Why Does Data Taxonomy Classification Matter?

What is data taxonomy, why does it matter, and how can it help you to stay ahead of the competition, particularly for businesses that rely on data?

The content below aims to serve 2 main objectives:

1. Explain the working mechanism of taxonomy classification and its benefits
2. Introduce a working business solution to organising unstructured text data

There is a lot to address, so let us have a closer look.

What is data taxonomy

It is estimated that over 250,000 new websites are created every day, adding to the billions that already exist and the millions that publish content on a regular basis. That is a lot of data management for a lot of information, and as with everything else on the web, only a fraction of it is valuable and relevant. 

You cannot be expected to sort through this unstructured data manually. Even if you had the time and the patience, new data is created faster than you can analyse and record it—you just cannot keep up.

That is where taxonomy classification comes in, also known as topic classification and topic modelling.

Data taxonomy classification services, such as those provided by Identrics, help to automate this immensely time-consuming process. Through real-time analysis of common words, they can help you to learn more about an organisation, entity, or brand, and provide some much-needed clarity.

How does taxonomy classification work

Taxonomy classification uses machine learning algorithms to crawl through hundreds of documents and find relevant, valuable information. Thanks to natural language processing, it can extract relevant information, and then provide a readable hierarchy of data, with vital data points broken down into easily digestible segments.

As an example, let us suppose that your goal is to glean information about a specific product or service as it relates to a certain brand.

Using Identrics taxonomy classification, you can find relevant and unbiased information relating to this product. This information is taken from a variety of online text data sources and as it is an automated process, your input is minimal, and our machine-learning software will do all the work for you.

The information will then tell you exactly what you need to know, highlighting everything from opinions, customer feedback and complaints to potential errors and oversights

Taxonomy classification prerequisites

Taxonomy classification can’t simply perform text mining over the entire internet and then categorise everything that relates to your brand, products, or services. You need to define specific criteria for it to work.

In the above example, for instance, you may choose to highlight “functionality”, “reliability”, and “usability”, all of which can tell you more about how your product is being used.

A trained algorithm will have an idea of what to look for based on these criteria. Once you have that information, you can tell it to search through a specific dataset, such as customer support emails, user reviews, or support tickets.

If it returns a wealth of data telling you that there is a specific issue with an aspect of the product, you can fix the issue and fine-tune the language model to look for other data points.

How do we perform data taxonomy classification

Taxonomy classification is a complicated process, but we have developed an automated process that is fast and simple. It is easy to use and can search seemingly endless data to find the results that you need.

Here’s how it works:

Text classification with machine learning

Machine learning is integral to the data classification process. In simple terms, machine learning is a type of artificial intelligence (AI) that works by learning from human data.

The algorithm is fed text, tags, and other predefined training data, so that it can learn what to look for. This enriched training data lets the AI fine-tune its methods, perform text analysis and find relevant patterns.

Why is data taxonomy classification important

The importance of taxonomy classification services largely depends on your brand and your place in the market, but there is a distinctive benefit for every company that relies on data.

The main goal is to stay ahead of the competition, and there are several ways that it can help with this.

1. Firstly, taxonomy classification methods can keep your content evergreen and ensure you are producing content that people actually want to read. If you have good content, you will drive more organic traffic to your brand.
2. Taxonomy classification helps to highlight promotable, shareable, and reusable content, all of which play a role in developing an effective content marketing strategy.
3. As noted above, taxonomy classification can also help you to highlight issues with products and services. It can give you better insights into your brand and how your customers interact with it, highlighting concerns and problems that you may have otherwise missed.

What is the difference between topic classification and topic modelling

Both topic modelling and topic classification can be utilised in similar ways, but there are minor differences between these two methods.

Topic modelling is also known as “unsupervised machine learning”, as the topic modelling algorithms do not require a predefined set of criteria. It also doesn’t need any kind of training on its use, so it is more accessible. However, you cannot guarantee a topic model has the same accuracy that you get from topic classification, which uses predefined lists to ensure that only the most relevant and accurate results are produced.

Both topic models and topic classification can be effective ways to categorise large amounts of data.

Data taxonomy development and classification

The world is moving at a rapid pace. Machine learning is one of the ways to move even faster and stay ahead of the competition. At Identrics, we have the tools you need to get on top and stay there.

Contact us today to set up to start receiving insights about your brand, potential high-volume content topics, and customer concerns you can act on.