Using Dovetail to document insights
The design team uses Dovetail to synthesize and document research insights from user interviews, usability testing, and more. Previously, design research data, synthesis, and research reports were scattered across different tools, making it difficult to quickly track and search past findings, especially cross-team efforts. As we scale our product teams, customer base and invest more in more frequent user research these problems become harder to solve. We use Dovetail to make sure that our research data, both raw and synthesized, is located in one place, ultimately making it easily searchable, shareable, and scalable across research projects and disparate teams.
Getting started with Dovetail
- Learn more about Dovetail via their demo page here.
- Learn more about how to use Markup here.
- Check out how Dovetail has analyzed and organized data in one of the projects in our “Example data” folder, like this one, “Airline seat reviews.”
Starting a new project
Creating a project
TBD At this time, projects have already been created for all research in progress. Please do not make any new projects at this time. If you feel a folder does not exist for your current research initiative, please reach out to @megan.standrew.
Updating the project readme
Each new Dovetail project automatically creates a new readme with a high level research plan template. Update this template when you begin the project with the following information:
- A link to the GitHub issue associated with the project
- The associated research plan
Importing raw data into Dovetail
Just as each new Dovetail project automatically creates a new readme, it also automatically creates a “Note grid” view in the left side panel. Here, you can update raw data in text, video, audio, image, or file format as a note. Uploaded videos and audio can be transcribed using Dovetail. Learn more about notes and how you can use them to import raw data here and here.
Analyzing raw data using Dovetail
Tagging data in Dovetail
Once you’ve imported your raw data, you’re ready to start highlighting and tagging to begin surfacing patterns. Tagging is fundamental to how we use Dovetail as it helps us identify, track, and organize patterns across all our qualitative data. Learn more about how tagging works here.
Global tags
Currently, we have a set of Global Tags that can be used across projects. These global tags provide consistency in our cross-team synthesis. Global tags are currently maintained by the design team here. Global tags are organized into five categories:
| Tag Category | Description |
|---|---|
| Persona | These tags can be used to highlight key aspects of the user’s identity that can help us better define personas or tracks motivations, pain points, etc. against other tags. |
| User Feedback | Used to tag what a user says about the product. This category also includes generic options (A through D) for when we must multiple designs against one ano |
| User Emotion | These tags are can be used to tag a user’s attitude or reaction. |
| Workflow | Used to tag steps in the user’s workflow, especially in the context of usability testing. This category also includes generic options (1 through 10) for when we want to analyze data by task. |
| User Flow and Tools | Use these tags to identify steps of the user journey as well as tools that are part of that journey. This tagging system is especially helpful for more generative research |
These global tags may help you sometimes but not all the time. As a good rule of thumb, we recommend using global tags about 50% of the time.
Creating your own tags
Each project has its own unique set of goals. That means synthesizing research may vary greatly. In order to account for this, we recommend you create your own tags as necessary to help you better synthesize. To create your own tags, go to “New tag board” in the left side panel.
Some other tagging best practices
Note: This section’s information is borrowed from GitLab’s Dovetail Tagging Best Practices. To properly manage research insights within Dovetail, here are some do’s and don’ts when creating your own tags.
Do’s
- Tag the data while it’s fresh in your mind
- Tag your data immediately after conducting the sessions, or after re-reading your transcripts. Having everything fresh in your mind will make themes more clear.
- Align your tags with your research hypothesis
- The goal of each tag is to link your user data to your research goals. Each tag should be directly related to one of your research hypotheses.
- Be consistent
- When you identify what tags you will be using, stick to them. The more consistent our tags are, the easier it is to find trends in our data.
- Less is more
- It is better to have 5 tags that you are confident in than 10 tags you aren’t. As a guideline, try to limit most studies to less than 15 tags.
- Think about how they’ll be used
- Assume that someday, someone other than yourself will use your tags to identify similar insights. Make it easy for them to do that.
- Take a second look
- After making your tags, take a small break and then read over them one more time.
Don’ts
- Do not use full sentences
- A tag should be 1-3 words long. Using multiple different tags will result in more useful insights than one longer tag.
- Do not use emojis
- Emojis are naturally more ambiguous than text, and tags should be as clear as possible.
| Poor Tag Examples | Better Tag Examples |
|---|---|
User is confused by navigation and fails the task | Confusion, Navigation failure, Task failure |
🆕 Features communicate the problem being solved and value to a new use | Positive Value |
Lack of clarity for some users | Confusion |
Exploring data
Once you’ve analyzed data via tags, you can further synthesize and explore using a variety of Dovetail features.
- Fields: When you open a note, you have the option to add and edit fields related to the note as a whole. You could think of this as a way to tag the entire piece of qualitative data. Learn more about fields here.
- Views: Create a new view in the left side panel to display your information in a new way. Note that each view has multiple layout options (table, canvas, board, etc.).
- Filters: In each view, you can use filters to display only certain tags or fields. This can help you more quickly identify patterns.
Generating insights
Now that you’ve managed to analyze and explore the date, it’s time to surface actionable insights (the reason you’ve been doing the research the whole time!). For consistency, make sure to create a new “Insights” view in the left side panel.
This section is currently a work in progress