15 Minute Read
For researchers, no matter what experience level, synthesizing data can be a long and tedious process. Thankfully, there are tools that can help speed up the process, to get to better insights, faster.
To better understand the synthesis process, this blog post will be broken down into smaller pieces: what is synthesis, affinity mapping, tagging, and writing insights.
Whether you’re a beginner or a research pro, this is for you.
At a high level, synthesis is the process of discerning meaning from a data set. This allows researchers to turn information into knowledge and to make sense of the “messy middle part of research."
Synthesis is the sense-making that happens between design phases. Researchers should continuously be synthesizing data and content, but formally, this effort usually happens after research is conducted.
Depending on experience level and project size, the synthesis process can take a few hours to a few days to complete. However, it is arguably the most critical step in the design process.
There are lots of different methods and frameworks for synthesizing data that can be added to a research tool kit. When choosing a method, choose whatever framework feels right for the data set.
Here are just a few examples:
It is important to remember that these are all human techniques and approaches to make meaning out of data. They are not always logical techniques, but the goal is to create quick, repeatable processes. Let’s take a deeper look at a popular method: Affinity mapping.
Creating an affinity map is one of the simplest and effective activities for synthesis.
It is a unique opportunity for collaborative research. Affinity mapping is an organizational process of discerning explicit and implicit relationships between data.
These meanings can sometimes only be uncovered by relating discrete pieces of data to one another and positioning them near each other. It’s an attempt to identify patterns by combining and grouping data either by logic or intuition.
The output of affinity mapping is more about the conclusions of the data. The process of affinity mapping and creating consensus helps build that spatial understanding of the data itself. These thematic takeaways or patterns of data that start to bubble up, start to indicate what emerging insights may be in the data set.
Some tips for affinity mapping:
Tagging is another helpful method when synthesizing your data because tags can assign meaning to pieces of qualitative data. When we talk about coding in this context, we aren’t talking about coding software or engineering, we are simply talking about creating a system to organize the data that has been collected.
Code and organize research notes in order to trace back to the original data source (ex: participant 1) or to assign meaning to tags to analyze across a larger data set (ex: pain point, needs, desire, opportunity).
Two buckets to think about when deciding on tags can be: Macro and Micro.
A Macro tag is good for zooming out. These are codes that are agnostic to the set of data being used within the framework, so researchers may call on these types of tags to use in every single one of their research projects. Such as: participant, persona ,and customer journey phase.
A Micro tag is good for zooming in. These codes are often specific to the set of data and the specific research goals for the project. So in this scenario, a researcher may use a tag to represent a particular feature in their product if they’re doing user research. Or, maybe the steps in a user flow or customer experience.
Use AI-powered research analysis to create better, faster research insights.
Writing actionable research insights is really the buttoning up of the research project. It is both scientific and creative. The majority of the project a researcher may spend their time being more methodical, but this is the time to be more subjective, intuitive and utilize storytelling.
Crafting insights requires confidence in the research, and acting on an informed hunch. Researchers get to create a complete view through the lens of their participants, and storytell around the themes they observed in the data. Insights should present an opportunity or potential for the future, but not necessarily the solution itself.
To form an insight, combine:
It is important not to confuse an observational statement with an insight statement here. If researchers stay too objective with an insight statement and don’t dig deeper into the research, asking why and peel away at the data, they will only get surface level insights.
A good way to think about crafting an inspiring insight statement is to combine how people think and feel with why they may feel that way, and transform it into an actionable, desired outcome.
If the synthesis process seems overwhelming, let me introduce Notably. The tool to help researchers create better insights, while speeding up the synthesis process.
Notably is a data-driven platform used to collect, analyze and share research. Inside Notably, researchers can organize projects, track participants, and synthesize data in a split screen workspace. With sticky notes, or a spreadsheet, teams can collaborate on research in one centralized location.
Each highlight or “note” from the raw data is connected back to the source, the participant’s voice or context is never lost. On the canvas data can cluster and themed, while staying in sync with the spreadsheet, to notice patterns as they are emerging.
Now with a brand new AI feature, you can generate insights from themes with the click of a button. Using AI, you can summarize all of the notes within your theme as a starting place for your research takeaways. Once insights have been crafted, notes can be directly added to as evidence. This allows the participants to directly support the research findings. From here, a link to the insight can be shared with a wider team in the organization.
Plus, it is free to get started with Notably! Click here to sign up.
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