Making Sense Of Your Data
Metadata management aims at correctly defining, integrating, managing, and sharing reliable metadata within an organization through the combination of organization, policies, processes, procedures, standards, and technology.
With the adoption of emerging technologies and the shift to digital business strategies, organizations are experiencing an influx of data. This new and vast data offers in-depth insights and holds great potential, but without properly defined and managed metadata, there is little context and comprehension, and the value of the data is lost. With a centralized metadata management system, data across the organization can be used for various analytics needs, ensuring its uncompromising purpose spans your organization's business initiatives.
Data At Your Fingertips
With an organized and content-rich metadata glossary, data discovery is fast and easy, enabling you to locate, track, understand, and utilize data effectively.
Gain insight into your information flow and where its touchpoints lie to understand impact assessment and support regulatory compliance.
With a centralized metadata system, various departments can use metadata objects as they match reporting across the organization.
Reduce time spent on metadata management with a more streamlined, centralized system, leaving more time for high value tasks.
A business glossary entails defining common terminology, exceptions, variants, and explanations for business users. This ensures smooth communication between stakeholders within your organization and a space of agreement and integrity when it comes to data.
Describing the data elements or entities from both a business and technical perspective is a crucial step in metadata management. These definitions are in the context of a conceptual, logical, and physical data model, ensuring that each data object is understood, allowing for the correct application as well as reusability.
From the moment that data is integrated into the system, it moves through several locations where it might be altered, risking the deterioration of quality and accuracy. With data lineage, each data entity is tracked, maintaining information about how it moves from one data set or repository to another. It also supports data element movement sources and target information, as well as data transformations, mappings, and transitions.
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