Data Visualization: Empower with Design Thinking!
|Begin your data visualization journey with Design Thinking! Unlock creativity, embrace user-centricity, and convey compelling stories through visuals.|
Keywords: data scientist, resonate with audiences, graphical storytelling, connections between people, data visualization, rapid prototyping, perspectives
Design thinking for data visualization
Harvard Business Review describes the role of a data scientist as “the sexiest job” of the 21st century. “Much of the current enthusiasm for big data focuses on technologies that make taming it possible,” says HBR, noting that data scientists are prized because they bring structure to large quantities of formless data and make analysis possible.
A key tool in the data scientist’s arsenal is data visualization, which is typically used to communicate insights and crystallize findings. “Often data scientists are creative in displaying information visually and making the patterns they find clear and compelling. They advise executives and product managers on the implications of the data for products, processes, and decisions,” adds HBR. And this is exactly where design thinking comes in, helping turn complex and incomprehensible data into a clear and clean display that can resonate with the audience.
Why data visualization matters
Interaction Design Foundation (IDF) reminds us that a picture is worth a thousand words, and often a story is best told graphically. “You could stare at a table of numbers all day and never see what would be immediately obvious when looking at a good picture of those same numbers.” Good data visualization can convey not just the relationship between quantitative values, it can also display relationships that are not obvious at first glance — for example, the connections between people.
If it has to work well, data visualization needs to be human-centric and easily palatable. As IDF asserts, we should always judge a visualization’s merits by the degree to which we can easily, efficiently, accurately, and meaningfully perceive the story that the information is telling. “To do this, we must understand the perceptual strengths and weaknesses of various graphical means for displaying particular stories.” And for this, we first need to understand how human perception works.
Design thinking and data visualization
Christoph Nieberding, managing partners at Designation, a Munich based design company, writes that the visual interface between a data product and a human being is very important. But, given the complexity of modern data products, standard visualization methods like bar graphs and pie charts are no longer sufficient. Besides, the consumers of that data may come from different backgrounds, different departments or teams, and have varied abilities to decode and understand the data being presented to them.
Design thinking can bridge the divide since it focuses primarily on users and their needs. Here, data visualization is created in context of user requirements and with a deep understanding of the data competency level of the user. “Designers need a deep insight into the data structure, the raw data and the data pipeline. What is developed creatively must also be technically possible. A pure UX view is not enough,” cautions Nieberding.
Design thinking also allows for rapid prototyping and experimentation with various approaches to data visualization, helping figure out the ones that work the best. You can do this by relying on storyboards, user journeys or experiences, and then measuring the effectiveness of each approach. This, in turn, helps you validate the assumptions you might have made about those consuming your data visualizations.
Applying design thinking
Design thinking is not something you tack on later, but it is a way of thinking, a mindset, that influences every step of your data visualization journey. To ensure your data visualization connects deeply with your target audience, you need to first seek out a wide range of user experiences and perspectives. ICTWorks observes that you will “never be able to solve every problem and overcome every data use barrier”, but you can at least work with your users “to develop a specific focus and thoroughly understand the barriers and challenges from their perspectives so you can tackle the most pressing issues”.
And that itself will be more than half the battle won.