(1) Slice your data, and check metrics in each slice
(2) Check for consistency over time, which is actually a special case of (1)
This is a type of as hoc bootstrapping. If your estimator is stable over various subgroups then it implies the estimator variance is low and you can be more confident in an observed effect.
Or (in more pragmatic and plainer terms) it's a way of seeing that there isn't a huge gap or disjunction in the data due to some IT related event such as a data migration, someone introducing a bug into the feed that's been unnoticed or a deeper failure in the sensors that are providing the data.
(1) Slice your data, and check metrics in each slice
(2) Check for consistency over time, which is actually a special case of (1)
This is a type of as hoc bootstrapping. If your estimator is stable over various subgroups then it implies the estimator variance is low and you can be more confident in an observed effect.