The Importance of Intersecting Design and Data Science

However, doing so ignores the beauty of being interdisciplinary and multi-faceted in today’s ever-evolving world. As time goes on and more and more people begin to pick up coding and technical skills, the benefits of specializing in one or two particular aspects of the tech industry will become more and more valuable.

In my particular case, I’ve found myself increasingly interested in design work as I’ve been building my own projects and databases and taking into consideration how certain aspects would scale from the perspective of a more technical person, like a software or machine learning engineer.

Here’s an example:

For someone interested in beauty like myself, exploring new products and ingredients is like breathing— it’s something I do on a regular basis without thinking too hard. Thus, building a database of beauty brands and ingredients is fairly enjoyable because I have enough domain knowledge of both the beauty and tech industries to comprehend how to combine them in a way that someone interested in either or would appreciate.

However, for someone with an expertise in software development, information on makeup and skincare may not be as important to them as understanding how to properly implement an API into their codebase with zero bugs and minimal friction.

These are challenges that most tech companies tend to address after their product is already built and in the hands and desktops of their customers. Thinking through them early on gives founders and entrepreneurs an advantage when it comes to growing and scaling later down the line.

While this is one example of how to combine both design and data science, many more are present, such as taking a more human-centered approach to building a product itself; and, in the case of a web or mobile application, considering how user behavior is heavily influenced by industry standards in UI/UX that we don’t tend to question as often.

Historically, A/B testing and casual inference have pushed key stakeholders to test and validate their assumptions, but can also become an echo chamber and self-validation loop when companies stray too far away from the initial values and theories behind the products themselves.

This is where design and data science need each other as checks and balances, similarly to the relationship between product management and engineering.

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