Metadata Management Definition, Explanation & Examples

What is Metadata Management?

Metadata management is the process of building and maintaining metadata.

Understanding Metadata Management begins with understanding what metadata is: it is simply data that provides information and context about other data, perhaps the core data being referenced to.

By creating metadata for your databases, you increase their readability, which makes them easier for others in your organization to use. This is especially valuable if multiple people are working with the same data, or when different teams are sharing data. Metadata makes it easy for everyone to find what they need without having to sift through endless lines of code.

Metadata that is well managed will make it easy to identify where sensitive information is located within your system and maintain a high-quality database, since finding and fixing bugs will be seamless.

What is the value of metadata management?

Metadata management is the process by which data managers collect, organize and maintain metadata, or data about other data. Metadata is information that describes specific aspects of a data source and its contents. Its purpose is to make it easier for humans and machines to discover, understand, interpret and use data. By creating a consistent source of truth, regular and routine checks on the metadata, and controlling who and how it's used, your metadata will continue to serve everyone who works with the core data itself.

Due to the nature of metadata management, its value increases as your data organization and company scale up,. This is because the number of databases you're working with and the size of those databases will increase exponentially as time goes on and as the company accumulates more data. That said, even smaller organizations can benefit from establishing a metadata management system early on so they have a solid foundation in place when they scale up their operations.

Why is it important?

Metadata is increasingly important today as the amount of available data grows exponentially. The collected metadata itself takes on the characteristics of big data, making it difficult to find any specific piece of information within this sea of metadata. This makes the process of managing it all that much more important.

The best way to describe this is that data is collected and stored every day- picture an eCommerce website storing customers, new stock, old stock, purchases, and abandoned carts. Not only does the historical data that needs to be stored and sorted grow, but so does the quantity of information in general as the eCommerce website hopefully grows. With every piece of data collected, there needs to be accompanying metadata. It can be a quickly growing mass of unsorted, unaccounted for data and metadata if not properly managed.

Metadata management is critical for effective information governance because it ensures that your organization’s data assets are well understood, easily discoverable and effectively governed.

Best Tools to Manage Metadata

There are a few methods of ensuring your metadata is managed correctly and follows sound data governance.

• Data catalogs. These tools provide a single platform to document what is in your data, what is in your metadata, and link them to the entities in question. They typically allow users to tag and categorize metadata, making it even easier to search through.
• Data dictionaries. Sometimes found in data catalogs, a data dictionary will clearly define what properties in a database consist of and how they interact with other properties in your database.

Examples

• Metadata repository: this repository would contain all the metadata associated with the data, including the source, date and time of collection, format, and purpose. The repository would also provide a way to track changes to the data over time. This lets dataa teams ensure that their data is accurate and up to date.
• Data dictionaries: the data dictionary would provide a detailed description of the data, including its source, format, and purpose. This would enable daata teams to easily understand the data and ensure that it is being used correctly.
• Tracking data usage: This system would track who is accessing the data, when they are accessing it, and for what purpose. This allows data teams to monitor the data and ensure that it is being used for its intended purpose.
• Data standards: Data standards are specifications that define the format and structure of data. Data standards help to ensure that data is consistent across different systems and applications.In summary, metadata management is an essential component of data analysis. It involves the use of data dictionaries, data models, and data standards to ensure data accuracy, integrity, and consistency. By managing metadata effectively, data analysts can ensure that their data is properly understood and used.

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