“Hello, is it me you’re looking for? / ‘Cause I wonder where you are / And I wonder what you do / Are you somewhere feeling lonely?” – Lionel Richie, Hello

One of the most challenging activities in taxonomy work is communicating the value of taxonomy to potential business stakeholders. With so many shiny, promising technologies and methodologies, it can be daunting for the taxonomy strategist to win over converts to taxonomy use. Taxonomies and their applications are often misunderstood or are narrowly focused on a few common use cases like navigation. While business users can clearly articulate their needs, they may not be able to connect those needs to how taxonomies can be applied in the business.

The taxonomy strategist must be able to communicate the value of taxonomy while expressing the complexity of semantic structures like ontologies and their supporting technologies simply and succinctly to a variety of business stakeholders. 

Communicating the Value through Examples

To gain taxonomy users, it’s essential to communicate the value of taxonomy. One way to start is to seek out areas taxonomy can directly address, find examples of the current state problems, provide taxonomy-based solutions, and then communicate the findings to the business owners. This process can be initiated by the taxonomy strategist or by the business owners themselves, assuming, of course, they know to contact the taxonomy team in an effort to answer their need.

One simple, powerful example is to review a search-dependent organizational website–which could be an internal intranet or external, public-facing website–and collect examples of navigational and search barriers causing confusion, poor search results, or revenue-losing scenarios. For each example, provide an explanation of how taxonomy might help. For navigational issues, the taxonomy solution may be category restructuring or improved, facet-based results filtering aligned to the typical user journey. For search retrieval issues, taxonomy may be used for typeahead search keyword matching or to improve search relevance to include more accurate or additional results through content tagging or keywording. Navigation and search are often close to time-saving or profit-driving activities, improving the efficiency and bottom line of the organization. Search examples and their potential taxonomy solutions, therefore, are closer to the source of organizational revenue and make convincing use cases.

As generative AI becomes more prevalent in organizations, finding examples of general or inaccurate results and how an enterprise, domain-specific taxonomy (and ontology) can act as foundational training data to improve those results can result in convincing proof of concept projects. Generative AI and machine learning models can seem like magic to the average user who may not know the amount of time and data it takes to train a model to produce accurate and useful results. Providing examples of poor machine learning model output can illuminate the need for clean, accurate foundational data. As an organizational source of truth, taxonomies can provide such semantic data.

To overcome user confusion about what taxonomy means or clarify what they think taxonomy means, try starting with the end result and work backwards. When assessing the value of a new bathroom faucet, someone will look at whether the fixtures look appealing and if hot and cold water comes out as expected. Initially, no one is interested in the pipes. Taxonomy, unflatteringly, is the pipeline infrastructure providing clean water to downstream consuming systems. First show excellent search results or machine learning outcomes and then explain how taxonomy is the basis for those results. If business stakeholders are interested in taxonomy, all the better for your work and evangelization. If they aren’t, let them be impressed by the final state and develop a process of working together to get to and maintain that final result.

Communicating the Value through Time and ROI

One potential stakeholder hesitation may be the time it takes to perform discovery, conduct the build, and put taxonomy values into production. This process can take time in the initial business stakeholder relationship. Once established, however, the speed at which business users can request concepts and see them live can move as quickly as your organizational systems can handle. People often believe they need to “move at the speed of business”, which, ironically, they think is fast but is more often cumbersome, manual, and slow. What they want is the magical now in which thought is converted to action faster than Captain Kirk can have his shirt ripped when first confronting an alien species.

Machine learning techniques, once perfected, can offer the kind of rapid response business owners are looking for, but only after a lot of training. Specifically, a lot of training on assets and data tagged with taxonomy. Too often, the “magic” of artificial intelligence business users are sold isn’t artificial at all: it is thousands of hours of tagging content and training models to get the desired results. If done properly, there’s nothing wrong with using machine learning models to quickly react to trending topics or generate text on the fly. However, the slower growth of a taxonomy, as I cover in my blog The Taxonomy Tortoise and the ML Hare, actually creates speed in other areas, saving time in responding to consumers’ direct search queries and tagging content to train and evolve machine learning models. Communicating the need for time investment up front to generate time-savings later can be compelling.

Communicating taxonomy ROI, which I covered a few years ago in my blog for Synaptica, Running a Successful Taxonomy Campaign, can be extremely difficult. How do you explain how words become money? Again, show the examples. Mining successful and failed search results and mapping these to taxonomy as metadata tagged to assets can show a direct line between creating taxonomy concepts, applying them to content, and successful search results that end in a product purchase. Going back to time, time is money: time employees spend manually creating, tagging, and manipulating content which drives sales; time spent training machine learning models; time spent seeking information which has not been tagged with metadata. Ramping up taxonomy processes to more quickly tag content and put words into production will result in quicker time to money and realized ROI. While starting taxonomies can be slow at first, the more success the taxonomy strategist has in engaging business users, the more quickly the taxonomy is built out and covers the breadth needed to tag assets and express important concepts users are seeking.

Communicating Complexity

Communicating the nuance and complexity of taxonomies and ontologies may be necessary as the details of a pending or ongoing project develop. Few business contacts need to know the difference between a flat list, taxonomy, thesauri, or ontology. In fact, I find there are disagreements about the differences even among practitioners. That said, users can come to the discussion believing that taxonomies are only hierarchical lists of terms. For most practical discussions, I use the term “taxonomy” to include flat and hierarchical lists of terms, properties, and hierarchical and associative relationships. I rarely bother with ontology concepts like classes unless they are necessary to meet the project objectives.

If these terms do need clarification, however, I often clarify with simplicity. Taxonomies are concepts (preferred labels) that include synonyms (alternative labels) and other metadata attributes (properties) and these concepts can be related hierarchically and through custom relationships (associative relationships). When discussing ontology, I usually state that taxonomies are the words you want to use and the ontology includes the rules for the words you want to use. For example, how concepts are grouped (classes), how they can be related to each other (domain and range constrained by classes), and whether certain properties can be made available for a use case (properties constrained by classes). That’s often all a user needs to know.

There are more advanced use cases, like machine learning, which is, in my experience, more of a mapping of ideas than an education. Data scientists usually use all the same concepts as taxonomies and ontologies but may use different terms to express them. After one or two conversations, the mappings are understood and the complexity is simplified. It’s not often a data scientist needs convincing to leverage taxonomies, but getting on the same page with conceptual ideas is a good way to make taxonomy value clear.

In large organizations, there is usually information architecture complexity as well. Because of this, taxonomy can often become necessarily complex as values are consumed by and flow through various systems. Understanding this workflow is not always a prerequisite for understanding the value of taxonomy, however, and does not need to weigh down conversations with potential business stakeholders. If it does become necessary, simplify those information architecture diagrams into simple flowcharts between systems, showing at a high level how taxonomy concepts move from system to system and what they do in each.

Being a taxonomy strategist is challenging, but is a necessary part of the job for taxonomy to show and prove its value in the organization.