Create a spreadsheet with columns for Audience, Research type, and Insights.
This allows you to organize your research and review insights across different research types, leading to more in depth analysis possibilities.
List all of your data and its sources, as well as any metadata you might have gained from user research and screener questions.
For example, keeping track of metadata like the job title of participants allows you to analyze how your product or research questions are answered differently at different levels of the organizational hierarchy.
Codify and tag your research data to identify common patterns.
Use a software solution like Dedoose, or create a physical system using sticky notes.
Write down quotes, observations, feature requests, and other insights from all of your user research types and queries on individual sticky notes.
Create some preliminary group labels to seed ideas about categories or patterns in your data.
Have team members begin posting their sticky notes by grouping together related ideas based on how they have been coded.
Iterate on your model and framework as needed, labeling the groups and iterations if desired.
This allows you to find patterns emerging from an initial grouping. For example, you might not notice that most feature requests in a certain area come from people with specific job titles until you have grouped all feature requests together.
Synthesize data from multiple sources and data collection methods to verify patterns and signals, and increase the validity of your insights.
The synthesis framework can help you prioritize insights as well as validate them. For example, if multiple types of research unearth similar insights, or back each other up, those insights become more valid and urgent to act on.