Designing for hundreds, thousands, or millions of people is a high stakes game where data provides us the safety and drive to make sure our work doesn’t fall short. But when we only look at the numbers, we’re eliminating one of the key things that helps us innovate and design greater – our creativity. How do we take back the ability to be creative in a world obsessed with data?
What’s the deal with Data & Design?
For this discussion, let’s think of “Data” as objective, retroactive, measurements. They’re the qualitative and quantitative factors that we pull together through observation or collection of specific points in the hopes of spotting trends or insights. Data is all recorded information based on or influenced by past experiences. On the flip side, “Design” are our subjective, proactive, ideas. Design is the way we anticipate needs and form solutions, through our individual experiences and interpretations of intent. It’s also what we associate with creativity and the distillation of creativity into ideas we can act upon or share.
Historically as entities - these two have always been the best of friends. Design needs Data to advance and inspire ideas and solutions. Data needs Design to orient focus and build understanding in measurements of information. But tension has been growing over the years and this friendship has become strained, creating ethical and tactical quandaries. It stems from a change in the balance of the relationship.
Where once equals - we have pulled data to sit above design. Above means:
- Placing higher value on the power and importance of the retrospective information in creating any solutions.
- Ubiquitously requiring measurement prior to making any decision or taking any action
Translating that into how it affects creativity and the things we make – well, we are focusing more on optimization than innovation. This is where “optimization” refers to the small incremental improvements of any existing system, situation, or resource in hopes of finding the most effective use. Whereas “innovation” is the introduction of something completely new to shift a system, situation, or resource toward greater efficacy or unforeseen value.
A tech industry example would be phones. The iPhone X is far more powerful than any previous gen of the phone, but it doesn’t change the paradigm of what smartphones are or could be. By comparison the original iPhone was born from a new space. It was about what a phone could be, beyond phone calls and texts. It was an innovation.
How did we get here?
So how did we end up in this state? How did we get into loops of constant optimization and data which supersede intuition or creativity? I believe it’s a combination of three factors:
- Tactical = the change in access and use of data within design processes and decision making
- Cultural = the change in value of design and specific metrics of data
- Business = the shift in scope of design decision making and data analysis
These three factors didn’t spring up immediately – we’ve been heading down this path over several years. We can dig into each one to find out how.
This is all about the change in access and use of data within design processes and decision making.
In effect - the way in which we used to gather and use data through physical manufacturing and design process was much more considered. Gathering data was slow and costly. And the risk of misinterpretation of data equally could result in massive losses. So data was more a guiding factor in decision making - rather than a core driver.
With software and the ease of changing design through code, data became more readily accessible - at smaller focus and larger scale. This access and ubiquity let data become a core factor in decision making and more rapid iteration in design.
In a Fast Co article Matt Webb (managing director of R/GA’s IoT Venture Studio in the U.K) put it well… “This is a unique problem of the software age. Historically, design was about making physical things, whether it be office chairs or album covers.…Designers have always done user testing, of course, but it’s much harder to change a physical object than it is a piece of code. Now, the constant tweaking of software creates a never ending design process, where every click is another piece of data to optimize.”
This pertains to the change in value of design and focus on specific metrics of data.
At the same time as the process of design and usage of data to support it was changing, we also saw a huge cultural shift in the value of design - from one that equally valued emotion, to one that favored function.
Taking a look at the early versions of some pretty popular experiences today, like Facebook, Google, Craigslist and more – we prioritized utility of interactions and information. Granted we did have limitation in the ability to create aesthetic forms by the languages and browsers of the time, but we also didn’t really care. We loved these experiences for what they did.
This function-first economy paved the way for a new and highly prized metric in our culture – “Engagement” (ie. the number of clicks or how long an individual stays on a page). In the battle for customers, it became less about how we might feel about these experiences - because there are so many variables in that - and more about how data could prove and support ways we could capture and retain their attention against the others’ seeking it.
And thus came the concept and movement of “Data-driven design.”
With our focus on capturing attention and engagement being the key factor - it was easy for us to take what data was (an objective, retroactive, set of measurements that could help guide our decisions) and turn it into the core decision making instrument for any change.
For example, there’s the infamous “41 shades of blue” from Google. In this test, Google’s engineers couldn’t decide between two shades of blue for showing search results. So instead of just trying one or the other, they blew out the options and tested 41 of them to see which attracted the most clicks. Going with a smorgasbord of blue was no big deal, since they could perform these quick changes to the code without too much risk or impediment to customers and audiences. It led to finally picking a hue of blue they could all agree on because of the quick access to data.
So, in that example, the increased speed of access to data allowed it to become far less risky to use in decision making for massively scalable software. Going further, the larger the quantity of data, the easier it is to act for the majority. Data could be used to settle any disputes and challenges between stakeholders and creative teams as they grew – increasing efficiency in the creative process.
One last aspect - this is about the shift in scope of design decision making and data analysis.
The interesting part about putting data in the driver’s seat for design, much like that seen through the “41 shades of blue” test, is that businesses also shifted the responsibility and accountability of design decisions outside of the design practices of holistic thinking and imagination, and into things like telemetry and focused problem sets.
So while design was becoming increasingly important in business, the scope of ideating, exploring, and analyzing data shrank - being divided amongst feature teams or subgroups of companies.
In that article by Fast Co with Matt Webb - summed it up well again: “More than ever before, designers are sitting on the C-suite of companies. Large corporations are investing in design because it makes good business sense…But as design has become integrated into the heart of companies…designers themselves, beholden to business interests that demand the most optimized, most persuasive version of something as opposed to the most useful and helpful for the user, have decreased agency. In other words, with power has come less responsibility.”
In my next post, I’ll dive into how we can rebalance the relationship between data and design. Stay tuned.