Bad Data can change sides

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I had blogged earlier on the one the issue of the idm one liner I came up with: “Your manual processes are as good as the errors they produce”. Often when my colleagues and I are embarking on an identity management project we become intimately acquainted with our clients data and manual processes. It is not uncommon for us to find ourselves in a situation where we don’t have good data to work with when doing Identity Mapping. What I mean by good data is: Having user records across target systems that match up on a unique ID across the systems. Think of this as a universal Employee ID number. Now if everyone did this Identity Management integration work would be simple and straight forward. But more often than not integrators have to bend over backwards to figure out how to “map” these records. Let me give you an example. Imagine having 5 Roberts in your Human Resources data (usually your authoritative source). While they may have unique last names, many people with common first names might have common last names i.e. Robert Thomas. Secondly even these two fields in an environment where there is bad data will have multiple spellings of the name. So Robert can also be Robbie, Rob, R-dizzle. Same goes with Mike, Michael, Mikey etc. If a unique ID number was used across systems these minor spelling mismatches wouldn’t matter because you would just look for a matching ID.
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