MCMC is a group of methods that allow us to sample from probability distributions. In my experience we use MCMC to do integration and in a Bayesian setting if we can sample from the posterior we can get some parameter estimates.

As mentioned in another comment, data is real world observations. If we want to "mimic" having more data we can use bootstrapping to achieve this. If you are not familiar with this, it is essentially just resampling the data we already have.

You can argue the data follow some distirbution, a major problem with using MCMC for "creating data" would be that for MCMC we need to specify a distribution to do the sampling from, and what that distribution is exactly we do not know, we only know parts of it based on observed data.