Transitioning Enterprise Data Architecture Using Actionable Metadata
Over the years, metadata can be compared to daily vitamins – people have invested in its management without necessarily knowing whether it does any good. Point to point data migrations are one of the sole activities that can take advantage of what we traditionally manage as metadata, but often what is captured and stored is not only insufficient to meet the immediate needs of the migration, any institutional knowledge of value is lost when the project is over.
However, in light of a growing trend of increased scrutiny over any scenarios involving data sharing and exchange, there is a corresponding demand for auditable governance over ways that information is stored and transformed as it flows across administrative boundaries. Some organizations look to technical solutions such as Services Oriented Architecture in search of reduced risk and complexity, and this approach can be dramatically improved when the approach blends knowledge of information utility with standardized shared services.
This requires knowledge about data use, representation, and meaning, and in this webinar, we suggest ways to broaden the scope of a metadata strategy by coupling it with knowledge about data creation, use, lineage, and governance. The resulting framework provides the launching point for incrementally transitioning a disparate collection of siloed data subsystems into an enterprise data architecture that not only shares what is already known about data use, but guides all consumers in ways to control future data use to retain consistency in utility without sacrificing agreement regarding data semantics.
What You Will Learn:
- How data semantics are often left to the consumer
- Similarity in business concept use in relation to different business term definitions
- Considerations for capturing metadata that can drive effective data sharing and integration
- How lineage connects definitions, instantiations, and policies within the data governance framework
- Ways that shared semantic metadata can help incrementally transition the data architecture