A practical guide to mastering data synthesis

So you’ve wrapped up your latest round of user research—interviews, observation sessions, maybe even a few surveys. You’ve got pages of notes, transcripts, and a few eyebrow-raising discoveries. Now what? 

You’re staring at a sea of qualitative data, and somewhere in there are the insights you need to guide your next big design or product decision.

This is where synthesis comes into play. It's the magical process of transforming raw, often messy data into something useful, actionable, and dare I say, insightful. 

But here’s the thing—synthesis isn’t just about slapping a summary on your findings. It’s about getting to the heart of what the data really means, uncovering patterns, and helping others see those patterns too.

In this blog, we’ll walk through how to master this crucial step in the research process and distill complex findings into focused recommendations that directly address your business or design challenge.

Why synthesis matters

First, let’s tackle the big question: why should you care about data synthesis?

Imagine you’re leading a UX team that just wrapped a set of user interviews. Sure, you could tell your stakeholders that users had trouble navigating a checkout page, but is that enough? What does that mean for the bigger picture? How does that shape future decisions?

Synthesis bridges the gap between research findings and action. It’s not just about sharing user quotes or observations—it’s about aligning those findings with your research goals and, more importantly, the strategic decisions that follow.

Here’s a quick gut-check: if your insights don’t provide clarity about what to do next, you haven’t fully synthesised them. The point of synthesis is to uncover the 'why' behind the 'what', giving context and meaning to the data you’ve gathered.

Immerse yourself in the data

Before you can find the magic in your data, you need to dive in—headfirst. This is the immersion phase, where you familiarise yourself with everything you’ve collected.

Immersion isn’t just about skimming your data. It’s about diving deep into the user stories, the quirks, the frustrations, and the “aha” moments buried within the details. Think of it as getting cosy with your data—you don’t just listen to what users say, you feel their experiences, and absorb their world view.

During this phase, you’ll start to see emerging patterns and subtle nuances that lead to deeper insights. The more time you spend truly understanding the data, the clearer those patterns become, setting the stage for meaningful synthesis.

To avoid overwhelm, start with these tips for staying organised.

• Tag as you go: Whether you’re using a platform like Askable, or sticky notes on a real or virtual whiteboard, categorise your data as you collect it. Highlight key themes, note contradictions, and tag emotional responses or critical pain points.
• Break it down: Organise your data into digestible chunks. If you’ve got hours of interviews, summarise key moments by theme or user behaviour.
• Use visual organisation: Try mind maps, affinity diagrams, or other visual tools to group and arrange data (see below for more detail). This helps you see connections that might not be immediately obvious.

Approaches to synthesis

There’s no one-size-fits-all method for synthesis, but here are a few tried-and-true approaches you can consider.

Thematic analysis

This classic data analysis method involves identifying recurring themes across your data. At its core, it’s about identifying recurring themes or patterns across your research—the ideas and experiences that crop up again and again in different user stories.

What makes thematic analysis so powerful is its flexibility. It’s simple enough to apply to smaller datasets but robust enough for more complex research.

It’s not just about categorising quotes, though. It’s about understanding what these themes reveal about the user experience and how they inform the broader design or product decisions you need to make.

Thematic analysis is great for spotting patterns in user behaviour or perceptions, but beware of confirmation bias. Make sure you’re letting the data speak for itself, rather than bending it to fit your expectations.

Affinity mapping

If you’re a visual thinker, this method might be your jam. Group related data points together, cluster by theme, and step back to see the bigger picture.

It’s a process of spatial reasoning that helps you visually identify patterns and relationships. It’s perfect for when you’ve got a ton of data from user interviews, surveys, or usability tests, and you need to sort through it without losing the human touch.

Plus, it’s collaborative. Affinity mapping works best as a team sport. Personally, I like to get my stakeholders involved in the mapping process—it gets them close to the raw data, and often sparks ideas and insights I wouldn’t have seen otherwise.

Machine learning and AI

If you’re dealing with a ton of data, machine learning can help identify patterns faster than the human eye. It’s not a substitute for human judgement, but a useful complement—allowing you to focus on higher-level insights.

