To offer a bit of theory, CNNs work well for many image tasks because they process spacially local information, without much care for absolute position. Essentially, every layer chops every image up into tiny crop images, and do an analysis step on the crops. The simple questions of "is this a line... corner... eye... face?" can be asked equally of every crop.

This means that the network only needs to learn once to detect a feature, rather than separately learn to detect that feature in each possible location it might appear. Therefore we can use smaller networks that train faster and need less data than if we had a fully connected architecture.

To return to the question, you could expect a CNN to work if your data is similarly spacially correlated. Put another way, if finding a pattern around cell x, y means the same sort of thing as the same pattern in cell a, b, then you are probably in luck. On the other hand if each column represents a meaningfully different concept, then a CNN will be a poor choice of architecture.

Images and numerical 2D data are the same thing. Having that, a CNNs viability depends on if the input data has a reasonable number and density of 2D features. If row 1 of your input data has no relation to row 2, a CNN is not a good choice. – Recessive – 2020-03-05T02:41:12.453

Of course, you can do it, but here's a new problem - how to interpret the results of the predictions? – Leox – 2020-03-05T16:46:15.477

There should be spatial correlation between neighboring elements. If in your matrix neighboring elements are (almost) independent 2D convolution is useless. – mirror2image – 2020-03-12T06:18:40.940