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. 

What does it mean to “synthesize” qualitative data?

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.

Methods for synthesizing qualitative data

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:

  • Affinity Maps
  • Journey Maps
  • Spreadsheets
  • Sticky notes
  • 2x2 framework

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.

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:

  • Each note should include a single thought or a short snippet of data (quote, feedback, observation)
  • Let the notes inform the groupings, rather than the other way around
  • Names of the themes should be enough to thematically understand the contents of the group, without having to read each individual note.

Tagging/Coding Data 

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.

Create your own AI-powered templates for better, faster research synthesis. Discover new customer insights from data instantly.

How do we translate hours of research into actionable 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: 

  • What you heard & what you saw
  • What it means & why it matters

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. 

Create better, faster research insights with Notably

Notably, your all-in-one research platform, empowers researchers to seamlessly synthesize and present qualitative data with unmatched efficiency. In the synthesis journey, the canvas acts as a dynamic workspace, allowing you to employ grounded theory principles. Effortlessly drag and drop notes into emerging themes, creating a multi-dimensional understanding of your data. Uncover the essence of your research by visualizing these themes through various frameworks like journey maps and empathy maps, all within a spatially intuitive canvas.

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. 

Take your synthesis a step further with AI-powered insights. Even before tagging or theming, you may leverage Notably's AI Templates to generate insights inspired by trusted design thinking frameworks. These evidence-based insights provide a bridge between knowledge and wisdom, capturing the essence and story of your research effortlessly. Create your own custom AI templates or experiment with different combinations of tags and themes. Then, package your synthesized insights with AI-generated cover images, incorporating videos and quotes for a visually compelling presentation.

Notably transforms your research process, making data synthesis a seamless and insightful journey from start to finish. Plus, it is free to get started! Click here to sign up and run an end to end research project.

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