The other dimension of breadth is about the four different types of metadata that must be managed: The first dimension is the ability to collect metadata from sources across the enterprise, both on-premises and cloud. Metadata management must be able to handle the breadth of data across the enterprise.
Leveraged automation to eliminate the need for tedious, manual curation of data sets
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Reasons that metadata management – encompassing data lineage – is important:Įnsures visibility into where the data is coming from, if it can be trusted, and how to find itĮnables transparency across hybrid and multicloud architectures, giving a complete view into data across on-premises and cloud data repositories Because data lineage clarifies the availability, ownership, security, and quality of data as it flows across the organization, it is an essential part of metadata management. Most critically, it gives data consumers visibility into what data is available and answers any questions they might have about its data lineage.ĭata lineage is essentially the “provenance” for data: an ongoing and continuously updated record of where data originates, how it moves through the organization, how it gets transformed, where it’s stored, who accesses it, and other key metadata. It ensures data is trusted and accessible as appropriate to data consumers throughout the enterprise. Metadata management is a foundational practice for any organization striving to make better use of its data. It allows users of all kinds – business, technical, and operational – to search for, understand, and securely access the data they need to do their jobs. So what is metadata management? It is the portfolio of best-practice processes and technologies that allow businesses to manage this data about their data and derive insights for more effective data management.
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