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Interpretability

Some machine learning algorithms are easier to understand than others. These are usually easier to explain to others as well. For example, linear regression is well known and easy to understand and explain to potential investors of your company. SVMs are more difficult to entirely understand.

My general advice: if SVMs are highly effective for a particular dataset, try to increase your personal interpretability of SVMs in the particular problem context. Also, consider merging algorithms somehow, using linear regression as an input to SVMs, for example. This way, you have the best of both worlds.

This is really context-specific, however. Linear SVMs are relatively simple to visualize and understand. Merging linear regression with SVM could complicate things. You can start by comparing them side by side.

However, if you cannot understand every detail of the math and practice of SVMs, be kind to yourself, as machine learning is focused more on prediction performance rather than traditional statistics.