Data Analytics for Design Thinking
It can be tempting at times to see Design Thinking almost as something that happens by accident. This is of course not the case for companies or individuals making a point of practicing it. But because it is a multi-faceted, qualitative tool for developing strategy, it is in some ways something that is happening all the time in the service of customers, audiences, or other subjects. Indeed, a Northeastern University professor’s assessment of Design Thinking in education in a fairly recent article was that “many teachers are already using design thinking but may not know it.”
This can be true in areas aside from education as well (education not being what we typically think of when we analyze Design Thinking). However, while it’s easy to understand how some version of this sort of strategy can come into play organically, the most effective modern Design Thinking is actually more intentional. Specifically, it is that which combines the core principles of Design Thinking with data analytics.
What Does Data Mean for Design Thinking?
Design Thinking is fundamentally a problem-solving method that can be applied to any number of projects or services in the name of offering better solutions to a target audience. As such, it is based on a few connected efforts that are meant to determine, through progress and results, what works best for the people being served. Those efforts can vary from one project to the next. But at the very core of Design Thinking is a progression empathy, to ideation, to experimentation (sometimes with lengthier creative and/or prototyping processes built in).
These steps are often described as being qualitative in nature. Any entity practicing Design Thinking can assess audience needs and preferences in the “empathy” phase, make internal creative decisions in the “ideation” phase, and then assess changes internally during a process of experimentation. But through the implementation of data analytics, the same processes can be turned into more quantitative ones. Data regarding audience preferences, idea feasibility, and the effectiveness of solutions can be used to inform the entire process from beginning to end, and over again through trial and error.
How Can You Implement Data in Design Thinking?
The first step in implementing data analytics in design thinking is to tap into the ever-expanding field of professionals in this discipline. While education in modern data science is still lagging behind the times in some respects, there is now an active and widespread field of study that has developed through internet-based institutions. And a growing job market is only attracting more students. According to a Maryville University write-up concerning online master’s programs in data analytics, positions like “Market Research Analyst” are expected to grow by nearly 20% by the year 2029. This sort of demand is giving rise to a whole new community of people educated in the process of assessing markets and conducting research through data-driven approaches. Professionals of this nature can give you the best opportunity to effectively integrate a data operation, whether you hire them permanently or opt for contracted work.
Even with a trained data analyst on hand though, you’ll also need to reassess your strategy and methodology to accommodate a more scientific approach. A DataKitchen piece about Design Thinking and data correctly points out that while we think of quantitative processes as being almost automatically efficient, there can be some hold-ups. As that piece put it, data teams are “constantly interrupted” by data and analytics errors, and spend much of their time “massaging data and executing manual steps.” This certainly isn’t true in every case, and if you are able to secure help from an educated data professional, you’re less likely to have problems. But your process should be designed to allow for occasional inefficiencies, or simply for brief delays while you’re awaiting data analysis.
Why Apply Data to Design Thinking?
We’ve covered this question to some extent in discussing the points above. But to assess the reason for implementing data in Design Thinking efforts more directly, we’ll simply point to two factors.
The first is that, as we noted in the post ‘Design is a Mess’, design can be a “muddy and frustrating and wonderful” process. This is not meant to be entirely negative by any means. But there can be a certain intellectual clutter or practical disarray to design processes. And while some might argue that this ultimately fuels creativity in some circumstances, it can also result in tremendous inefficiency. And that brings us to the second factor, which is quite simply that data works. It needs to be implemented with proper strategy, and handled correctly once it’s gathered. But data can turn a hit-or-miss approach into a targeted one, which in turn can take some of the “mess” out of design.
Because of these factors, proper implementation of data analytics in Design Thinking can be incredibly beneficial.
