Conducting high quality, rigorous research is tough, regardless of how seasoned you are, because each research project is completely unique. In addition to actually doing the research itself, aggregating and organizing research notes can be overwhelming. Making sense of research data during synthesis and writing up a research report takes a lot of time. And if you don’t organize research notes and set yourself up for success early on, it will take even longer. You’ll miss out on important observations that will slow down analysis and impact the quality of research findings.
Taking the time to code and organize research notes is key to avoid feeling overwhelmed by the sheer volume of data. In this article, we’ll share some practical tips to set you up for doing high quality analysis and synthesis.
Re-Organize, Re-Group, Re-Compile: A method for making meaning out of mess.
You must be wondering – organize, group and compile make sense. But what does the ‘Re’ mean? This is a recursive approach to research. You cast a wide net to gather as many ideas and data points as you can when conducting research. Don’t filter the data or try to make sense of it prematurely.
This data-gathering stage is where you pull in qualitative data, like interview transcripts with direct quotes from a user interview analysis and/or observations from a user researcher’s notes. Only once you’ve collected all of data do you start analysis.
It’s useful to timebox synthesis to a day or two, depending on the size of study. Because of how fresh the data needs to be in mind, it isn’t the type of thing you can span over weeks. Ideally, this process can be done with a teammate, but it can also be a solo activity.
Break down information into smaller pieces of data that might become sub-topics, and then cluster that data into groups that display likeness or tension. Group and regroup that data to sharpen it and you’ll start to recognize recurring patterns or themes using a grounded theory approach.
Don’t think about it too much, these groups aren’t set it stone, so just go with gut. Later on, we’ll talk about how color coding and tags can augment you here.
Once the initial cluster analysis is done, you begin to dive deeper into the data. Your research hasn’t quite crossed the chasm to become anything meaningful quite yet, but you might start to sense emerging insights. During this messy middle stage of analysis, data still appears to be a bunch of disparate observations, anecdotes, and verbatims bunched into subtopics.
You may feel the need to do additional research as some points need to be elaborated further, or you want to add additional points. Continue to follow the above method again if you do bring in more data.
Using physical or digital research notes
This process can be done with physical sticky notes or digital sticky notes. Some researchers prefer working outside of the physical limitations of a screen and to manipulate and marinade with the data in person. I’m a big fan of the physical war room, but there are a lot of upsides to working data digitally. Using tools designed specifically for this process, you won’t lose track of where data came from and will save time otherwise wasted writing and manually coding sticky notes.
Whether you opt for physical or digital notes, continue to regroup data into sub-topics and then topics, until you feel confident with the higher level themes that are emerging.
Applying meaning to research notes with color and tags
Coloring and tagging, otherwise known as “coding” in research, are effective ways to organize research notes and assign meaning to pieces of data. They are helpful as you start to pull apart and apply different lenses to data during the synthesis process.
Color as a visual cue
Color can be a powerful visual cue to see how patterns distribute across themes. For example, using a unique color for each participant or persona type can reveal an interesting visual that becomes a nugget of an emerging insight.
How heavily are you influencing one theme by a certain persona type or participant?
You can also assign a color to sentiment and see how positive or negative emotions are distributed across or concentrated in a particular product experience or workflow. This too can be done with either physical or digital sticky notes.
Global versus project tags
You can think of tags in two buckets: global or project-based. Some tags will be universally applicable to any research, while others will surface during analysis and be completely unique to that dataset.
For example, you may decide to code data across all research projects with persona type, like “Parent” or “Teacher.” Or you may get more specific and assign it to a participant as well, like “P1” or “T2.” You might also decide as a research organization to adopt tags like “Pain Point”, “Motivation”, “Goal”, or “Need.”
An example of a tag that might organically reveal itself in the data would be “Inequity”, “Age appropriateness”, or “Student interaction.” Notice that these are much more specific.
You can code data physically on sticky notes by simply writing the tag in the bottom of each note. However, there are constraints to this method, like if one note should be coded by several different tags and fits into multiple themes. In this scenario, you can duplicate the note.
If this process of coding data sounds tedious and time consuming, it certainly can be. But it’s also important. Turning over every stone and marinating in the data is important to fully immerse yourself into the synthesis process.
Organizing research notes
Organize research into four sections in a research project: Info, Data, Analysis, and Insights.
The Info section serves as a space to document research plan and goals. It can also be where you document the global and project tags used along with their meaning. This helps the team stay on track and on the same page, as well as orient any stakeholders or coworkers to the project.
The Data section is where you organize raw research data, including written observations, video and audio recordings, photos, and more. This is where you start the process of coding data, highlighting important parts and tagging them with global or project tags. Each highlight turns into digital sticky notes on the canvas and a row in a table in the analysis section.
The Analysis section is where you begin making sense of notes. This is where you apply the method we discussed earlier of re-organizing, re-grouping, and re-compiling notes. In this workspace you can group data into “themes”, recolor data by different criteria, as well as use AI to run a sentiment analysis from notes. As you continue grouping and regrouping data, patterns will start to emerge which will inform research insights.
In the Insights section, you can begin to develop thematic takeaways from research. What does the data mean, and why does it matter? Each insight allows you to add evidence from data to support conclusions. This is especially helpful once you begin to button up research into a report, to then share with team and stakeholders. The thematic takeaways you discover through research allow you to know what future research needs to be done to expand on topics, which direction you may need to pivot to, and most importantly to develop a plan to better benefit users and customers.