Languages like 'R', 'Python', and MatLab (yes I use the old name) have these. They are useful.
One of the key ideas in LabVIEW is that the user needs minimal interventions to code a useful result. As more information is encoded in a data type there is more opportunity to make "hands free" code that "just works". I think these two data types can do that.
Primary data type in R
It is Array-like, but NOT an array
each column contains one measurement (row) on one variable
It acts like a list of vectors, but the vectors have the same number of rows, and indexing allows a return of all or subset from all columns
column types are heterogenous - they can be different
column types can be set. A column of 0 vs. 1 can be set as factors/binomial values or as continuous.
There are functions that data analysis folks do every day that are informed by variable type, so the function operating on the inputs doesn't need type specified because it is interior to the table. This means you can say "plot(mydata)" and if your data is set up well, the graph parameters are already specified and useful.
This is from Hadley Wickham, a very famous person in 'R'. He does great work, and his name has high brand value in data-analysis.
Uses the "data.table" package
is able to be screaming-fast (think roller-coaster) especially when used with the "split-apply-combine" approach to data analysis, and SQL-like operations.
is built for handling huge data (100GB tables) quickly and efficiently.
In many applications the same operation is not possible due to memory constraint or viable due to processor overhead can execute adequately (aka wonderfully) by using this data type on the same hardware.