SAS Data Management
provides the tightest integration in the industry that spans the entire
data management lifecycle. Metadata is shared between the data management
and analytics domains. For example, during the profiling phase, data
correction strategies are identified and documented within the SAS
repository. After documenting these rules, the profiling engine can
be prompted, with a single click of the mouse, to automatically build
the data correction workflow. The profiling engine shares all metadata
with the data quality engine. This shared metadata includes items
such as data source connection information, data quality rules defined
during the profiling phase, and field names.
This automatically
generated workflow can be invoked through Service-Oriented Architectures.
For example, users, groups, and logins can be shared with data quality
jobs to streamline integration with SAS analytical solutions. This
integration layer can include complex business logic. It can also
contain core data quality algorithms such parsing, standardization,
and matching.
Shared metadata in SAS
is common throughout the platform. It uses metadata bridges that are
available to integrated metadata across applications. SAS applications
use a relationship importer that enables metadata to flow across the
metadata bridges.