millennial’s attention span and interest in the subject matter.
The interactivity of the visualization can be important as well, as it gives each and every viewer a personalized experience. This is something that is close to the hearts of millennials as they value the time they spend on every website.
By creating an irreplaceable experience in interacting with a particular set of data, millennial viewership is likely to rise as it gives them an additional reason to not only view the data but actually be involved in the data itself.
In the end, millennials value attention to detail and authenticity the most. When it comes to creating general content for millennials, not just data visualizations, it is always important to keep their unique preferences in mind and create with purpose. Simultaneously, data scientists and data analysts are working to learn more about millennial behavior patterns.
Since you’re here…
Thinking about a career in data science? Enroll in our Data Science Bootcamp, and we’ll get you hired in 6 months. If you’re just getting started, take a peek at our foundational Data Science Course, and don’t forget to peep our student reviews. The data’s on our side.
This post was written by Tammi Chng. Tammi is a freelance writer based in Singapore. Specializing in healthcare and technology, she’s never without her head stuck in a book and her mind on the run.
