What best describes a method to ensure data quality when importing a large volume of contact data into Salesforce?

Ensuring data quality when importing a large volume of contact data into Salesforce is critical to maintain the integrity and usefulness of the data. The most effective method among the provided choices is to deduplicate the data before loading it. This process involves identifying and removing duplicate records from the dataset before it enters Salesforce.

Deduplication at this stage significantly minimizes the risk of entering duplicate entries, which may lead to confused data insights, increased storage costs, and challenges with user experience. By resolving these issues beforehand, the integrity of the data is upheld, as duplicate records can disrupt both reporting accuracy and application functionality.

Utilizing Salesforce’s native duplicate rules alone may not address existing duplicates in the source dataset prior to import. While these rules can prevent the creation of duplicates during import, they do not clean the data upfront. Clustering data by regions can help organize records for import but does not inherently improve data quality. Manually reviewing all imported records can be a long and inefficient process, particularly with large datasets, and is not a scalable solution for ensuring data quality.

Overall, deduplicating data before loading ensures that only clean, high-quality data enters the system, thus maintaining the efficiency and effectiveness of Salesforce as a tool.