Taxonomy Management 101

Every day, we create billions and trillions of data, from a click of our phone to saving a document in the cloud. Storing and sorting this data can be quite overwhelming even for the average person — never mind an organization whose data seemingly never ends, and is crucial to their overall business success. Maintaining this data in an efficient way requires tools that are designed to meet these needs.

While spreadsheets previously sufficed, enterprises are now looking to more intelligent software to manage their data and stay ahead of the curve. Organizations who have extensive unstructured data know the struggles of extracting meaningful information from it. As a result, experts in data management and semantic technology urge the use of comprehensive taxonomies to do the job. 

Taxonomy management has allowed us to not only find things easier and faster, but it also allows us to find linkages within our data, which we would have never found before. As a result, taxonomy management enables enterprises to confidently tackle data governance, while allowing organizations to further scale their business intelligence and overall ability to innovate in competitive markets.

What is a Taxonomy?

In a broad sense, taxonomy is the method used for organizing similar content into relevant groups. To put it even more broadly, taxonomy is how we classify things. From its conception, taxonomies have played an important role in biological science, where it has been largely used to organize the animal kingdom. Think of mammals vs. birds vs. reptiles and all the details in between: within the mammals group, we have cats, whales, apes, etc.; as we move further down the line, we have different species of apes such as gorilla, chimpanzees, etc. If you can visualize this as a tree of sorts, you’re already on the way to understanding what a taxonomy is at its basic level.

Taxonomies in everyday life

By organizing words and concepts based on their relationships to each other, a taxonomy is exceptionally useful at maintaining order of complex systems that are rich in data. Users of a taxonomy can classify words into hierarchical groups that logically flow through this “tree.” As humans, our natural instinct is to categorize objects in this way simply because they just make them easier to navigate and understand.

For this reason, taxonomies are the foundation to many of the systems we are used to in everyday life — it even translates to physical, tangible things outside of the digital space. Picture a clothing store that has men’s clothes on one side and women’s clothes on the other. The season’s trending pieces are at the front of the store, followed by sections that have business clothing, casual clothing, accessories, etc. These sections all have specific purposes: for one, they are easy for the shopper to navigate which ultimately benefits the customer experience, but they also serve great business value. Quite often, the trendy eye-catching pieces are at the front to lure customers in and the sale pieces are at the back because businesses want their customers to spend time rifling through the more expensive items before they can be swayed by the discount pieces.

This store setup operates on a taxonomy of sorts. Items are classified into different categories (i.e. the sections), and within these sections, pieces are given different selling values. The layout is intuitive because it is so structured, allowing for better customer experience so that they do not feel lost in the store, and easier opportunities for the store to sell pieces according to their specific, categorical value.

Taxonomies in business

An enterprise can extract value in a similar way. The data within an organization is even more complex than our shopping store, as it spans across various departments and roles. A taxonomy can help categorize this data into its respective fields and map the connections between them to make meaning. Giving your data a structured framework via comprehensive taxonomies is the first step to deriving fruitful insights about your business – but it cannot be conquered in one day. This is where taxonomy management is crucial.

What is Taxonomy Management?

Getting Started with Taxonomy Management

So, how do you get started? Get to know your data. 

The first step to managing a taxonomy is understanding the type of data you have and what it can tell you. The type of data refers to a number of things including text documents, Excel sheets, images, etc. In the workplace, it can be everything from email exchanges to invoices to employee records to sales presentations. Considering that data like this is so diverse, it is quite difficult to sort and analyze without efficient taxonomy management.

Organizing your data into a domain-specific taxonomy means using a logical and intuitive order to classify your data based on your expertise, and then fitting it into the overall structure of your business. Your domain expert, in collaboration with taxonomists, will need to periodically update the taxonomy framework in order to incorporate new changes that would reflect the data in your domain. Once a taxonomic framework is in place, it will not only ensure that data is more accessible to all users – but it will also help machines understand the hierarchy of the given information and its relationships. The key here is that once machines are given enough context with this hierarchical, tree-like taxonomy, the machines can take your data and create meaningful answers to your questions. 

Standardizing Language

An important characteristic to managing a taxonomy is your team’s ability to communicate, which can only be accomplished if users of the taxonomy are using the same terminology. Many enterprises waste time and effort looking for (and often not finding) data because of mis-labeling or different terminology across departments. To combat this issue with a taxonomy, departments can bundle synonymous terms together under one term (known as a concept), mitigating confusion while searching for information. This allows users to not only search using terms they understand and use daily, but their search will also automatically expand to include terms that are synonymous with other parts of the organization – making data more accessible and findable across all departments throughout the organization.

