uniformity in implementation across systems that
may contain duplicate sets of the same data. original data should be weighed in the event future
workflows require the original form of the data.
Set a lifecycle and stick to it Model Management
Setting a lifecycle for data that determines the point
in which data is retired and no longer needed ensures
stale data is not floating around incurring costs, as
well as driving decisions. Predictive models drive many organizations. These
models are used to define many things from recom-
mendations to risk profiling. These models are just as
critical as the data feeding them, if not more so. These
models should be considered in a data governance
strategy to account for who can approve new model
deployment, how they are tested and what documen-
tation is required for all models produced.
Track metadata across the organization
Metadata has become more critical in recent years
with the increase in unstructured data being stored
and analyzed. The metadata about creation, owners,
and topics is key to understanding and increasing the
value of a data set. Having an organization-wide pol-
icy and single instance for tracking all metadata will
enable anyone in the organization to quickly locate
information that is relevant to their work.
Track copies/instances of the same data set
with locations and times of creation
As information systems increase in complexity, it is
more and more common that a dataset will be copied
multiple times within an organization. These replica
copies are k