The Scientific Method and Data Science in UX Design

Asad Ali Junaid
Jun 14, 2018 · 4 min read
© Asad Ali Junaid.

The Scientific Method in UX Design

The purpose of employing a scientific method in the User Experience profession is to make sure we, as User Experience Practitioners, have not been misled into thinking we have deduced something which is far from correct during a design process.

A significant number of industry professionals perceive User Experience (UX) Design more as an art than a science because most aren’t aware of the scientific basis of the profession.

The belief is that, UX decisions can be made by discussions and opinions than by-

· Developing a hypothesis

· Running experiments to test hypothesis

· Reviewing literature, relevant examples & case studies

· Measuring & interpreting results to deduce what works & what doesn’t

· And then making informed design decisions

Because the UX profession employs different skillsets and engages people from diverse backgrounds, UX professionals have also been guilty of harboring the perception of UX being more of an art than a science by not being methodical in their approach and by not employing the prescribed methods to come to a rational decision for creating, proposing and evaluating design.

It is critical that stakeholders in organizations understand and appreciate the scientific basis of the UX profession so that the UX Professional has their backing for applying the recommended UX methods and practices with due diligence at various stages of a product or application design.

When the design emerges from a rigorous rational process based on scientific methods, empirical evidence and irrefutable data, then it will accurately represent the end user preferences and will be received well.

Data Science in UX Design

One of the goals Data Science is to communicate insights obtained from data gathering and analysis in the form of simple stories or visualizations that a layman can understand and come to conclusions on.

It has been long established the cognitive processes of perception and pattern recognition are more efficient in deducing or extracting meaning in an environment, than the cognitively intensive processes of memory, integration and inference. Data Science takes advantage of the cognitive process of perception and pattern recognition to tell these stories.

UX designers get data from the multitude of activities they are involved in which span a vast spectrum of people, skills and situations. Planning and conducting UX activities in a structured and scientific manner yields rich data with invaluable insights.

While dealing more data than they ever did, UX Designers should think of data as a valued tool that can be used to sharpen their instincts about users. Sophie Riwaters in her article ‘What is Data Driven UX’ lists out the four critical things a UX Designer can do with the help of data. They are-

· Prove — that you’re on track or not

· Reveal — future opportunities or pain-points

· Discover — new patterns and trends

· Improve — your design by adding objectivity

In fact, Laura Denham goes on to suggest that Data Scientists are the next UX Designers. In her article she explains that there is a new form of UX which is being driven by data, AI, and cognitive computing and that those who can understand and implement data-driven insights will become critical team members across organizations.


By learning how to scientifically conduct UX studies, methodically collecting and analyzing data collected and subsequently providing sound design recommendations, the UX Professional will not just increase his own credibility within the organization but he will be easily able to evangelize and implement UX processes with a fair degree of ease across the organization.

When data is used in a way which tells stories, deep awareness of end-user preferences and behaviors come to light leading to significant design insights and decisions which can disregard individual opinions and preferences.

References and Further Reading:

· Luda, M. Why UI / UX Design ≠ Art (2018)

· Marsh, J. UX is a Science. Not an Art. (2013)

· Bennett, K, B., and Flach, J, M. Graphical Displays: Implications for Divided Attention, Focused Attention, and Problem Solving. (1992).

· Denham, L Data Scientists Are the Next UX Designers (2016)

· Brooke, S. How Can Data Science Improve UX Design? (2018)

· Huang, R How Designers Can Use Data to Create Amazing Work (2016)

· Riwaters, S. What is Data-driven UX (2017)

· Nielson Norman Group. Analytics and User Experience (Course)

Author Bio

Author is a designer, startup co-founder, fiction novelist and a design educator. He can be reached at asadjunaid (at) gmail (dot) com

NYC Design

A publication for designers of New York & design lovers from all around the world.

NYC Design

A publication for designers of New York & design lovers from all around the world. Design thinking is what makes us share with the whole world.

Asad Ali Junaid

Written by

Designer@Adobe, startup co-founder, fiction novelist and a design educator.

NYC Design

A publication for designers of New York & design lovers from all around the world. Design thinking is what makes us share with the whole world.