Event sourcing is most powerful when the data necessary to recompute the state is small but the amount of state you are potentially interested in subsequently accessing is large, most of which is computed.
It is also incredible useful for financial applications where risk models (credit risk, market risk, fraud, etc) need backtesting. If you can't read the time-ordering of state events directly, a Risk/Data Science team spends an inordinate amount of time reconstructing time histories of events inferred from a mix of static db tables, audit logs and periodic data warehouse snapshots.
It is also incredible useful for financial applications where risk models (credit risk, market risk, fraud, etc) need backtesting. If you can't read the time-ordering of state events directly, a Risk/Data Science team spends an inordinate amount of time reconstructing time histories of events inferred from a mix of static db tables, audit logs and periodic data warehouse snapshots.