The update that landed on the near twin
The right change was made to the wrong person, and your system is completely satisfied with that. Both records exist. Both are valid targets.
How it actually happens
You have a J. Martinez and a J. Martinez Jr. Or two branches of the same company. Or the same person who signed up twice years apart. Your automation matches on a name, or an email that got reused, and confidently updates one of them. It picks wrong.
Why nothing catches it
There is no error to raise. The record it found was a real record. The update it made was a valid update. Software has no way to know it wrote to the wrong human unless someone told it in advance what right looks like, and almost nobody has.
What it costs
Usually an afternoon of untangling, sometimes a lost account, and occasionally a disclosure problem if the record held anything sensitive.
How we test for it
We build invented customers designed to be confusable in the exact ways yours are: near identical names, shared addresses, recycled email. The correct target for each operation is written down first. Then we count how many landed right.
Find out whether this one applies to you
The fit check is free and written. Four short answers, no call, no files, no credentials. A person reads it and tells you straight whether this is worth checking in your setup.
Request a fit check or call (832) 966-3895
The other patterns
The retry that billed the customer twice · The stale update that reversed a correct state · The discount typed as 100 instead of 10 · The job marked complete when a step silently failed
All scenarios and amounts on this page are fictional teaching examples, not customer incidents.