Data management has always provided challenges. Poor quality data in Salesforce starts a vicious cycle where users mistrust the data, which hinders user adoption and further degrades data quality when users are not actively adding and updating records. It’s best to ‘nip it in the bud’ as soon as possible. Salesforce data cleansing is no …
Here are highlights from article Ultimate Guide to Salesforce Data Quality and Data Cleansing | Salesforce Ben
1. Challenges of poor data quality in Salesforce:
– Users mistrust the data, hindering user adoption
– Degrades data quality when users don’t actively update records
– Impairs digital transformation efforts
2. Understanding data quality metrics:
a. Completeness – proportion of available records or values filled on a record
b. Uniqueness – avoiding duplicate records
c. Timeliness – data reflecting the real world at a specific point in time
d. Validity – adhering to the syntax and intended purpose
e. Accuracy – data correctly describing the real world
f. Consistency – data representing the real world across multiple databases
3. Importance of data quality exercise:
– Conducting a 1-hour data quality exercise every week/month
– Keeping track of changes in data quality over time
4. The 3 Cs of data quality:
– Compliance, Completeness, and Correctness as priorities in the changing data privacy landscape
5. Data capture for completeness and timeliness:
– Data can be captured at different points in business processes by employees, customers, and prospects
You can read it here: https://sfdc.blog/IJytu
Source from salesforceben(dot)com