Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.
So most early-stage data governance managers kick off a series of projects to profile data, make inferences about data element structure and format, and store the presumptive metadata in some metadata repository. But are these rampant and often uncontrolled projects to collect metadata properly motivated?
There is rarely a clear directive about how metadata is used. Therefore prior to launching metadata collection tasks, it is important to specifically direct how the knowledge embedded within the corporate metadata should be used.
Managing metadata should not be a sub-goal of data governance. Today, metadata is the heart of enterprise data management and governance/ intelligence efforts and should have a clear strategy – rather than just something you do.
Quite simply, metadata is data about data. It’s generated every time data is captured at a source, accessed by users, moved through an organization, integrated or augmented with other data from other sources, profiled, cleansed and analyzed. Metadata is valuable because it provides information about the attributes of data elements that can be used to guide strategic and operational decision-making. It answers these important questions:
Organizations don’t know what they don’t know, and this problem is only getting worse. As data continues to proliferate, so does the need for data and analytics initiatives to make sense of it all. Here are some benefits of metadata management for data governance use cases:
So how do you give metadata meaning? While this sounds like a deep philosophical question, the reality is the right tools can make all the difference.
erwin Data Intelligence (erwin DI) combines data management and data governance processes in an automated flow.
It’s unique in its ability to automatically harvest, transform and feed metadata from a wide array of data sources, operational processes, business applications and data models into a central data catalog and then make it accessible and understandable within the context of role-based views.
erwin DI sits on a common metamodel that is open, extensible and comes with a full set of APIs. A comprehensive list of erwin-owned standard data connectors are included for automated harvesting, refreshing and version-controlled metadata management. Optional erwin Smart Data Connectors reverse-engineer ETL code of all types and connect bi-directionally with reporting and other ecosystem tools. These connectors offer the fastest and most accurate path to data lineage, impact analysis and other detailed graphical relationships.
Additionally, erwin DI is part of the larger erwin EDGE platform that integrates data modeling, enterprise architecture, business process modeling, data cataloging and data literacy. We know our customers need an active metadata-driven approach to:
erwin was named a Leader in Gartner’s “2019 Magic Quadrant for Metadata Management Solutions.”
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