Before you create a Quality project, you must identify the issues you want to resolve and align these with the business goals or other objectives required for the data. For example, do you need to standardize the data format, identify incorrect addresses, or remove duplicate records?
Note: Use the Baseline Analysis features to analyze and profile your data to learn where
issues exist and to drill down and investigate the data. This information will help you
define your data quality objectives.
Some of the most common data quality objectives are:
- Identify and remove duplicate records
- Cleanse and standardize data formats
- Identify specific data elements
- Normalize name and address data
- Identify and standardize all data that is NOT name and address related
- Identify incorrect, obsolete, or invalid data
- Identify multiple customers within a household and link them together
- Find the same customer among multiple files
- Update files with new data
- Re-engineer and consolidate data after cleansing to create unique views
If you have enterprise data-cleansing standards, you can set up complex workflows using business rules and other rules-driven processes to bring data into compliance with your data governance requirements.