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8 min read

The roles of data and intuition in design

In search of logical problem solving, design is steering away from taste. Can data and intuition co-exist in our decision making?

Data and intuition in design

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Breakthroughs occur when we combine different forms of knowledge. Among these are the things we know and the things we feel.


While they seem contrary in nature, data and intuition can combine to forge new insights and innovations. Yet, in the course of designing products and services, it’s important to ask, When should we rely on fact? When should we follow our instincts?


There are so many digital products today that allow us to instantly measure the impact of a different button color, or of a change in wording. When data is so readily available, it can become a driving force in shaping products and services.


This raises the question of whether we should approach design as more of a science or an art. Perhaps it’s through the integration of both data and intuition, that designers are able to make the most informed decisions.



What is intuition?


Intuition, also called instinct or gut feeling, is linked to subconscious decision-making.


According to psychologist Daniel Kahneman in his book Thinking, Fast and Slow, intuition is the ability to spot patterns and to integrate insights from past experiences. Intuition is automatic, effortless, and subsconscious.


We cultivate intuition by observing the world around us. As we process bits of information from our surroundings, we develop an instinct that tells us when something feels right or wrong. When we notice patterns, our bodies release neurotransmitters to the brain and gut, which, in turn, communicates a sense of understanding faster than conscious thought.


When it comes to intuition, we often can’t exactly explain why or how it is that we know something.


Intuition is mostly useful, but often biased. For one, it may be limited to a person’s past experience. For example, if you talk to different web design experts, they’ll each have a different instinct about color and layout, depending on their prior experiences.


With variances in intuition, and increasingly complex design problems, we more often than not turn to the power of data.



Intuition in design

Not only is design steering away from instinct and taste, it is also increasingly about solving problems and crafting useful and functional solutions.

What is data?


Data are bits of collected information about the world.


It can be quantitative, such as product metrics, or qualitative, like customer reviews. In general, quantitative data tells us the what, and qualitative data tells us the why.


A mixed approach combining both quantitative and qualitative data is most common for product development, as it brings together the best of both worlds. By synthesizing insights from randomized controlled studies, big data sets, and the lived experiences of customers or potential customers, we can look for patterns that might be too subtle or too complex for us to identify without the systematic collection and analysis of data.


Data has this marvelous effect of seeming scientific and objective. However, we have to understand that data, too, can be biased. At each step—when deciding what to measure, how to measure, and how to present the findings—we have the potential to shape the data according to our personal biases.


In the words of statistician and artist Edward Tufte in his book The Visual Display of Quantitative Information, we should strive to present data as clearly and as honestly as possible. Maintaining graphical integrity means to present the data in an accurate and precise way, similar to the use of scales that are properly proportioned. Our findings should not be oversimplified or obscured, but rather layed out to allow audiences to reach their own conclusions.


Despite the potential for possible biases, designers can turn to data to help them make more informed design decisions.



Data in design

However ingenious an idea may seem, innovation rarely appears out of thin air, free of predecessors or origins. It’s by observing problems in the world around us, and exploring possible solutions, that we come up with truly innovative ideas.


Balancing data and intuition


Design may be shifting towards more logical decision-making, using well-defined processes and frameworks. Not only is design steering away from instinct and taste, it is also increasingly about solving problems and crafting useful and functional solutions.


Yet, if we let go of our gut feelings altogether, we’ll be missing out. It’s in fact the synthesis of data and intuition can help designers make better calls. These decisions might include anything from what products or features to make (and when to say no to a feature), to what experiences to create, to what interfaces to design.


Let’s look at some common scenarios to explore the role of data and intuition in design decision-making:



1. Creating a novel product or feature


In order to create something brand new, that has few marketplace precedents, we need to rely on intuition. Without the benefit of data gathered through comparison, it falls upon the individual or small groups of people to build something from the ground up.


This can involve recognizing patterns or subtle hints in the marketplace before others do. Simultaneous invention often happens this way. Independent groups of people in different parts of the world, come up with the same idea or invention at around the same time.


In his book Black Box Thinking, journalist Matthew Syed explains this phenomenon. According to him, intuition—particularly the recognition of subtle patterns in the world—enables people to converge upon similar breakthroughs. For example, general relativity was famously conceived by Albert Einstein, while three other researchers reached the same conclusion all at the beginning of the 20th century in response to Newton’s laws.


However ingenious an idea may seem, innovation rarely appears out of thin air, free of predecessors or origins. It’s by observing problems in the world around us, and exploring possible solutions, that we come up with truly innovative ideas.


To cultivate intuition, designers can begin by observing the world around them. By noticing how designs can be improved, such as door handles that indicate push, when they actually need to be pulled, is one widely acknowledged example. Another might be observing how a set of light switches might be better designed to communicate their controls.


By diving into these design problems, and exploring potential solutions, designers can cultivate pattern recognition and intuition.



Data and intuition in design


2. Creating a product or feature that has marketplace precedents


When creating products or features that have marketplace precedents, it helps to begin with data. Existing products can be evaluated via benchmarking, and these insights may be used to make more informed design decisions. By comparing competitor products or existing features, designers can save time and money by learning from the successes and mistakes of others.


When designing a new vehicle feature, for example, we look at market research to examine how competitors have implemented the same feature. By referencing market data, we can explore the impact of different designs, and the result of their implementations. Studying what’s already out there can help us tap into a readily-accessible body of knowledge, without having to invest the time and resources into building the design out from scratch.


Data can also inspire innovation by revealing gaps in experience. It might show where customer pain points are, such as by looking at where they leave a site, and why these pain points exist, like detailed customer reviews. These data points can offer both the problem and solution, which helps drive the product forward.


Intuition is essential in data collection because it can be extremely easy to collect the wrong type of data. This can mean data that doesn’t apply to the design problem, or data that’s no longer relevant at the current stage of design.

3. Exploring customer behavior


To understand customer behavior, the right kind of data can be essential.


Qualitative findings help us understand why people do the things they do. User interviews and observations are rich sources of information for understanding the how and the why behind customer behaviors.


Let’s say we’re curious about customer behavior during a typical commute. Go-alongs can be a great source of insight, as they combine both observational data and interview responses. Mixing different methods of data collection is particularly beneficial. Observational techniques can reveal much more than just conducting interviews alone. This is simply because we don’t always know what we do or why we do the things we do.



4. Testing design assumptions


Assumptions are generally accepted as fact, but need to be verified with data when in question. Designers typically design with a variety of assumptions about customer behavior, which are usually derived from intuition and past experiences. To test these assumptions—particularly ones that will have major customer impact—we can look to data.


When designing the interface for a vehicle, we might assume that customers will read the instruction manual. A design based on this premise might include a more simplified interface, that communicates less information. As a result, this assumption might greatly impact the overall experience of driving and owning the vehicle, so we would need to gather data in order to either verify or reject it, before allowing it to instruct our UX design processes.


While some assumptions will be accurate, others may be completely mistaken. One key part of this is understanding when we are making an assumption in the first place. By noticing our assumptions, or pausing to write them down, we can become more aware of the beliefs that we take for granted as being true, which in turn drive our decision making.



Data and intuition in design


5. Choosing between a small set of options


When choosing between a small set of options, data can help us distinguish between designs that work, versus ones that don’t.


A/B tests are a great way to differentiate between a small set of options, tied to a measurable outcome. For example, say we need to choose a color for our call-to-action button. By running an A/B test, which can be set up as a randomized controlled study, we can measure which button color results in the most clicks.