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Metadata: Fuel that Powers System Integrations and AI Context Recognition
Metadata is data which is used to describe other data. It generally consists of tags, descriptive labels, and statistics associated with data that enables better search and classification of that data.
Metadata often provides the key for businesses when integrating multiple software applications to determine how data from multiple systems relate to each other. Especially for large organizations using many different applications, getting a grip on how data and associated metadata map to each other is important.
Metadata is also increasingly important as the “fuel” that enables artificial intelligence and machine learning algorithms to recognize context and to be able to be more accurate in producing analytics and recommendations.
A key finding in a 451 report was that when managing data objects, metadata provides “a framework for identifying and contextualizing data that can be used to automate long-term data management.”
Gartner also reports in a recent study on Metadata Management that”data and analytics leaders are adding metadata management capabilities. The trend is being driven by conflicting pressures: on the one hand, demands for more flexibility in accessing data is growing; on the other, the rising importance of data governance, risk and compliance forces organizations to become more cautious regarding the possible uses of data. This is leading to demands on organizations to offer more transparency in the uses of data, what it actually means, where it is coming from, and whether it can be trusted.”
