If you’re reading this, you’re likely already convinced of the value of tags and filters when it comes to creating, saving, and retrieving content. You may even be involved in developing taxonomies at your organization. When working on initiatives aimed at enhancing targeted search and enabling content-level tagging, you’ll find that the systems that house and categorize your content each come with their own strengths and limitations regarding the extent to which they support tagging and filters. We have touched on system limitations in a past blog on Taxonomy Implementation Best Practices, and this blog revisits and expounds upon system limitations in greater detail. 

While some fortunate teams have the coveted balance of leadership support, generous budget, and ample time to shop around for a solution that supports robust taxonomy tagging and customizable filters, the reality for most of us entails having to make the best of whatever systems are already in place. Even teams that invest in a Taxonomy and Ontology Management System (TOMS) often have to navigate system limitations when integrating the tool with the existing systems. The systems that consume the taxonomy terms and filters tend to come with shortcomings that pose obstacles to optimal taxonomy implementation. Ideally, the consuming system supports hierarchical facets, captures semantic context like synonyms, supports an unlimited number of tags, and allows for advanced user permissioning, but the reality often falls short on at least one of these features. 

To an extent, these limitations should be expected, because the systems in question, be they content management systems, collaboration platforms, or learning management systems, have to fulfill a vast range of business needs. For these systems, tagging or search filters may be one of many desirable features, rather than their core function. 

In this blog, I invite two audiences to consider system limitations for taxonomy implementation: firstly, those who have the opportunity to shop around for systems to consume their taxonomy, and secondly, those who have to work around the limitations of the systems they already have in place. While this is by no means an exhaustive list, I’ll discuss some of the main considerations around taxonomy implementation for content management. 

All too often, systems are unable to support a fundamental element of taxonomies: hierarchical relationships. In these instances, the client finds themselves having to use tags or filters that don’t reflect broader and narrower relationships, and falsely represent very granular and very broad concepts at the same level.

For instance, you may have a tool that supports tagging, which is a great start for making content easier to find, but you aren’t able to indicate that tags like “Senior Health” and “Maternal Health” should be considered more specific than the broader “Health and Wellness” tag (Figure 1).

Why it Matters

The importance of hierarchies lies in the very definition of what separates a taxonomy from a mere controlled vocabulary. Because a taxonomy is a hierarchy of concepts accompanied by semantic context, one of the primary reasons a taxonomy is valuable is because of its ability to express broader and narrower relationships in a human-readable as well as machine-readable format. 

Hierarchies are especially valuable for supporting inheritance, in which attributes or properties defined at a higher level in the taxonomy are applied to lower-level elements, and, in turn, whatever qualities are true of the child concept are also true of the parent, or higher-level concepts.

In Figure 2, having hierarchical tagging would mean we wouldn’t have to tag a product with “To-Go Items,” “Beverages,” “Espresso Beverages,” and “Cafe Latte,” because we can see that Cafe Lattes are a type of Espresso Beverage, which is a type of Beverage, which are categorized under To-Go Items. 

This can be especially valuable because hierarchical tagging can help reduce the amount of work that has to go into using tags to store and retrieve content, as well as helping the user understand the significance of their tags. Without hierarchical tagging, a user may have to tag a piece of content with both the narrowest concept as well as broader concepts, rather than just apply one tag – which results in excessive tagging, depleting the tags’ value.

 

Recommendation: Develop detailed tagging guidance to compensate for system limitations, accompanied by an easily-accessible visual of the taxonomy hierarchy for users.  

System Limitation 2: Semantic Context Not Supported

In many systems, tagging – even hierarchical tagging – may be supported, but the tags themselves aren’t accompanied by any additional semantic context to provide additional guidance around what the tag means. For taxonomies adhering to Simple Knowledge Organization System standards, each taxonomy concept (or tag) should include not just the term itself (or, preferred label), but also be accompanied by alternative labels (equivalent terms or synonyms), a definition, and, if applicable, scope notes to provide additional context around intended usage or disambiguation between other concepts. However, many systems lack the ability to store or leverage this valuable context. In many cases, a tag is just a tag, and system users have to rely on implicit knowledge to interpret the broader meanings associated with each tag.

