This can be likened to data in Excel versus data in Word. In Excel, we have data organized in rows and columns, which makes it easy to apply formulas and calculations and create clear graphs. In Word, however, we work with dynamic text, which requires a different approach to analysis.
Structured data is collected where respondents are asked specific questions with specific answer options, often on a scale where respondents make a conscious choice. For example, it could be a question about simplicity where the respondent is asked to rate the subject on a scale from 1 to 5, as Brilliant often uses.
The results of these types of questions can be easily sorted, visualised, and interpreted through graphs and diagrams.
Unstructured data is collected when respondents answer open-ended questions in the form of text comments (and/or audio). This is common as it is an effective way to collect qualitative data from many respondents simultaneously. For example, it can be linked to an NPS question where the respondent is prompted to motivate their response in written text.
Open-ended questions usually generate large amounts of text data that are difficult to sort, interpret, and visualise in a clear and efficient way. Instead, a very time-consuming task arises to read, interpret, sort, categorise, and analyse the texts in order to draw relevant conclusions. This is where text analysis comes into play.