Keeping data modeling at the project-level
with data architects designing and developing databases
in isolation, has led to problems of redundancy, lack
of knowledge, and diminished confidence in existing
data. Establishing a data architecture at the enterprise
level enables organizations to make data assets more
visible, manage them over the long term, and repurpose
them for new applications.
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| Existing data source assessment |
| Powerful reverse-engineering capabilities
across a wide variety of data sources enable organizations
to easily understand their existing assets. |
| ER/Studio |
|
| Metadata management and integration |
| A blueprint of the data that integrates,
compares, and maps all the metadata into a central hub
makes data sources and associated business rules visible
across the organization. |
| ER/Studio |
|
| Design, create, and update data sources |
| Enterprise model management supports
living architectures that show changes and enable enterprise-wide
propagation of updates. |
| ER/Studio |
|
| Enterprise-wide communication and knowledge transfer |
| Information about data sources needs
to be easily and accurately transferred. Data architectures
are closely linked with application (UML) modeling,
business process modeling, and data integration (ETL)
solutions. |
| ER/Studio |
| DT/Studio |
| Describe |
