Tags are an important part of performing advanced analysis and capturing themes from project data. These are commonly used by researchers and designers alongside fields to gather in-depth findings from a single project. In this lesson, you will learn ways to create and use tags to analyze data in projects and make the most of magic highlight with effective tag descriptions.
Tags help you connect the dots between important moments surfaced as highlights, either within a single piece of data (like an interview) or across multiple pieces of data (multiple interviews) that make up a whole project.As tags can be used to thematically group bite-size information captured within your data quickly, we recommend starting simple and giving them a title that captures the theme of the highlighted text.
When it comes to analyzing qualitative feedback, the process of tagging your data requires careful thought and can sometimes feel like a detailed, time-consuming task. This often leads to a crucial question: Is the effort of tagging really worth it?The answer, as with many aspects of research methodology, is: it depends. The value you get from tagging hinges on four key factors: your dataset, your current process, the tags you choose, and the amount of time you’re willing in invest in managing these. Evaluate your current circumstances to determine whether this method of analysis is right for you.
By default, tags are local to a project, meaning those tags will only appear as options for the single project they are connected to. Anyone with Full or Edit access to a project will have the ability to create and manage the tags for that project.
If you are looking to apply a top-down approach to tagging, you can also create individual tags, install a Dovetail project template to customize or import existing tags on a board before highlighting your notes. This is great for standardizing your tags for any kind of research including usability testing.You can view and organize tags into groups on a board under Tags. On this board, you can define groups and color-code related tags. You can also move tags across groups, merge related tags together, and cluster your observations into themes that can be elevated and talked to within an insight.
You can improve the quality of our magic highlight feature by adding descriptions to your tags. What makes a “good” tag description will vary depending on the tag and what you are looking for but there are, however, some general guidelines and recommendations you can follow.
Use the bare minimum required words
If you can say it in three words, use three words instead of a sentence. A guideline is to try and not use any more than 50 words.
Repeat the same word
Do not use different words for the same meaning, even if they are synonyms.
Be complete with your information and explanation
If a new intern wouldn’t understand the term, assume AI wouldn’t either.
Use examples if necessary
Provide examples of what to do/what to know rather than examples of what not to do.
For this, use something concise, or bullet points. A bad example can throw the LLM off track.