Data Science Needs UX, Especially UX Research

Any model in math is better if it is stronger on one or more of these factors: reliability, extensibility, stability. UX research can get at what factors matter in a person or systems behaviour. This in turn allows data scientists to build more reliable and extensible models.

And also understand when the model might no longer apply (i.e., is not stable). It is not about one being better than another. Data Science and UX Research need each other.

Other areas Data Science and UX help each other:

Data Science and UX can be a feedback loop for each other. An improved qualitative model can improve a quantitative model, and vice versa.
UX research can help identify what is worth trying to quantify and so help define meaningful variables in the system.
UX research can help pull out causes. Equations, by their nature, are not causal (even Newton had to add two other laws to F=ma). We need human insight to reveal casual connections.
Existing math models can help UX researchers ask questions they might not have thought of otherwise.
Business people, understandably, love performance metrics. The “marriage” of UX research and Data Science can only help raise the profile of UX research in the eyes of business stakeholders.

Scott Plewes

Chief Strategy Officer

Over the past twenty-five years, Scott has worked in the areas of business strategy, product design and development in the high tech sector with a specialization in experience design. He has extensive cross-sector expertise and experience working with clients in complex regulated industries such as aviation, telecom, health, and finance. His primary area of focus over the last several years has been in product and service strategy and the integration of multi-disciplinary teams and methods. Scott has a master's degree in Theoretical Physics from Queen's University.