I have always been amazed by master sommeliers, by how much they know about wine, food, and tasting. For someone like me, the most I can say is “I like this” or “I do not like this”. Experienced sommeliers can take my past preferences into account and recommend new things that I may like. However, I would argue that my lack of ability to specifically articulate what aspects I like, why, and what could be changed — hinders me from being a more active agent.
Personally, I was trained — at schools and home — to think about facts and behaviors. I would argue that many of us are not trained to explain attributes about our own taste — what we like and why. We may not even notice the features and aspects we like and dislike. If we do notice, we may be unable to label that information and categorize it in a way that can help inform future decisions. We give unspecific feedback and reviews such as “I love it” or “I hate it” without being able to explain why. This is not to suggest that we need to be “experts,” but that we should be somewhat fluent in articulating our preferences.
Over the past 5 months, I started a habit of writing down what I like about the products and services that I use. I spent time thinking about which attributes I liked and did not like. I asked questions about how products/services/experiences impacted me — physically, psychologically, socially and intellectually. Did they engage me? Did they change my behavior? How? Over time, I noticed that I became slightly more articulate and better equipped to offer specific feedback. I even began to develop a specific point of view towards features, products, and services around me.
Why does it matter that we articulate what we like and do not like about products — wine, YouTube, Facebook, a course, a blouse? I would argue that the more informed we are about our own preferences and the better equipped we are to articulate the attributes, categories, and reasoning behind our taste, the more active we can be in how our world gets designed. Researchers, designers, and machine learning engineers are trying to “hear” people as they design, but we — as individuals — should strive to be more literate, and more proactive in making sure we have well-articulated points of view so we can be active participants in shaping what gets designed for us.