Data management becomes fundamental as datasets multiply. Among the solutions for efficient handling of large volumes of data, metadata stands out. But what exactly is it? What is it used for? What types are there? And how is it properly utilized? DataScientest answers your questions.
What is metadata?
Definition
Metadata describes essential characteristics of a dataset or an individual data point, such as the author, date of creation, and function. The purpose is to provide context or instructions for its processing, much like an index card in a library catalogs information about a book.
This facilitates the identification and reuse of relevant data, being an essential part of data governance.
Data vs Metadata
Generally, metadata is data about data. It can serve as a roadmap to understanding the complexity of large data sets.
How to differentiate them?
Both are data, but they present different challenges.
The primary data (referenced by metadata) is valuable from business, science, IT, marketing perspectives, and some could be classified as confidential.
On the other hand, metadata aims to facilitate the processing of data and typically does not require rigorous protection.
We can use the metaphor of a letter sent through a postal service: the content of the envelope would be the primary data, of interest only to the sender and recipient. Meanwhile, what is written on the envelope (address, recipient’s name, date of dispatch) are metadata, which assist in the mailing without creating confidentiality risks.
Functions of Metadata
Besides simplifying data processing, metadata can fulfill broader objectives:
- Minimize the risk of data loss: by providing context. This allows the description of the creation process and facilitates its recreation if necessary.
- Optimize data search: by locating specific information, such as the date or the type of data (image, video, file, etc.).
- Promote the linking of data: the use of associated keywords allows grouping data by common themes.
Tip: Given its importance in the Big Data era, it’s crucial to create metadata as soon as datasets are generated to avoid an overwhelming workload.
Nowadays, there are platforms that automate the creation of metadata, thereby simplifying data categorization.
Types of Metadata
There is a wide variety of metadata, which can be classified into 6 main families:
- Descriptive metadata: facilitates the search and understanding of primary data, such as format, title of an image, author of a document, and language of a video. The details may vary depending on the type of data.
- Provenance metadata: identifies the source of the data and its changes over time.
- Technical metadata: highlights the tools needed to read data, promoting interoperability between systems.
- Metadata of rights and access: informs about copyright, licenses, and who can access the data.
- Preservation metadata: documents the history of the data.
- Citation metadata: necessary when the data will be used by third parties.
Uses of Metadata
For correct utilization and reuse, metadata must be complete and understandable for everyone.
As a result, various metadata standards have been created, such as:
- Dublin Core or DCMI: the most popular, initially used for bibliographic information and now applied to a variety of data.
- Darwin Core: especially in bioinformatics.
- Data Documentation Initiative (DDI): an international standard for surveys and social observation.
Other standards are based on the specificities of each dataset and discipline. For more information, visit the Digital Curation Centre (DCC) website.
Each standard includes a schema with mandatory and/or optional elements and a description of the syntax.
Learn about Metadata with DataScientest
With the exponential growth of data, metadata management is key for organizations, which turn to data governance specialists. These advanced technical skills are acquired through specialized training, such as that offered by DataScientest.
