It is the joint probability of the data observed. This can be used to estimate parameters (Maximum Likeihood), including the population mean. The value of the likelihood itself has no interpretation other than the joint probability of all the data. It does not imply the final observation in a series is improbable. That would be equivalent to saying that you are improbable because you could only be the result of a single sperm from your father and a single egg from your mother.
Likelihoods are also used in Likelihood Ratio tests, the ratio of likelihood for two models for the same data. Bayes Factors adjust this for a prior assumption.
There are other difficulties with your calculation: it assumes IID events, ignores selection, ignores “indirect” routes, and ignores deletions. Outside of very specific types of data (phylogenetic analysis). It lacks any definition of “design” that would allow the likelihood of design to be calculated for a likelihood ratio test*. We can force a Bayes Factor by making very silly assumptions, which it the closest “valid” interpretation for what your calculation actually means.
* With the possible exception of a Dependency Graph model (Ewert 2016), but this has other problems.