You can read more about using AI tools for synthesis in our blog posts A human-centred guide to using ChatGPT for UX research, and 5 advanced ChatGPT prompts to streamline your UX research.

Supercharging your insights

Once you’ve identified themes or patterns, it’s time to rely on those insights to drive design decisions. But before you dive into report writing, take a moment to reflect. Are your insights truly representative of user needs, or have you fallen into a bias trap? 

This is where reflexivity comes in. Reflexivity is your internal check on bias, a reminder to step back and examine your assumptions. As researchers, we bring our own perspectives into the room, and it’s crucial to reflect on how these might be colouring our interpretation of the data. 

Are we unconsciously leaning towards conclusions that fit our expectations? Challenge your assumptions, look at your data from different angles, and seek feedback from your team.

Reflexivity invites us to revisit our data with fresh eyes, to question the filters through which we’re viewing our research. By practising reflexivity, we strengthen the validity of our insights, ensuring they truly reflect user experiences rather than our own preconceptions. 

My favourite tool for this is collecting hypotheses from stakeholders before fieldwork starts. What do we think our users want? What attitudes, behaviours or needs do they hold about our brand or product? 

When you have these hypotheses in writing, you can move into research with clarity around your biases and the expectations of stakeholders. Then, you can report back on whether hypotheses were validated, challenged, or inconclusive. Hypothesis playback sessions can be a lot of fun—I’ve even run interactive ‘myth-busting’ workshops at strategy offsites.

Another approach, which is one of my personal favourites, is co-design. To bring customers, users, or stakeholders into the design process to validate your findings and co-create solutions. Not only does this enhance the depth of your insights, but it also fosters a collaborative atmosphere where everyone feels invested in the outcome.

Triangulation refers to the process of using multiple sources, methods, or perspectives to gather and analyse data in order to increase the credibility and validity of your findings. 

Synthesis becomes richer when you bring in multiple data sources—combine user interviews, survey data, and usability tests to deepen your understanding and grasp a well-rounded view of the problem you’re trying to solve.

You can read more about triangulation in our blog post—How triangulation turns research insights into actionable recommendations.

Co-design takes collaboration with users to the next level. Instead of keeping users at arm’s length during the design process, you bring them into the fold, working alongside them to ideate and prototype. 

It’s a more democratic approach to design, grounded in the belief that the people who will use your product should have a hand in shaping it. 

Co-design not only fosters a deeper sense of empathy, but it also leads to more robust solutions—ones that are more likely to resonate with users because they helped create them. Plus, it creates buy-in from the very start.

Bringing your insights to life

Here’s the thing: no matter how ground-breaking your insights are, if they’re not communicated well, they’ll fall flat. 

Storytelling is the glue that makes your insights stick. Remember, people are more likely to remember stories than statistics. Frame your insights in a narrative that leads stakeholders from the problem through to the solution, weaving in user quotes and real-world examples to bring your data to life.

Whenever I’m presenting insights to clients, I like to start with a story. Like the time an academic researcher had a box of live crickets delivered to their workplace, but had no idea where on an enormous university campus those crickets ended up. 

It’s one thing to report on data—it’s another to craft a compelling narrative that brings those insights to life. Good storytelling draws your audience in, helping them connect emotionally with the research findings. 

When presenting to stakeholders, it’s the difference between a dry report and a story that moves people to action. 

Focus on user experiences and real-world examples, framing your findings within a narrative arc that takes listeners on a journey—from the problem to the insight, and finally, to the solution. It makes data relatable and memorable.

Visualisation transforms your findings into something digestible and actionable, making complex insights easier to understand at a glance. 

A picture is worth a thousand data points—literally. The beauty of a well-crafted visualisation is that it can communicate the depth of your research without overwhelming your audience. 

It’s about balancing clarity with insight, ensuring your key takeaways are obvious while also leaving room for deeper exploration. Good visualisation doesn’t just support your story, it becomes part of the story, helping your audience engage with the data in a more meaningful way.

Think journey maps, personas, and infographics—visuals that tell a story. Just be sure your visuals don’t overwhelm or confuse—keep them clear, clean, and directly tied to your narrative.