Ever wondered what you should look for when you’re transforming design research data into insights that lead to new opportunities for your project?
Raw research data and insights
When you’re doing design research, you’re gathering a lot of raw data like photo’s, video’s, interviews, quotes, etc. The next step is transforming this data into insights.
Whenever you’re researching a topic, you do this with a goal in mind. You want to learn about something or you want to solve something. Raw data from research is your input and the insights are the result. Insights will point you towards solutions. Insights are not solutions, but they are the stepping stones towards your goal and ambition. The goal of an insight is to uncover new opportunities. Insights are about small things that open up a lot of possibilities.
Example: In a project we found that if you put people in a uniform, they immediately show different behavior. So something very small, like putting on a uniform, can completely transform behavior. That’s a small insight that can lead to big opportunities.
Understand your data
Once you’ve gathered your data, the first thing you should do is lay it all in front of you. Have a good look at it yourself and ask questions like ‘What do I see here?’ or ‘Why is this happening?’. Here you are trying to make sense of your data. You are trying to understand your data.
Example: For instance, we did a photo study of our town, following tourists. It turned out that there were a couple of spots recurring throughout the photos. What we saw is that tourists tend to stop at certain locations to photograph iconic sights of the town. That was something we learned through the photos that we didn’t know before.
Get different perspectives
After analyzing the data yourself, make sure you don’t keep it all to yourself. It’s a good thing to let other people have a look at your research data as well, because this will give you a broader perspective.
Keep the goal in mind
When you’re doing design research, you usually have a bigger goal in mind. An ambition or something you want to solve or research. When you’re transforming your research data into insights, it is also important to keep this goal in mind. It might be obvious when you’re gathering your data to keep looking at your goal. But it’s also important when your analyzing your data.
Example: Like the the photo study of tourists in a town. We weren’t looking at the architecture. We were looking for clues that would help us towards our goal. We were looking at our data with a certain mindset.
Don’t be afraid to state the obvious
When you are analyzing research data, it’s very smart to name everything that you see. Even if it might be obvious to you. Because something that’s obvious for you, might not be obvious for someone else. When you’re working in your team, everyone should be able to state what’s obvious to them. There will always be things that will surprise you. Something you may not have thought of or hadn’t seen. So starting by stating the obvious is a really easy but important step.
Look at your research data, like it’s the very first time
The hardest thing with raw research data is to look at it and seeing new things. Especially when you’ve been in an industry for the last twenty years, or when you have a lot of experience in a certain field. When you are looking at situations that you’ve seen over and over again, it’s hard to spot new things. It’s hard because you always have an explanation for everything that is happening. ‘He’s doing that because …’ or ‘We can’t change that because …’. That’s why you need different people looking at your data from different perspectives. The key with going from raw research data to insights is trying to look at your data like you’ve never seen it before. Be curious and ask ‘Why, why is this happening?’.
Look for patterns
When you’re looking for insights, our best advice would be: don’t get overwhelmed! Don’t get overwhelmed by the amount of raw research data that you’re going to have in front of you. Don’t panic. Just lay it out and try to get an holistic overview. Look for patterns. What are curious things that are happening, and why are they happening?
What is your strategy for analyzing design research data? Let us know in the comments.
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