Why it Matters

Although good taxonomy design entails carefully selecting self-explanatory, clear concept labels, there can still be a certain amount of implicit knowledge required to accurately interpret, apply, and leverage tags. Ideally, anyone using a taxonomy should be able to also review alternative labels or scope notes as well as definitions, but this is often unavailable.  Alternative labels, definitions, and scope notes are essential for documenting otherwise implicit knowledge that, without which, could result in tags being inappropriately applied to content. This can result in undesirable outcomes like scope drift, incorrect tagging, and more. 

The semantic context provided by a robust taxonomy is also essential for future-proofing a taxonomy. With industries experiencing greater degrees of uncertainty and worker turnover, it’s more important than ever to avoid taking institutional knowledge for granted. For instance, a team may be initially aligned on the terminology and meaning for a taxonomy of 50 concepts, which are also tagged to content. However, let’s say that the team responsible for the taxonomy undergoes a reorganization at their company, and some individuals move to different teams, others retire, and others move to another company. Whoever inherits ownership of the taxonomy may no longer have access to the knowledge that contextualizes the meaning of those taxonomy terms. Over time, the terms lose their meaning, and are subject to different interpretations and usage.

In Figure 3, we can see that both pieces of content (a blog and a help article) mention “Tips.” Without the additional context provided by the alternative labels and definitions, it would be reasonable to have both pieces of content tagged with “Tips.” However, being able to access this context reveals that only the second article should have this tag.

 As illustrated in Figure 4, suppose a company isn’t consistent about using the term “Seller Protection” or “Merchant Protection.” Perhaps the Legal team calls it “Merchant Protection,” but customer-facing content tends to say “Seller Protection,” and internal customer support workers refer to it as “Merchant Protection.” In a case where content has to be manually tagged, just having a tag for “Seller Protection” without also indicating that “Merchant Protection” is an equivalent term would result in content not being tagged, even when it should be.

 

Recommendation: Ensure that key semantic context such as alternative labels and definitions is available to users, highlighting areas of frequent confusion.

System Limitation 3: Tag Number Limits

Another issue that users frequently encounter is where a system supports tagging, but imposes limitations on the quantity of tags supported. These limits primarily manifest themselves in two ways: firstly, by imposing a limit on the total number of tags that the system can support, or secondly, putting a limit on the number of tags that can be applied to a single piece of content. Some systems may impose both kinds of limitations.

Why it Matters

Generally speaking, limiting the number of tags you maintain in your system can actually be one of many ways to adhere to best practices in taxonomy. We frequently encounter situations where the number of tags within a system have proliferated well beyond a manageable quantity, and users may only meaningfully engage with several dozen tags out of thousands. In these instances, we often find that many of the tags are duplicates, represent concepts that are no longer applicable or relevant, or go into an excess level of granularity. However, there can be situations in which a higher number of tags may be necessary, such as when a particularly diverse range of content or data needs to be retrievable and a certain degree of specificity is required to ensure precision in search.

In one instance, we worked with a client using a CMS that could only allow for a total of 15 tags to be assigned to a single piece of content. While, in most cases, 15 tags or fewer would be an appropriate maximum number of tags to assign to a document to avoid over-tagging, this limitation could be problematic for long-form content, like complex user agreements or long scientific articles. In another instance, a system could only support 500 total tags – which would be appropriate enough for a repository of simple help articles, but may be insufficient for a database of complex healthcare topics.

Suppose a content repository houses hundreds of long, highly complex user manuals like the one shown in Figure 5. However, the content repository only allows for 12 tags to be applied to any given content item. In this situation, a 12-tag limit would be woefully inadequate.

 

Recommendation: Explore ways to accompany complex documents with additional context to complement the limited tags.

System Limitation 4: Inadequate Options for Roles and Permissions

When implementing a taxonomy with a system, whether that means configuring search filters or tagging content, it’s crucial to be able to configure user roles and permissions. On many teams, content roles are distinct; you have content strategists, content authors, content managers, a taxonomist, and so on. Furthermore, a mature taxonomy requires governance, where well-defined roles dictate who is responsible for deciding upon and carrying out updates to a taxonomy, and effective governance is difficult to achieve without a system that supports distinct permissions to accompany these defined roles. However, not all systems have customizable roles to support governance or even basic taxonomy management. In some cases, new tags can be freely entered, or anyone can have the ability to apply tags to content.

