What is the definition of metadata management?


Companies need to be able to convert their data into business assets in order to stay competitive. But as data comes in large quantities, in different types and varieties, companies are struggling to make sure it can be used, shared, and analyzed. This makes metadata control a strategy for companies that are interested in gaining value from their data catalog software.

Metadata management is a broad-based agreement on how to define information assets. This is metadata: a collection of descriptive data and providing information about other details. For example, the document will contain metadata describing the type and size of the file, the date the document was created, the author, the date of some changes, as well as other more detailed metadata such as titles, tags, and comments.

However, there is more than one type of metadata management solutions. For example, semantic metadata defines the “meaning” of data. It explains the meaning of the topic of the data by explaining how it relates to other facts. In this sense, semantic metadata is not just a list of categories, but it is based on descriptive details about “data”. Food-related metadata, for example, can be explained in detail, including details of nutrition, country of origin, and so on.

There are also functional metadata that is stored and organized so that it can be used manually or independently for one or more methods. Functional metadata makes it easier and more efficient for users to build, manage, and use data management systems for analysis, data, government, or any other purpose. Functional metadata also makes the broader data management process smarter and more efficient because it covers machine learning, integrates personal knowledge and links. Thus, functional metadata will not only highlight missing, inaccurate, or bizarre facts, but will also help improve the quality of analysis by correcting and improving data to improve decision making and prevent harmful errors.

With modern metadata management systems that use semantic and functional metadata, organizations can connect, deploy, and access their own data governence software. This makes it more transparent and better equipped to control the quality and risks associated with the data and its use.