Simplicity is one appealing property. Writing a query to find data problems is simple. Give the query to Isok and use it to manage your problem resolution process.
Do you ...
Import data into PostgreSQL to be cleaned up later?
Clean up database content over time?
Allow or dis-allow specific data patterns on a case-by-case basis?
Monitor data for changes, or for unusual but not dis-allowed conditions?
Not want to write data validation apps or find triggers unsuitable, or too much work?
Isok may be for you if you are involved in data cleanup, or data integrity maintenance, or don't want to put your data monitoring into an app or otherwise design something, or are tired of re-examining the "problems" your queries report that you have determined are not really problems.
Isok can help you manage your data's integrity, especially when little technical effort can be spared or manual review is involved.
If you can write a query to find problematic, but sometimes allowed, data, Isok will show you only those problem cases that you have not accepted as valid. You don't have to repeatedly re-review query output.
If you want to manage your data cleanup over time, Isok can help ensure that newly added data is "cleaned", while scheduling the cleanup of old issues.
If you can write a query to find problems in your data, don't want to engineer anything more, and want a system to track and manage the problems discovered.
Discover problematic data patterns, track them, and manage them, by reporting not only the existence of particular data patterns, but also by tracking changes to patterns of data and managing issue resolution. Resolution may involve accepting questionable data, unchanged. Isok is especially suited when importing “dirty” data into PostgreSQL for cleanup and analysis, and for corner cases where business logic is “fuzzy”.
There can be a use-case to monitor for, and manage, outright errors in data, when you don't want to use triggers or, especially, constraints, for this purpose.[1]
[1] Triggers and constraints are the usual data validation methods, because these prevent erroneous data from getting into the database in the first place. But you may need all data, “valid” or not, to be in your database, or you may have other reasons why triggers or constraints are not an appropriate approach.