Why it Matters

Anyone who’s been involved in content tagging knows how challenging it can be to successfully guide a group of taggers to consistently tag content the same way. Some may take an exhaustive approach, tagging content with concepts that may only be tangentially relevant or only mentioned in passing. Others may be more cautious, only applying tags when a concept acts as one of the core themes of a piece of content. While drafting detailed tagging guidelines and providing robust tagging training can go a long way in mitigating these differences in tagging approaches, this is not a foolproof approach. 

These issues can be only further exacerbated when systems allow for any individual to add new tags to a system without any formal governance review. In one instance, we worked with a nonprofit client where it was easy to add new tags to a system, and over time, the total number of tags grew to over 2,000. In another case, at a large financial technology organization I worked with, there were no system limitations on how many entries could be added to a drop-down menu of customer contact reasons, which resulted in a list of over 1,400 entries, the majority of which were duplicates. In situations like these, tags lose the value they were meant to provide, and may even foster more confusion than if there were no tags at all.

In the example shown in Figure 6, a system where any users can create a new filter or tag without any user roles restricting permissions can easily lead to a proliferation of overlapping or duplicated terms. Here, we see a drop-down menu of “Account Questions,” where users were able to create new tags without having to review which tags are already in place, resulting in six different selections about creating an account. Lack of controls around tags or filter creation can quickly result in an unwieldy tool that is difficult to use. If tags or filters become bloated enough, users may even select a random term or only pick the top term rather than scroll through hundreds of entries.

Figure 7 illustrates a situation where two different content authors were asked to tag content. On the left, one tagger got carried away, providing far more tags than were necessary, and misinterpreting “Brand Health” to mean physical health. On the right, a different tagger was more strategic and selective, and only applied tags that would be necessary to retrieve the intended report. User controls help ensure that only qualified individuals can create, apply, and manage tags, and are essential to prevent an uncontrolled proliferation of tags, enable consistent tagging, and support ongoing governance.

 

Recommendation: Provide detailed tagging guidelines as well as training to ensure aligned approaches. For systems where new entries can be freely added, communicate frequently about the importance of preventing accidental entries, and schedule frequent clean-ups.

Conclusion

When navigating the transition from designing a taxonomy to implementing it in the intended systems, it can be common to encounter a gap between ideal implementation (hierarchical tagging without system-imposed limits, controlled by tight role-based user permissions), and reality. A certain degree of flexibility, creativity, and above all, effective documentation will be necessary to develop effective strategies to get as much out of your taxonomy as possible in spite of any challenging system limitations. Developing a detailed set of use cases that the taxonomy is intended to address is essential, so you can evaluate which system limitations are acceptable and which should be considered deal-breakers for the success of your project. 

Are you in the process of implementing a taxonomy at your organization, but struggling to navigate any of these system limitations? We have extensive experience navigating these system limitations to guide organizations to successful, impactful taxonomy implementations, and would be happy to help. Contact us to learn more.

The post Navigating System Limitations for Taxonomy Implementation appeared first on Enterprise Knowledge.

Just as a semantic layer passes through degrees of maturity and complexity as it is developed and operationalized, so too does a taxonomy. While a taxonomy comprises only one facet of a fully realized Semantic Layer, every incremental increase in its granularity and scope can have a compounding effect in terms of unlocking additional solutions for the organization. While it can be tempting to assume that only a fully mature taxonomy is capable of delivering measurable value for the organization that developed it, each iteration of a taxonomy provides value that should be acknowledged, quantified, and celebrated to advocate for continued support of the taxonomy’s ongoing development.  

Taxonomy Maturity Stages

A taxonomy’s maturity can be measured across five levels: Basic, Foundational, Operational, Institutional, and Transformational. Taken as a snapshot from our full semantic layer maturity framework, the following diagram illustrates each of these levels in terms of their taxonomy components, technical manifestation, and what valuable outcomes can be expected from each at a high level. 

Basic Taxonomy

A basic taxonomy lacks depth, and is essentially a folksonomy (an informal, non-hierarchical classification system where users apply public tags). At this stage, a basic taxonomy is only inconsistently applied across departments. 

