Length
5 min read
In my previous post, we explored the difference between observations, themes and insights. We highlighted how insights are derived from observations but offer much richer information that can drive action within your organisation. The bridge between raw observations and actionable insights is a very important process called ‘synthesis.’
We’re following that last post closely with steps on how to turn data into insights, with a step-by-step below for easy reference and a little checklist, so you know you’re on the right path.
But first: Why synthesis matters.
Synthesis is that moment where things click. It’s where the noise starts to feel organised and patterns emerge. And when done well, it can completely change how a team sees a problem, or even what they thought the problem was.
Customer research without synthesis is a pile of stories. It might be interesting, even emotionally compelling, but it’s not going to drive confident decisions.
Here’s the good news: getting to insight doesn’t have to be mysterious. It just takes a bit of structure and a lot of curiosity.
4-Step Process: Turning research observations into actionable insights.
So as promised, a short step-by-step guide that outlines a practical method to turn raw research into rich understanding. I’ll assume you’ve done your UX research already.
Step 1: Group research observations into themes.
Start by organising your notes, quotes, observations and qualitative data into clusters. This process is also called ‘affinity mapping’ and Neilsen Normal Group provide a detailed resource with more detail and images on the process. Having these in the same format (i.e. all post it notes) can help to bring various sources together. Define your clusters by:
- Similar behaviours or responses
- Repeated phrases or actions
- Shared moments of friction or delight
At this stage, you’re asking: What are we seeing more than once?
If something doesn’t fit with your existing clusters, simply give it its own group and see if it finds friends in your process. We often work horizontally and when there are 3-4 post it notes, we give that little cluster a title.
Step 2: Analyse and compare themes for patterns.
Now dig deeper. Take a look that those cluster titles and see what stands out when you hold themes side by side?
- Where are the contradictions?
- What’s missing?
- What’s surprising?
Move themes and notes around. Break big clusters into smaller groups. Make sure each theme is distinctly different to every other. You may want to bring in some first and second level themes to help organise your information. For example, a set of themes may be specific to a channel, or phase in a journey. So a first level theme maybe ‘uncertainty before deciding’ with second-level themes being ‘difficult to find details,’ ‘lack of confidence in information’ and ‘weighing up options.’
It’s ok if not every post it has a home. Move on to the next step when you feel confident patterns are emerging in your analysis process.
Step 3: Add organisational context to your themes.
This step is often skipped, but it’s crucial – you’re not analysing data in a vacuum.
Review each theme and ask yourself the following questions on the themes together:
- Where do the strategic priorities connect?
- What are the known constraints or enablers and how do these enhance or prevent themes?
- How are the themes shaped by values, brand, or customer promises?
- Are there any active projects impacting themes? How?
Context helps you turn interesting into useful.
Keep an eye out for tension – the disconnect between what people say and what they do, or between their expectations and reality. That’s where insight lives. Keep this in the back of your mind as you progress.
Step 4: Craft clear, actionable research insights.
This is where it all comes together. You’ll spend some time here crafting language and choosing the exactly right words. Aim to address each theme in the suite of insights you come up with – if you pick and choose then your themes are not reflective of your evidence based research. You are likely to cover multiple themes in a single insight.
A good insight is:
- Rooted in evidence
- Shaped by context
- Purposefully clear, focused and thought-provoking
- Actionable – it leads you somewhere new
Test your insight with a simple gut check:
“Does this explain why something is happening?”
“Can we do something with this?”
If not, keep refining. Language matters here — challenge every word. Strip out jargon. Insights don’t need to be poetic, but they should be sharp.
What makes a good insight meaningful and actionable?
The best research design insights have a bit of tension to them. They’re often both surprising and obvious – the kind of thing that people nod along to.
They:
- Shift your perspective
- Unlock a clearer path forward
- Stick in your head
- Are true across teams or department
Insights don’t end the conversation, they deepen it and allow you to move into the second half of the double diamond. They prompt better questions, more focused exploration and smarter decisions.
Don’t stop at the obvious.
Anyone can collect data. But the teams who create real change are the ones who take the time to make sense of it. They go beyond the what and uncover the why.
So next time you’re knee-deep in sticky notes or drowning in a spreadsheet, remember it’s not about sorting everything perfectly.
It’s about spotting meaning.
And making connections.
And finding the stories in the noise.
