How would you approach data cleanup and de-duplication in CLM?

How would you approach data cleanup and de-duplication in CLM?

• A. Delete all records older than a year.
• B. Ignore duplicates and leave as is.
• C. Manually copy records to avoid duplicates.
• D. Identify duplicates via unique identifiers, merge records, normalize data, and implement duplicate detection rules.

Answer: (D)

The main concept is establishing a disciplined, scalable approach to clean data and prevent duplicates in CLM. The best approach uses unique identifiers to reliably spot duplicates, merges records so no information is lost, normalizes data so fields are consistent and comparable, and implements duplicate detection Rules to catch duplicates automatically going forward. This combination ensures data integrity, reduces manual cleanup effort, and prevents future duplicates from slipping in. Deleting records older than a year risks data loss and may remove legitimate historical information needed for audits or reporting. Ignoring duplicates leaves the data set inconsistent and undermines search, reporting, and workflow accuracy. Manually copying records to avoid duplicates is inefficient and error-prone, especially as the data set grows; it also doesn’t scale and can miss duplicates that aren’t obvious.

The main concept is establishing a disciplined, scalable approach to clean data and prevent duplicates in CLM. The best approach uses unique identifiers to reliably spot duplicates, merges records so no information is lost, normalizes data so fields are consistent and comparable, and implements duplicate detection Rules to catch duplicates automatically going forward. This combination ensures data integrity, reduces manual cleanup effort, and prevents future duplicates from slipping in.

Deleting records older than a year risks data loss and may remove legitimate historical information needed for audits or reporting. Ignoring duplicates leaves the data set inconsistent and undermines search, reporting, and workflow accuracy. Manually copying records to avoid duplicates is inefficient and error-prone, especially as the data set grows; it also doesn’t scale and can miss duplicates that aren’t obvious.