Question 184 - Certified Data Architect discussion

According to the Salesforce documentation1, data quality is the measure of how well the data in Salesforce meets the expectations and requirements of the users and stakeholders. Data quality can be affected by various factors, such as data entry errors, data duplication, data inconsistency, data incompleteness, data timeliness, etc. To mitigate data quality issues, some of the recommended solutions are:

Leverage picklist and lookup fields where possible (option A). This means using fields that restrict the values or references that can be entered by the users or integrations. This can help reduce data entry errors, enforce data consistency, and improve data accuracy.

Leverage validation rules and workflows (option C). This means using features that allow defining rules and criteria to validate the data that is entered or updated by the users or integrations. This can help prevent invalid or incorrect data from being saved, and trigger actions or alerts to correct or improve the data.

Leveraging Apex to validate the format of data being entered via a mobile device (option B) is not a good solution, as it can be complex, costly, and difficult to maintain. It is better to use standard features or declarative tools that can handle data validation more effectively. Leveraging third-party AppExchange tools (option D) is also not a good solution, as it can incur additional costs and dependencies. It is better to use native Salesforce features or custom solutions that can handle data quality more efficiently.