Why can the quality of data visualization make or break your project?
Questions you need to ask yourself when creating data visualizations
As a design studio, we had the chance to work on a ton of projects that contain some sort of data visualization, from simple tables to complicated charts. So in this blog post, we’ll offer a brief insight into our thoughts on the topic.
Consuming large sets of data isn’t always straightforward. Some data sets are so large and complicated that it can be almost impossible to conclude anything useful from them. That’s where data visualization comes in.
What is data visualization?
Some nice people at IBM say that:
“Data visualization is the process of translating large data sets and metrics into charts, graphs and other visuals. The resulting visual representation of data makes it easier to identify and share real-time trends, outliers, and new insights about the information represented in the data.”
In the times of almost instantaneous access to information and as our society’s attention span keeps dropping, data can get lost because it’s hard to find, confusing or overwhelming. As a result, we try to keep data visible, relevant and digestible by simplifying and making it as visually pleasing as possible.
Most of our projects include digital products, websites and deck presentations, so we tend to create simple charts and graphs, and we make the most important information stand out the most.
What we see in Data Visualization today:
When we talk about graphs and charts, the first images that pop into people’s heads are a bunch of outdated schemes, charts and diagrams on a powerpoint presentation that have been copied from an excel sheet of some kind.
As the world transitions to digital more and more, and new products emerge on a daily basis; visually pleasing, accessible and understandable data becomes an everyday necessity.
When working on the research phase of various projects, we rarely come in contact with well designed, and, more importantly, unique visualizations.
A great number of “modern” products have the same approach, they use really similar visual solutions. That practice can water-down the uniqueness of every individual product and the relevant data that it provides.
Common patterns in data visualization:
In our eyes, data visualization is used almost everywhere, in the digital, as well as the real world. From business idea pitch decks and pandemic impacts, to bus stop time-tables. We see patterns everywhere, from displaying large data sets, to conveying a simple idea from one person to another.
When choosing the right visualization, we need to look at three, in our opinion, main factors:
The purpose of showcasing the data
For us, this is the most important aspect to consider when designing any sort of visual or data representation.
The complexity of the data
This greatly shapes the visual side of the solution, we analyze how abstract or how literal our visualization has to be depending on the complexity of the data.
The limitations of the medium
The same design approach can’t be applied when making a visual for a card on a mobile screen, a table for a laptop, or a growth chart for a wall-sized, projected business presentation.
That being said, you probably noticed some common patterns already. And how each one can be used to represent different sets of data, with differing purposes.
Some of the more popular visualizations are:
Numbers, Graphs, Charts, Pies, Tables, Candles, Bars, Diagrams and Flows
Our process of designing unique, but functional visualizations:
When we look at some of the projects we’ve been working on, most of them have some sort of data visualization, from simple call-out numbers and tables, to more complex charts and bars.
We’re always looking for ways to innovate our processes, but here is an overview of our current approach:
Understanding
Once we know our clients, their goals, strategies and ideas, we try to understand, clarify and find the purpose of the data that we need to showcase.
Experimenting
We experiment with different visual directions and ideas a lot, sometimes expanding the initial design direction into something new.
Limiting
The simpler, the better! If we can keep the visualization options to a minimum, it becomes easier for the eye, people will know where to look for certain information and we will avoid confusion.
Defining
By writing rules and defining visualization use cases, we can get a clearer view of what’s really necessary, and it gives us solid, custom-built guides that implement really nicely into our Design Systems (more on that here / link to the other blog post).
Client benefits of using data visualization:
Sometimes, as a client, non-designer or even designer, it can just be easier to go with a presumably “safe” visualization for your data.
And we understand that, but, in order to have the best impact on end consumers, we take our time to develop custom visuals for different data sets and goals.
Along with the usually requested things and expected benefits, our clients can say that, at the end of our projects, they are left with: unique designs, a clear structure and, most importantly, correctly informed end users.
Conclusion:
Choosing the right visualization pattern, checking all the necessary boxes and creating a unique solution that’s still in line with the project you are working on is hard, and it takes tons of practice, iterations and time to do it accurately and effectively. The most important thing is to create a visualization that makes data more accessible to everyone, and the execution depends largely on the type of data and the context behind it.
We hope that we helped you with this insight into our thoughts about data visualization and made your process a tiny bit easier!
HOLOGRAPHIK® is a creative studio specialized in design and motion. Our work mainly covers strategy, art direction, web and mobile solutions, 3d and motion design. We help brands and visionaries find dauntless and unique visual identities, create lasting value and inspire future trends.
Stay up to date with everything HOLOGRAPHIK®:
Website │ Instagram │ Dribbble │ Twitter
