Enforcing Data Quality Through Data Lineage Metadata

Understanding and improving information quality (IQ) almost always involves knowing where data came from, the business (and data quality) rules applied to it, where those rules were applied, and any transformations the data went through. That is, you must understand and document the metadata around data transformations. You will learn how to build an information chain and the many ways the information chain can help you understand and document data lineage, rules, assigning of stewardship, and semantic mapping. A basic metamodel will be presented for recording transformations in a metadata repository, and how to customize the metamodel to add more detail for business rules and the rule application point. You will learn processes to record and implement business rules and how to segregate data that fails the rules. You will also learn the process and cultural implications of implementing rigorous IQ through metadata management. A case study will document actual cost savings and productivity increases from having successfully tracked lineage for data.

• Building the Information Chain
• The value of the Information Chain
• Tracking data lineage across the Information Chain
• Specifying IQ rules
• Examining the metadata that must be recorded and analyzed to understand what happened to the data
• Inspecting the Lineage metamodel