What are data taxonomies?

Taxonomies are not new—they have been around for as long as human beings have felt the need to organize information. A data taxonomy is a technique of classifying data into hierarchical groups and subgroups based on similar characteristics. It offers an efficient way of categorizing data to show its uniqueness and lack of redundancy. 

Taxonomies are the building blocks of artificial intelligence and machine learning. And though they may look like data models at first glance, taxonomy charts are flexible in design, meaning they can be customized to meet any business or research needs.

There are very many advantages of implementing data taxonomies, as highlighted below: 

Improved clarity and communication

When the definitions of essential terms are easily understandable, the data sources begin to make sense. With high-level data organization, you can gain more clarity, thus improving communication.

Data taxonomies can increase the accessibility of vital company data owing to improved clarity of information. Case in point, it can be very embarrassing for your employees to learn about new product features from customers. Data taxonomy guarantees harmony in your organization, ensuring everyone reads from the same page.

Facilitating better data quality

Introducing data taxonomy within your company can help you improve data quality. One of the main advantages of data taxonomy is that it cleans and organizes your data, making it easy to find and use important data items. It also helps you highlight any inconsistencies in the data, thus avoiding errors.

Improving decision-making in an organization

It's all too common to find yourself in circumstances where the power of hindsight would have come in handy when thinking about driving a more positive outcome. That said, you can still take advantage of this superpower by accessing critical data at the right time.

Circulating accurate information to people within your business can offer important insights that will help management make critical decisions. Unfortunately, sometimes employees may be unaware that such data exists, and even if it does, it may be hidden in a series of folder structures.

Data taxonomy facilitates the organization of such critical data, organizing what type of information it is and where to find it.

Facilitating data interoperability

Another importance of data taxonomy is that it provides data interoperability. When your data is organized and clear, it becomes easily shareable within different departments and systems. This is especially helpful for large companies where information is internally and externally shared.

Avoiding duplication of data

Data taxonomy prevents the repetition of information. A vivid understanding of your data can show you the available datasets to help you easily figure out if the data you require is already available. For example, if someone in finance is looking for a specific Google sheet, they can check if it already exists before requesting it.

Picture data as books in a library. Unstructured data is the same as a disorganized pile of books lying everywhere, with no card catalog to lead you to the information you want. A poor taxonomy may group these books into several categories, depending on their genre. This means if you're sorting the books by the publication name, year of publication, or the author's last name, the taxonomy will not help you so much.

An effective taxonomy is akin to an enormous bookcase, with books grouped onto shelves and marked with thousands of unique identifiers, for example, author name, year of publication, genre, and other meaningful tags. 

Organizations can use data taxonomy to classify data based on different categories, such as ordinal, numerical, or categorical data. You can also use taxonomy to highlight the relationships between different sets of data, like the hierarchy of data components in a given database.

You can use these strategies to create effective taxonomies:

Engage various stakeholders in the creation process

You should have all stakeholders involved in the initiative, especially those working with the data directly. The more involved these people are, the more likely they will be to embrace the taxonomy once implemented.

Carry out regular system reviews and updates

Getting the taxonomy up and running should not be the end of the road. Operations can change in your business, from your data collection methods to your organization's needs. Therefore, reviewing and updating your data taxonomy to accommodate changes is important.

There are three distinct types of data taxonomies, as highlighted below:

The dimensional model

This database model uses measures and dimensions to describe data. A measure is a value, for example, profit. On the other hand, a dimension is a specific piece of information, such as time. Therefore, the dimensional model presents data as measures and dimensions in a table.

The entity-relationship model

This model outlines how different entities in a database relate. An entity can be any data piece, such as a customer. The model, therefore, shows the relationship between two entities, such as a client and a product, in the form of a diagram.

Object-oriented model

This model describes data in terms of the object and its characteristics. An object can be a customer, while the attribute can be specific information about themselves, such as where they live.

Now that you know and understand the benefits of data taxonomies, let's look at how you can come up with effective data taxonomies:

1. Set clear and precise objectives of the taxonomies

Before you do anything else, outline the goals and objectives of developing data taxonomies. This will help your team understand what you intend to achieve and how you can keep track of your progression.

2. Come up with a clear structure for the taxonomies

You need to create a concrete design for the taxonomies. It would help to create a data hierarchy with tables containing similar information, created logically for easy understanding. You can use a hierarchical structure or the less common faceted structure.

3. Define and group the terms and classes within the taxonomy

This is one of the most challenging parts of building a data taxonomy. Once you have a structure, you must agree on the terms and data categories. Keep the terms simple and short to avoid complicating matters.

4. Implement the taxonomy

Now that you have put in all the hard work, the time is ripe to implement your data taxonomy across the right systems and platforms and get employee feedback.

5. Ongoing governance and optimization

Once people have become accustomed to the taxonomy, you can have strategies to ensure it continues to have a lasting impact on the organization.

The following are real-world examples of data taxonomy to improve your understanding:

• A technology company wants to rebrand. As part of the process, they’re considering redesigning their website. Because they have data taxonomy in place, it's easy for them to restructure how content appears on their website.

• A marketing agency needs help tracking the files of each client. Using a data taxonomy that groups assets by customer, role, and use, they can get the necessary parties on the same page about how to look for client files, making the work easy for everyone.

Data classification can be described as the systematic arrangement of data in categories according to established criteria. For instance, grouping items in a store by color or gender can be termed as classification. 

People often classify items because groups are easier to understand than a random cluster of unrelated items. Unlike data taxonomy, classification doesn’t usually go beyond a few groups per class.

Data taxonomy, on the other hand, is the technique of assigning names to items or groups of objects according to where they lie in a hierarchy. When it comes to data management, both taxonomy and classification are techniques for organizing large sets of data in a way that makes it easy to understand.

Both techniques allow us to come up with databases of separated but related assets for easy comparison and contrast. However, while taxonomy provides more detailed and exhaustive lists, data classification is more surface-level. Also, while data taxonomy explains the relationships between different data sets, classification only groups them.

To understand the different types of testing, you should classify them into a taxonomy that puts similar testing types under one group. You can organize the tests by the kinds of questions they answer. 

In addition to classifying learning objectives, data taxonomy guides the developer in knowing the test content. A taxonomy makes evaluating whether the test questions match the learning level and goals easy.