“What if we learn what we already know?”

“What if we learn something we don’t want to know?”

“What if we don’t learn anything?”

These are some of the questions in the back of our minds when we kick off new research projects. The anxiety-inducing power of these questions withers with experience, but the questions themselves linger.

The intimidation of a blank canvas is something most designers, and creatives in general, are used to contending with. Research, however, seems to have its own special kind of intimidation factor. Perhaps because, with the learning that comes through research, there is so much about the outcomes of research that you can’t control. It’s unlike most professional endeavors. Usually you’re responsible for creating or building something using your skills or wit, something you choose, build, and mold until it’s right. The truth you’re after with research isn’t like that at all.

What is research synthesis?

In research, this step of truth-seeking or  “making sense of mess” is called synthesis. And while teams are always in some degree of synthesis, formally this happens between design phases and usually after research is conducted.

We synthesize research data to make it meaningful, to turn information into knowledge. 

Truth-seeking is a kind of a loaded endeavor these days. We live in a complex world. One with bias, nuance, and privilege. And while it’s true that as a user researcher you don’t control the outcome of research, you do control the synthesis process. Here the choices you make, the methods and tools you use, and the perspective you bring (or don’t)… all influence the outcomes of research and its integrity. 

There are lots of different methods and activities for research synthesis. Some examples include:

  • journey maps
  • affinity maps
  • empathy maps

These research methods become your toolkit to combine what you saw and heard, the objective, with what you think and feel, the subjective. 

By using a good framework and data practices in synthesis, you can see exact moment inferences are made, where bias might be recognized, and where the lens of the researcher is used to create a new perspective.

The output of research synthesis is what you believe to be true, an emerging insight… which can go on to inspire deeper understanding and action. 

Research synthesis method spotlight: affinity mapping

Affinity mapping is one the simplest and most effective activities for research synthesis. It is an organizational process of discerning explicitly and implicit relationships between data. It’s an attempt to identify patterns by grouping together data either by logic or intuition.

This is an example of an affinity map.

The outputs of an affinity map are themes. Themes are hidden patterns that emerge through an affinity between data points. Themes bubble up into insights statements that teams can use during ideation.

Here is an example of grouping data together based on logic or intuition into a theme.

A few tips for affinity mapping:

  • Each note should include a single thought, ie a short snippet of data such as a quote or an observation.
  • Use a system to code your data so that you always have an attachment (we call this the red thread) back to the original data source. This data practice is good for quality assurance and future context-building.
  • Affinity mapping is inductive, meaning you let the affinity of the digital sticky notes inform the groupings rather than the other way around. For example, don’t start with a group of patterns in mind and then cherry pick the data to fit those groups. Let the clusters emerge naturally, and then check them against whatever hypothesis you might have had to begin with.

3 tips for research synthesis

What does successful research synthesis look like? Like most good questions, the answer is: it depends. 

Research synthesis will be different for different scales of research projects, across teams, methods, and tools. Below are a few tips for setting up your research projects for successful synthesis:

  • Don’t set out to “validate ideas” - We met with a research coach recently who was on a mission to ban these words from his team’s vocabulary, and for good reason. Validating ideas can be an aspect of research, but it shouldn’t be the goal. If validating ideas is the goal, there will be a silent and powerful self-fulfilling prophecy at work from the very start. 
  • Have 3 - 5 clear questions - These user research questions aren’t necessarily the exact questions you ask participants (though they might be). More so they serve as internal goals that should inspire the nature of all of the questions, the synthesis framework you choose, and how you focus your insights.
  • Control the controllable - While you can’t control where your data leads you, there is a lot you can do to put quality control around your data set and synthesis process. For example, by using a good audience screener, you can be sure you have an equitable distribution of participants. By tracking your qualitative data with a quantitative lens, you can observe interesting trends such as certain participants having more data points than others. Spend some time up front identifying where you can control the quality of your dataset, how you’ll identify potential quality issues, and keep those aspects in mind as the research progresses. 

Why it matters

Research synthesis is the stage of research that is often the most tedious and, for some, least understood. Even if you, as the researcher, have a firm grasp on research synthesis, it's safe to say most of the stakeholders in your organization won't.

Intentional and explainable research synthesis can go a long way in helping your research be accurate, trustworthy, and impactful. If you can unpack an insight by walking someone back through your synthesis process and actually inviting them into the learning experience with you... your insights and research will have a much bigger impact.

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