The Five Elements of Data Quality
0 Comments Published by John Dillard on Wednesday, August 16, 2006 at 10:04 PM.
Identity Management is predicated on the availability of accurate data to determine who a person is, define their relationship to the organization, and what systems, applications, and assets they are entitled to have. Without accurate user information, realizing the benefits of Identity Management is impossible. Big Sky suggests that all identity data should be evaluated and rated against the following 5 criteria:
| Element | Question to answer |
| Accuracy | Are the data correct? |
| Completeness | Are all the required identity data stored for each user? |
| Timeliness (i.e. latency) | Are data updates occurring promptly? |
| Relevance | Have we gathered all the data that is relevant to what we are trying to accomplish? (e.g. roles-based access control) |
| Availability | Are the needed data being collected as part of our processes? |
Looking at your data across these 5 elements provides a straightforward check to ensure that any inconsistencies or omissions are identified. Process inconsistencies/ deficiencies are most often the root cause of data quality issues, which are generally owned by your local HR department. Here's where you need to start thinking about governance for your Identity Management program to get visibility into your organization's HR processes and a handle on data collection.
For more info on Data Quality and IdM check out: DataFlux, EIMBlog, and Sun’s Ten Best Practices for Identity Management Implementation (Data Quality is #9 on their list)
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