Identifying Quality Data Objectives - trillium_discovery - trillium_quality - Latest

Trillium Control Center

Product type
Software
Portfolio
Verify
Product family
Trillium
Product
Trillium > Trillium Discovery
Version
Latest
Language
English
Product name
Trillium Quality and Discovery
Title
Trillium Control Center
Copyright
2024
First publish date
2008
Last updated
2024-10-18
Published on
2024-10-18T15:02:04.502478

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.