You’ve probably heard the theory that left-brained people are analytical math whizzes and the right-brained among us are creative, artsy types, right? So what’s a creative designer to do when working towards a design goal from a large base of data? In my experience, the answer is to throw out that theory and combine those brain hemispheres, building the creative process out of the available evidence.
Some projects come with a lot of data. This can be hard numbers (quantitative data, such as a heat map of user interactions, link click-through metrics, or a ratings scale) and/or a wide range of descriptive information (qualitative data, such as notes taken during a focus group or open-text responses on a survey). Data is great support for a project, but it can very technical, and the amount to sort through can be overwhelming. When this happens, creative thinking can get lost in the shuffle, completely drowned out by the information. This is important to me because when data and creative thinking are properly combined, the creative thinking makes the data so much easier to understand – and ultimately easier to explain.
I guess what’s really important to me is making the data so easy to grasp that a child could understand it. Once you’ve done that, you’ve paved the way to establish your goals, attack problems from new directions, and deliver better outcomes. As a designer, I’ve seen how this leads to a better user experience at the end of the day. Thinking about it so much makes me want to write about it, to show how a creative thinks about end-goals during a data-driven project.
An Idea Takes Shape
The best results often begin with a good brainstorm! When an idea is born, I usually start the process by sketching rough concepts in a shared online whiteboard, such as Miro. This gives access to the whole team, whether they’re remote or in-person, so we can brainstorm together. More brains = more brainpower. I make sure to include background information on the board, things like articles, examples, half-baked ideas and user stories that help showcase the data set, the challenge and/or the problem. Personally, I also like to check if there is a similar concept on the market that can be improved upon. Why reinvent the wheel if you don’t have to?
Data of all kinds can help build this backdrop. For example, qualitative interviews or focus groups can help answer early questions we may have, or even reveal questions we didn’t know we should be asking. Collect enough of this data and we can place their answers on the board and organize them into clusters of ideas or group thought patterns, a process called affinity mapping. Later on in the process, we can test prototypes in measurable ways like click counters or heat mapping to get an idea of how users are interacting, and add this information to the board as we continue progressing.
The end result is sort of like a mood board; it’s a great background to support everyone in the creative process. Having this board in place, with all the information visible, helps me to understand the data itself and begin really thinking creatively about the challenge. I start seeing the similarities and connections and piecing together the project first in my head, then “on paper” as we start sketching out solutions.
Once we’ve used this overview to decide what project we’ll be building – maybe an app, an IoT device, AR overlay, or some other exciting piece of tech – I get started with low-fidelity wireframes. After the layout is approved by stakeholders, I will include copy and some mid-fidelity wireframes. When those are approved, I will provide full-color, high-fidelity prototypes for final approval. All the information on the white board helps the full picture come into focus, and as we learn more, we can still add data to the board. Every decision is formed by the full scope of hard data and broad information, narrowed down as we move along and let our creativity fly.
Make it Simple
Having a process is all well and good, but let’s back up: there’s also the art of tackling data. While more information generally provides more refined results, sometimes it can seem like there’s too much data at the outset. When I approach a project that involves a ton of data, my first goal is to simplify it. Using the collected data, I pick out the repeated comments or phrases, because these tend to be most important to the customer. I like to focus on what the users are looking for – what is the problem they want to solve, what are the challenges for which they’re seeking answers? This information is usually hidden in the data; the correlations tell a story, and after being briefed on the project/goals, analyzing and interpreting the data usually reveals it.
It’s important to find these connections to help confirm what the customers want. Sometimes companies will move forward with launches without going through the supporting data. This can result in a product that doesn’t solve the challenge or isn’t right for the target audience. This can be costly, and no company wants to launch a product that doesn’t meet an actual consumer need.
On the contrary, highlighting these connections can put a large amount of data in those “a-child-could-understand-it” terms. Users are trying to accomplish X, but Y gets in the way. Users are getting Y but want Z. Simple concepts at the end of the day, but that’s the goal – cut through all the noise and give the people want they want, in a way anyone can understand. New technologies can help bring data sets together and be springboards for action. For example, an IoT device collecting sensor data can be designed to take action based on business logic – it’s just about knowing what’s valuable in the data that leads to the specific action.
By understanding this connection between the problem and the solution, and being creative about it, clients can make sure a new product matters to their users. Customers, in turn, will feel better that they’re being heard, and their needs are being met.
Follow the Numbers
Using data as my guide, my goal is to unlock an idea’s full potential. For instance, when I worked on a new Internet system that would allow banks and merchants to communicate with the U.S. Treasury, I leveraged the data provided by our client team, who gathered requirements and provided use cases. Thinking creatively, we worked towards updates to the platform that improved the experience for users. These updated processes made it easier for users in various roles to pinpoint the necessary data for their own tasks – an end result we could only reach by focusing on the full picture at the start.
On smaller, independent projects where I gathered data from various end users, I synthesized the research with affinity mapping, persona creation, storyboarding, and other methods. When I do this, I’m searching to find out what was most important to the users. What did they want, what did they need? Then, in the design placement and layout phase, I expanded with card sorting, screen activity testing, and A/B testing. This gave me results that showed how users were responding to the product – and helped identify what was (or wasn’t) working.
Taking these steps made the execution of wireframes and prototypes more effective. It also made it easy to take a creative approach to gathering and utilizing large amounts of data. By opening your mind to the user experience and working inward from a broad canvas, you can let your creativity lead you to better results – and avoid the missteps that come when you zero in on a solution from the beginning and working towards it without letting the data lead the way.
These are just some steps and thoughts on my process, but the central piece is something anyone can apply – data can go a long way, but it goes further the more you understand it, use it, and explore it with a creative mindset. This means taking those two sometimes independent instincts – the left-brain urge to analyze and understand logically and the right-brain urge to follow creative hunches – and letting them team up to guide you to an innovative solution that’s also supported by the evidence. Though creativity and data-driven evidence both open multiples pathways at the start, they work together to filter and strengthen the scope as your end result takes shape.
Finding a process that lets creative thinking and data-driven concepts go hand in hand is something we find very valuable at Go Studio, and I’m glad to be part of a team that’s driven to explore quickly but not haphazardly. What’s your process for making the most of data when building your projects?
