Similar to how linear transforms can be represented as 2-dimensional arrays of numbers (that is to say matrices)[0], tensors are a higher dimensional analogue with a rich theory in their own right and a representation as higher-dimensional arrays of numbers. Similarly, if you look at a tensor solely as an n-dimensional array of numbers, it ignores important differences in the mathematical behavior of objects with the same representation. To give an example: Different parts of a tensor can behave differently under change of basis. [1]
[0] https://www.youtube.com/watch?v=kYB8IZa5AuE
[1] https://en.wikipedia.org/wiki/Covariance_and_contravariance_...