A dataset is considered to be longitudnal if it tracks the same kind of information at multiple points of time. For e.g. the marks of students over multiple years, patient health records over a period of time, etc.
The most important advantage of longitudnal data is that we can measure change and the effect of various factors over the data-point time values. For e.g. what is the effect a particular drug had on a cancer patient? The effect of different teachers on a student?
So essentially, longitudnal data helps in establishing cause-n-effect relationships. Longitudnal data stores are also being used for predictive modeling and other areas. Longitudnal data stores are very popular in the Life Sciences and Healthcare industry.
I am interesting in learning the best practices for creating and optimizing a data-model for longitudnal data stores.