What is Data Taxonomy?

Data taxonomy: full definition

Data taxonomy is a way of organizing and classifying data to create a structured hierarchy. It helps businesses categorize their data to access and use it easily.

Information is grouped according to its characteristics, attributes, and relationships and placed into categories and subcategories.

There are typically multiple levels or layers in a data taxonomy, each level representing a specific category or class. Top-level categories are broader, while lower levels are more granular. Organizations can custom-build their taxonomy structure based on their needs and the nature of the data.

You can apply taxonomy to various data types, including structured data (databases and spreadsheets) and unstructured data (documents and multimedia files).

Data taxonomy benefits

Adopting a data taxonomy gives businesses a structured and standardized approach to managing their information. It is the foundation for effective data governance, integration, and analysis and has many benefits.

Consistent organization

With a data taxonomy, companies have a single framework for sorting and labeling data. This makes communication and clarity between teams, departments, and systems easier, reducing confusion and creating a harmonious environment.

Better data access

Data taxonomy makes information easier to access. Users can navigate through the hierarchical structure to find specific data they need, saving time and resources in searching for information. People within the business know where the data is, so they can quickly discover new information and utilize it for different purposes.

Improved data quality

Building a data taxonomy helps improve the quality of a business’s data. Using a consistent way of naming and organizing information allows users to spot errors and anomalies, making it easier to clean the data. Data cleansing enhances its reliability, giving organizations more confidence in their information.

Alternative models to data taxonomy

While data taxonomy is a widely used and practical approach for organizing and classifying data, businesses can consider alternative or additional models based on their needs.

Data ontologies

Data ontologies define the context and relationship between different entities. It links data in various formats, allowing for a more sophisticated understanding and inference. Ontologies go beyond simple categorization and hierarchy and look at what the data represents, its properties, and what it relates to.

Metadata schemas

Metadata schemas capture the characteristics of each data element. It describes data in detail, including additional information such as its format, purpose, location, and creation. The framework lets users customize their data tagging and search capabilities, allowing them to use the information more effectively.

Graph databases

Graph databases capture connections between data elements in a network-like structure. They use nodes and edges to represent relationships, which helps display highly interconnected and interdependent data.

Folksonomies

Folksonomies occur where the data has a diverse or subjective categorization. Users describe data by assigning keywords or tags based on their understanding and perspective. Most organizations won’t intentionally create folksonomies. However, one might evolve after many users create content and use individual definitions. It’s also known as social or collaborative tagging, as it’s a collective way for people to understand data better.

Faceted classification

Faceted classification categorizes data based on its facets and attributes, each representing a distinct data aspect. Data is organized into categories based on collective characteristics, and users can find information by filtering using different properties.

Next steps

As the volume and complexity of data continue to grow, the role of data taxonomies is integral to a business's growth.

Data taxonomies form a solid foundation for effective data management, allowing organizations to make informed decisions, gain valuable insights, and unlock the full potential of their data assets.

By implementing a robust data taxonomy, businesses can confidently navigate their data landscape and maximize the value of their information resources.