Digital design is in a good place as a practice and craft.
There are well-established patterns, guides, recipes, and abundant educational resources. Standards and uniformity exist where needed while leaving space for creativity and experimentation. Reflecting on the progress of digital design and the possible futures ahead exposes many remaining user experience challenges and uncertainties for designers, technologists, and product managers to wrestle with.
Framing these “Grand Challenges” helps bring purpose and clarity to nagging and unfulfilled needs that are not easily solved or overcome and have the expectation of hard work over a long time. There is no shortage of topics spanning many levels of abstraction to consider. I chose interfaces for AI, technical problem spaces, data visualization, spatial computing, and enterprise software, which we’ll explore further in a series of posts. Take them as provocations or nonsense. Please question, debate, reject, refute, or take action on them.
First up—data visualizations
Data is the heart and soul of most general consumer and enterprise software applications. Users need data visualizations to help them synthesize, analyze, and make sense of data. Users need to gain new knowledge from data, but instead of empowering knowledge gain, UIs apathetically say, “Here is the data; you figure out what to do with it.” Product managers and designers cannot expect users to have the skills of data scientists or data engineers, yet that is the burden imposed by a table of data. Recently, consumer apps have produced interesting and useful data visualizations, but enterprise software has progressed little in the last few decades.
Data scientists and engineers could also benefit from better forms of data visualization. Tools like Matplotlib (and others built on top of it) and Jupyter Notebooks are, unfortunately, overused simply because of their ease of use and familiarity. Technical users (scientists, engineers, developers) will overlook the value of user experience because their mindset is that the “answer” is all that is important, and UX is a luxury. I argue that UX is not about comfort and indulgences but is an aid for seeing data from different perspectives and in other forms that would otherwise not be possible. In effect, good data UX leads to better answers.
Data should be exciting, vibrant, and interactive. Several great practitioners of data visualizations have shown us how to elevate raw data into something more meaningful (e.g., Edward Tufte, Giorgia Lupi, David McCandless, Cole Nussbaumer, Mike Bostock, and Manual Lima, to name a few). The challenge is to follow their leadership and make data visualization pervasive and available in more digital experiences. Designers must be comfortable designing data visualizations, and development teams must build them proficiently.
Focal Points:
- How do UIs empower users to pull meaning from data without being expert data scientists or analysts?
- How does data visualization become ubiquitous in design practices? How does data visualization become muscle memory just as rounded corners and color palettes are?
- How do product managers and designers become more comfortable with data?
- How do software developers become more proficient in building data visualizations?
