Why is Functional Data Analysis (FDA) not as popular? I am interested in FDA (data perceived as functions), as someone from a pure mathematics background and I think it can help provide solutions to some major challenges in data analysis (also data science), compared to the ordinary perspective in which data is perceived, as vectors. However, I have not found so much literature about it on the web. Could someone help me understand why this is so? Also, where can I get some good reads in FDA?

FDA was late to the party and benefits against established applications are at times incremental. In many cases, "standard longitudinal data techniques" check most boxes already. FDA does provide some unique advantages on specific use cases (e.g. working with density functions or covariance surfaces as the unit of analysis) but those are specialised and rather recent applications.
