Data-driven design sounds pretty self-explanatory on the surface: collecting data about activities of users on a website and making key design decisions based on the insights garnered from these numbers. Using this data from web analytics to make design decisions can be a cheaper, automated, and potentially quicker way to know what users want. Or is it?
We, as students and professionals of the UX/IA profession, already know that many businesses have no idea that UX research and design is a discipline, and if they do know it exists, might not necessarily know how it works. Even if a business or company knows about UX design, they might have the hubris to think they can straight up design or engineer their way directly to a great user experience. This is where analytics and data-driven design can be seen as a workaround to the iterative UX design process. Why hire a team of people to do qualitative research on our users when we can cheaply collect quantitative data. Quantity is better than quality in the world of big data, right?
Lets take a step back and explore web analytics. Web analytics is the measurement, collection, analysis and reporting of web data for purposes of understanding and optimizing web usage. This means collecting concrete numbers on metrics like page views, bounce rates, click throughs, etc. These numbers could be useful in discovering usability problem areas on your site. Users never click through on a certain page, so something must be wrong. Lets fix the problem! Now the decision on HOW to fix the problem is the difference in depending purely on analytics versus using these numbers as a tool to begin the the UX design process in a certain area of the site.
By utilizing purely analytics, a company can adopt the model of South Park’s underpants gnomes to their design decisions (apologies if the reference is a little dated). As a quick reminder, the gnomes of South Park would collect underpants in Phase 1, ????? as Phase 2, then profit is Phase 3! By only working with quantitative numbers, a company is basically following these three phases: 1 is collecting data, 2 is ?????, 3 is great user experience and growth! From my personal experience working with a company that adopts this model, I can say that this mysterious phase 2 is founded in “intuition”, “gut feeling”, and “industry knowledge”, which truly is….???
But luckily we as UX/IA students and professionals know that this approach is flawed and completely leaves out one of the most important aspect of the UX research and design process: context. There are many resources available on the web that explain how to incorporate data-driven design into the UX design process, some of which I link below. Basically, these quantitative numbers offer a great starting point to designing for a better experience. Resources, especially time, are not infinite, and having quantitative numbers can be a good way to know where to divert attention.
Pamela Pavliscak (2015) Six Myths about Data-Driven Design
Rameet Chawla (2015) Creativity Must Guide the Data-Driven Design Process
Bartosz Mozyrko (2015) Putting Big Data in Context
Jennifer Cardello (2013) Three Uses for Analytics in User-Experience Practice