In our ML, data is analyzed in time buckets (of width equal to
bucket_span). When looking at the results, they are also presented in the context of a time bucket. Depending on the specific configuration of the job that you've created, the anomaly records that you'll see will have information about what was unusual (and by how much). It won't show you the particular elasticsearch document that was the culprit, because it may not be just one - it could be many documents within that
If you desire to view the raw data that is relevant during that window of time, you can use the "custom URL" functionality to specify a destination (such as the discover tab of Kibana) where you can view the raw data during the time of the anomaly (and optionally filtered by the specific "influencer" that ML may have identified).
For additional information, the following blog may be of interest: