Midwest UX 2014 Recap: Data and Design

In a pair of back to back talks Jen Matson (@nstop) and Pamela Pavliscak (@paminthelab) explored different aspects of what data means to the user researcher. Jen Matson approached the problem from the perspective of working within a larger team while Pamela Pavliscaks talk focused more on how we can understand and decipher data without a master's degree in statistics.

Measuring the Wrong Data: Data Driven Design Pitfalls

The problem with data is that everybody can interpret it differently. As professional whose goal is to synthesize research findings into meaningful plans of action this becomes especially challenging. Jen Matson spent a half hour walking through several examples to demonstrate where looking at the wrong metrics could have led to design decisions that actually made the user experience worse.

Of all the examples that she worked through the one that stood out to me was "Throwing Stuff Against a Wall". All too often on projects it is tempting to use your gut, build something, and then hope that you got it right. Unfortunately this can mean investing lots of time and resources into designs which should never have been approved. Poor communication and distracting feedback leads us down a rabbit hole into Wonderland instead of towards better experiences.

If you have about a half hour to spare and find yourself struggling within a larger organization to use data for good this talk is highly recommended. If you don't then at least listen to the actionable list of takeaways at the end.

Be Data Driven Without Being a Data Scientist

Coming from a technical background I was familiar with the distinction between "data" (raw numbers) and "information" (numbers with some context). Pamela Pavliscak expanded on this by showing that unless you stop to consider the meaning behind the data you collected then you are just generating more data for the sake of having more data.

After a brief fifteen minute break the theme of data continued with a exploration of how non-mathematically inclined UX professionals can harness the power of data in their own work. Highlighting five key areas, it attempted to build a foundation for the data curious to start from. Those five areas included curiousity, probability, transparency, context, and meaning.

The biggest takeaway from this talk was that context and storytelling matter just as much when looking at data as they do when presenting wireframes, developing personas, or any of the other many ways in which design gets communicated to stakeholders and other team members. Without a sense of scale as to what a 50% increase in traffic means or 1,200 successful checkouts per day your numbers are just numbers. Only when you link them to a meaningful narrative do they help you determine what is considered successful.