As an example, a single business unit (Marketing) may have begun developing a basic taxonomy that other business units (Sales) may be starting to integrate with their product taxonomy. 

Components and Technical Manifestation at this Level

• Basic taxonomies are only developed for limited, specific use cases, often for a particular team or subset of an organization.
• At this stage of maturity, a taxonomy expresses little granularity, and may have up to three levels of broader/narrower relationships. 
• A basic taxonomy is likely maintained in a spreadsheet, rather than a taxonomy management system (TMS). The taxonomy may be implemented in a rudimentary form, like being expressed in file structures. Taxonomy concepts are not yet tagged to assets. 
• At this stage, the taxonomy functions primarily as a proof of concept. The taxonomy has not yet been widely validated or socialized, and is likely only known by the team building it. It may represent an intentionally narrow scope that can then be scaled as the team builds buy-in with stakeholders. 

Outcomes and Value 

• The basic taxonomy provides an essential foundation to build upon. If it is well-designed, the work invested in this stage can serve as a model for other functional areas of the organization to adopt for their own use cases. 
• At this stage, the value is typically limited to providing a proof of concept to demonstrate what taxonomy is, and working towards establishing consistent terminology within a department.

Foundational Taxonomy

The foundational taxonomy is not yet wholly standardized, but growing momentum helps to drive adoption and standardization across systems and business units. The taxonomy can support simple data enrichment by adding semantic context (like relevant location data, contact information, definitions, or subcategories) to an existing data set. Often, a dedicated taxonomy management solution (TMS) is procured at this stage, and it may be unscalable to proceed to the next level of maturity without one. 

Components and Technical Manifestation at this Level

• The taxonomy is imbued with semantic context such as definitions, scope notes, and alternative labels, along with the expected hierarchical relationships between concepts. A foundational taxonomy exhibits a greater level of granularity beyond the basic level. 
• The taxonomy is no longer only housed in a spreadsheet, and is maintained in a Taxonomy Management Solution (TMS). This makes it easier to ensure that the taxonomy’s format adheres to semantic web frameworks (such as SKOS, the Simple Knowledge Organization System). 
• The addition of this context serves the fundamental purpose of supporting and standardizing semantic understanding within an organization by clarifying and enforcing preferred terms while still capturing alternative terms.  
• Some degree of implementation has been realized – for instance, the tagging of a representative set of content or data assets.
• The taxonomy team actively engages in efforts to socialize and promote the taxonomy project to build awareness and support among stakeholders. 
• A taxonomy governance team has been established for ongoing validation, maintenance, and change management. 

Outcomes and Value

• At this stage, the taxonomy can provide more measurable benefits to the organization. For instance, a foundational taxonomy can support content audits for all content that has been auto-tagged. 
• The taxonomy can support more advanced data analytics – for instance, users can get more granular insights into which topics are the most represented in content. 
• The foundational taxonomy can be scaled to incorporate backlog use cases or other departments in the organization, and can be considered a product to be replicated and more broadly socialized.
• The taxonomy can be enhanced by adding linked models and/or concept mapping.

Operational Taxonomy

The operational taxonomy is standardized, used regularly and consistently across teams, and is integrated with other components or applications. 

At this stage, the taxonomy is integrated with key systems like a content management system (CMS), learning management system (LMS), or similar. Users are able to interact with the taxonomy directly through the system-powered apps they work in, because the systems consume the taxonomy.

Components and Technical Manifestation at this Level

• At this level of maturity, advanced integrations have been realized – for instance, the taxonomy is integrated into search for the organization’s intranet, or the taxonomy’s semantic context has been leveraged as training data for generative AI-powered chatbots.
• At the operational level, the taxonomy acts as a source of truth for multiple use cases, and has been expanded to cover multiple key areas of the organization, such as Customer Operations, Product, and Content Operations. 
• By this stage, content tagging has been seamlessly integrated into the content creation process, in which content creators apply relevant tags prior to publishing, or automatic tagging ensures content is applied to current and newly-published content. 
• A TMS has been acquired, and is implemented with key systems, such as the organization’s LMS, intranet, or CMS. 
• The taxonomy is subject to ongoing governance by a taxonomy governance team, and key stakeholders in the organization are informed of key updates or changes to the taxonomy.

