Tagging allows you to quickly, simply, and easily understand what's happening in your data at a granular level.
Your iterative process to identify key themes in your unstructured data
Tags (also known as labels or code-frames) are simply keywords or key-phrases strongly related to what is included in a dataset (e.g. a comment, a description, etc.).
Take for example, the statement:
I travelled to Australia last summer and enjoyed the amazing nature.
Good tags could be: Australia, Travel, and Nature
Applying tags to items in a dataset is referred to as tagging.
What comes with tagging?
- Group items so that those in with the same tag have meaningful similarities (i.e. specific features or properties).
- One-to-many assignment. Meaning, that an individual data point (e.g. response, document, comment) can be assigned to one or more tags. (If you would like a one to one relationship, see Clustering).
- Informed decision-making through the identification of different patterns. It is is a great tool to unravel hidden patterns in the data.
- Uncover unexpected and emerging themes without bias, by viewing the data without the need for an upfront, fixed taxonomy
- Drill down to identify the detail behind each tag, including what's driving each group, instantly
Updated 4 months ago