Extracting Meaning from Unstructured Data

Perhaps one of the more difficult problems with managing any kind of dataset is that much of the data is unstructured. An example of this is how hard it is to quantify responses from a survey that require free-text answers. Unlike multiple choice surveys whose responses can be easily scanned into a machine and pulled from, text-heavy answers use a lot of manual effort to read and analyze what is being said and whether or not it is applicable to the larger topic.

The same goes for documents that are text heavy. Many of these documents are located in different places (i.e., databases, file servers, etc.) where information can only be filtered out by humans, making it difficult to re-use and locate information in real-time. Enterprises are constantly looking for ways to improve their knowledge base by either linking to these documents, or trying to find methods to make it more accessible.

In taxonomy management, this is where text mining comes in. Text mining is the ability to extract pertinent terms and tag them so that machines can sort unstructured data. Text mining with an underlying taxonomy helps machines to further understand the context that these terms fit in, thereby ensuring that a user’s query is not only limited to the terms used in the search. When integrated with a taxonomy, a search engine is given access to information typically only available to humans, which allows for better meaning and context of a term in order to enhance search results and provide the user with a richer search experience.

Intelligent Search

The ability to search for things based on your search criteria is not the only thing that taxonomy management excels in. It also connects the dots and understands how your information is structured, helping you to find relevant information and recommend things that you may not have realized were based on your search criteria. Having a structured taxonomy in combination with text mining capabilities, helps machines understand connections and hierarchies between all of your data – which can then suggest further information relevant to the user’s search.

Think of a common Google search where you input something specific in the field like “what is a taxonomy” — the search will generate results that pertain to these keywords (i.e., the titles of the results will be exactly what you have entered), but once you have scrolled to the end you see that Google has suggested further searches such as “father of taxonomy,” “taxonomy animal kingdom,” etc. You as the user may not have thought about making these connections, but the machine has done it for you based on intelligent search capabilities, ultimately providing more fruitful research. Meaningful information provided by a machine, based on a sound taxonomy, means the ability to make better data-driven decisions and continuous intelligence that enterprises can rely on.

Agile Data

Agile enterprises understand the need to be able to pivot and shift based on growing trends within the market. They can only do this if they ensure their data isn’t rigid. While most enterprises struggle with just having an overview over all of your data points, having taxonomy management in place means enterprises are able to reuse and add data for other types of data models. The flexibility to continuously revise and update taxonomies by key stakeholders ensures that enterprises stay relevant and that their Business Intelligence is never outdated. Organizations will be able to pivot based on urgent needs and concerns without affecting daily business.

Enterprise Knowledge Graphs

An Enterprise Knowledge Graph leverages a taxonomy by taking all your data points and elevating them, thereby scaling your data and knowledge management capabilities to an enterprise level. While a taxonomy helps to organize and structure your data, an Enterprise Knowledge Graph takes it a step further by incorporating AI and Machine Learning onto a virtualized layer over your databases, helping your machine to not only learn and understand relations that are common sense to us, but also to provide real-time and relevant answers that could be dotted across all your databases. If a taxonomy is a “tree,” an Enterprise Knowledge graph is a “web.” The knowledge graph can combine different taxonomies together and map them across this web to give an overview of all your data’s relations. The advantage here is that your data is far more intricate and intuitive; it is easier to derive meaningful insights from your data when all the dots (or data points) are visibly splayed out and connected.

The benefit of having an Enterprise Knowledge Graph is that your data remains in place. By creating a virtualized layer, the Enterprise Knowledge Graph does not store data, but helps to point towards the information you need based on your query. Taxonomy management within your Enterprise Knowledge Graph also guarantees that all users (regardless of which department) will always have the most up-to-date and relevant information because it will act as the single source of truth for all your data. This means that any revisions made would change all the data points connected to it, helping to update everything relevant across all your data sources.

Commit to Taxonomy Management

As enterprises continue to struggle with the increasing volume of data created, it is abundantly clear that a structured way to organize and find information is the only way enterprises can remain agile in such a competitive market. Think back to our shopping trip, where it was easy for our customer to navigate the clothes store and even easier for the business to sell certain items based on how they were sectioned off in the store. Clothes were classified by their unique selling value and rotated out based on the trends, allowing stores to stay competitive and generate better sales. Both customer experience and business strategies were enriched simply because the experiences were driven by taxonomic structure. 

In a corporate enterprise, automated data organization across departments can help make better data-driven decisions based on reliable continuous intelligence. These decisions will not only allow enterprises to react faster, but also pivot as required in the changing market. The flexibility of a taxonomy also means that enterprises can continuously shift based on new information within their field of expertise and can leverage this information to scale their business processes. As they build upon their data and knowledge organization systems using a taxonomy, enterprises will be able to further scale their systems by incorporating AI and machine learning through an Enterprise Knowledge Graph. These semantic tools can drive the meaning and context of enterprise data, allowing for better user experiences for everyone – from everyday business users to upper management users alike.