Data Taxonomy: Organising data for better utilisation

Businesses generate vast amounts of data daily. However, according to a Seagate study, more than two-thirds of this information remains unused. How can it be structured effectively to unlock its value?

Data taxonomy provides a structured framework to classify data, preventing information fragmentation. It is a fundamental component of a modern data infrastructure.

🔎 Discover what data taxonomy is, the main types, and its role in information organisation. Learn how to build a logical classification system to enhance data utilisation and improve overall efficiency. 🚀

What Is Data Taxonomy?

It provides a coherent and structured framework that makes information easily accessible. By logically organising data, taxonomy simplifies data exploitation, analysis, and governance.

This concept is similar to biological taxonomy, where species are grouped into hierarchical classifications called 'taxons' such as kingdom, order, family, and genus. It is also widely used in content marketing.

Data taxonomy differs from other data structuring models, particularly:

Data taxonomy improves governance and decision-making. It is essential for data-driven businesses.

Different types of data taxonomies exist depending on business needs. A company can organise data along several dimensions: products (e-commerce catalogue), customers (CRM segmentation) or orders (tracking and reporting).

👉 Let’s take a simple example. Imagine you manage an e-commerce website specialising in racquet sports 🎾🏸. Below is a step-by-step approach to structuring a product taxonomy.

You want customers to be able to find what they're looking for quickly and your teams to be able to manage the catalogue efficiently. To achieve this, product data needs to be well organised so that it can be integrated into the CRM and marketing campaigns.

To cover all racket sports, you can group products by sport type:

Each sport has similar product families, allowing you to structure taxonomy as follows:

Sport (Main Category):

For the Tennis category, for example, the breakdown might look like this:

To assist customers, specific data fields must be included:

- Weight, head size, balance, rigidity for the rackets sub-category
- Surface (clay, hard, grass), size for shoes
- Gender, size, material for the textile sub-category.

A well-organised data structure offers multiple advantages. In this example, product categorisation enables:

However, data taxonomy extends beyond product categorisation. It applies to all data used within a business. Here's what it might look like for an e-commerce site in general.

This global approach ensures:

A well-managed data taxonomy helps organise and classify data, ensuring it is usable and actionable. To achieve this, it is crucial to define clear categories that align with business needs. Maintaining a consistent structure across all tools and updating it regularly ensures continued effectiveness.

Simple spreadsheet tools (Excel, Google Sheets, Airtable, Notion) or diagramming tools (Miro, Mindmeister, Whimsical, drawSQL) may be enough to design the first diagrams with different levels of depth;

Data catalogue platforms such as Atlan, Alation and Castordoc enable companies to organise, explore and govern their data efficiently.

Consumer-focused taxonomies, combined with an analysis of interactions across all marketing touchpoints, enable precise segmentation. Reverse ETL solutions can then be used to activate the customer data in the datawarehouse, targeting the right audiences and the right channels.

A well thought-out taxonomy directly improves business performance. It optimises reporting, refines marketing targeting and reduces data silos. By ensuring rigorous classification, companies gain in efficiency and maximise the impact of their marketing campaigns. 

DinMo makes it easier to send the right message, at the right time, to the right place and to the right person.

A well-structured data taxonomy helps businesses organise and categorise information efficiently. The key components include:

An effective taxonomy helps businesses improve decision-making, optimise content classification, and enhance overall market insights.

A data taxonomy enhances business intelligence by structuring information into clear categories and groups. This makes it easier for teams to understand relationships between datasets, track market trends, and gain insights.

With a strong taxonomy, companies can:

By using taxonomies, businesses reduce time spent searching for relevant information, ensuring that decision-makers work with the best available data.

Implementing a data taxonomy comes with challenges that impact business efficiency and customer experience. Some common issues include:

Despite these challenges, using best practices and adopting the right technology helps businesses build a structured and reliable data system.