What is Data Taxonomy? Examples Included

What is Data Taxonomy?

A data taxonomy allows organizations to manage and classify information. Explore the benefits, examples, and alternatives to data taxonomies below.

Table of Contents

            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 example

            To help understand how data taxonomy works, let’s consider an example for an ecommerce company.

            Top-level categories may contain:

            • Products
            • Customers
            • Orders
            • Marketing
            • Inventory

            You then have the option for several subcategories from these main categories. For example, subcategories under ‘Products’ could include:

            • Electronics
            • Clothing
            • Home and kitchen
            • Beauty and personal care

            Subcategories under each product category would help classify the information further. For example, under ‘Electronics’:

            • Computers and laptops
            • Smartphones and tablets
            • TVs and home entertainment
            • Audio and headphones

            Under ‘Customers’ the company might organize the information like this:

            • Registered customers
            • Guest customers
            • Loyalty program members

            Under ‘Orders’ like this:

            • Pending orders
            • Shipped orders
            • Cancelled orders
            • Returned orders

            Examples of subcategories under ‘Marketing’ are:

            • Campaigns
            • Promotions
            • Customer segmentation
            • Advertising channels

            And under ‘Inventory’ the classification system may go as follows:

            • Stock levels
            • Warehouses
            • Reordering
            • Supplier information

            This is a basic example of what a data taxonomy for an ecommerce company could look like, with many more classification opportunities to organize the business's information further.

            Utilizing a data taxonomy like this helps efficiently manage and analyze the organization’s information, supporting product catalog management, customer segmentation, order tracking, marketing campaigns, and inventory management.

            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.

            Efficient data analysis and reporting

            Categorizing data into meaningful groups helps with data analysis and reporting. It’s more straightforward to find and understand, so teams can better analyze specific categories and spot patterns and trends. Businesses can use this information to form insights and make data-driven decisions.

            Data governance and compliance

            An adequate data taxonomy helps companies comply with governance practices. Clear naming conventions, standards, and ownership rules ensure businesses can easily follow data regulation, policy, and quality requirements. Additionally, it lets organizations track and manage data lineage consistently, adhere to data minimization recommendations, and swiftly delete sensitive data if needed.

            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 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.

            To get started, check out our data taxonomy playbook for advice on implementing an effective data hierarchy.