In April of this year I attended the UXIM15 Conference in Salt Lake City, UT, where I sat in on Jared M. Spool’s keynote: “Is Design Metrically Opposed?” The basic premise of Spool’s keynote is this: Google can’t tell you what the emotion, the human behavior, the mindset that influenced the action that resulted in the analytic. The human experience needs to be measured as well in order to create a holistic experience.
Since then, I’ve had a number of conversations with UX peers about how much power we (as a profession) give data when it comes to making user-centered design and experience decisions. While doing some additional research, I came across an article on UXMag on this very topic: “6 Myths about Data-Driven Design” by Pamela Pavliscak. I’ve summarized below; read the full article here.
So what counts as data…and what will inform design in a meaningful way?
Myth 1: Data Means Numbers
Numbers represent the actions of real people with complicated lives. But even the most organized sets of numbers don’t answer a lot of questions we still have about the user experience…why people take action or why they don’t, or how they felt about it, or what expectations they bring to the experience.
Because qualitative insights are not numeric, they are often not considered data.
Myth 2: Data Is the Objective Truth
Because quantitative data is typically tallies actions and those actions are tallied by software, it makes quantitative data seem like hard fact. Even if data is big, it does not mean it’s objective. Bias is inherent in any data set because datasets are created by humans who interpret them and assign them meaning.
Big or small, not data is perfect. Good data describes its biases, and always provides context.
Myth 3: Bigger Is Always Better
When we think bigger, we tend to think about tallies; the volume and velocity part of the big data equation. But big data is also about variety, and that means diverse sources. We have to get our data working together in a way that isn’t all about back-end integration. In other words, creating meaningful categories (metrics) to evaluate, understand and track.
Broader, not bigger, is the better.
Myth 4: Data Is for Managers, Not Designers
When using data to inform design, there are three ways of looking at things: proving, improving and discovering. Because different teams refer to different types of data, they may be discounting or not aware of data of other teams.
Data is not just about proving who is right or wrong. It’s about making improvements and discovering new possibilities.
Myth 5: Data Kills Innovation
Data is seen as the antithesis of innovation, specifically in three ways:
- Most data is backward looking. It’s not easy to make predictions based off of discovered patterns and trends.
- Data is tactical rather than strategic. It’s a good way to tweak a design element, but not for creating an amazing experience.
- Data, especially analytics, seems to skim the surface. Data does not work well for informing design, because it lacks information about motivation, expectations, perceptions or emotions.
Myth 6: There Is a Right Way to Use Data to Inform Design
As of now, there is no one canonical way that works for every team and organization.
A few guidelines:
- Use data from a variety of sources
- Include numbers and context.
- Make sure data is sensitive to the complexity of the human experience.
- Use data to track changes over time, rather than just proving who is right or wrong.
- Decide on meaningful categories to make sense of the data and tell a story about the experience.
- Develop a way to share and discuss data.
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