Outcomes and Value 

• The taxonomy is integrated with essential data sources to provide or consume data directly. As a result, users interacting with the systems that are connected to the taxonomy are able to experience the additional structure and clarity provided by the taxonomy via features like search filters, navigational structures, and content tags. 
• The taxonomy can support enhanced data analytics, such as tracking the click-through rate (CTR) of content tagged with particular topics. 

Institutional Taxonomy

The institutional taxonomy is fully integrated into daily operations. Rigorous governance and change management capabilities are in place. 

By now, seamless integrations between the taxonomy and other systems have been established. Ongoing taxonomy maintenance work poses no disruption to day-to-day operations, and updates to the taxonomy are automatically pushed to all impacted systems.

Components and Technical Manifestation at this Level

• The taxonomy, or taxonomies, are fully integrated into daily operations across teams and functional areas – for instance, the taxonomy supports dynamic content delivery for customer support workers, the customer-facing product taxonomy facilitates faceted search for online shopping, and so on. 
• The organization’s use cases are supported by the taxonomy, which supports core goals such as ensuring a shared understanding of key concepts and their meaning, providing a consistent framework for the representation of data across systems, or representing the fundamental components of an organization across systems. 
• Governance roles, policies, and procedures are fully established and follow a regular cadence. 

Outcomes and Value

• At this stage of maturity, the taxonomy has been scaled to the extent that it can be considered an enterprise taxonomy; it covers all foundational areas, is utilized by all business units, and is poised to support key organizational operations. At this stage, the taxonomy drives a key enterprise-level use case. 
• Data connectivity is supported across the organization; the taxonomy unifies language across teams and systems, reducing errors and data discrepancies. 
• Internal as well as external users benefit from taxonomy-enhanced search in the form of query expansion. 

Transformational Taxonomy

The transformational taxonomy drives data classification and advanced analytics, informing and enhancing AI-driven processes. At this stage, the taxonomy provides significant functionality supporting an integrated semantic layer. 

Components and Technical Manifestation at this Level

• The taxonomy can support the delivery of personalized, dynamic content for internal or external users for more impactful customer support or marketing outreach campaigns.
• The taxonomy is inextricably tied to other key components of the semantic layer’s operating model. The taxonomy provides data for the knowledge graph, provides a hierarchy for the ontology, categorizes the data in the data catalog, and enriches the business glossary with additional semantic context. These connections help power semantic search, analytics, recommendation systems, discoverability, and other semantic applications. 
• Taxonomy governance roles are embedded in functional groups. Feedback on the taxonomy is shared regularly, introductory taxonomy training is widely available, and there is common understanding of how to both use the taxonomy and provide feedback. 
• Taxonomies are well-supported by defined metrics and reporting and, in turn, provide a source of truth to power consistent reporting and data analytics.  

Outcomes and Value 

• At this stage, the taxonomy (within the broader semantic layer) drives multiple enterprise-level use cases. For instance, this could include self-service performance monitoring to support strategic planning, or facilitating efficient data analytics across previously-siloed datasets. 
• Taxonomy labeling of structured and/or unstructured data powers Machine Learning (ML) and Artificial Intelligence (AI) development and applications. 

Taxonomy Use Cases 

Low Maturity Example

In many instances, EK partners with clients to help develop taxonomies in their earliest stages. Recently, a data and AI platform company engaged EK to lead a taxonomy workshop covering best practices in taxonomy design, validation activities, taxonomy governance, and developing an implementation roadmap. Prior to EK’s engagement, the company was in the process of developing a centralized marketing taxonomy. As the taxonomy was maintained in a shared spreadsheet, lacked a defined governance process, and lacked consistent design guidelines, it met the basic level of maturity. However, after the workshop, the client’s taxonomy design team left with a refreshed understanding of taxonomy design best practices, clarified user personas, an appreciation of the value of semantic web standards, a clear taxonomy development roadmap, and a scaled-down focus on prioritized pilots to build a starter taxonomy. 

By clarifying and narrowing their use cases, identifying their key stakeholders and their roles in taxonomy governance, and reworking the taxonomy to reflect design principles grounded in semantic standards, the taxonomy team was equipped to elevate their taxonomy from a basic level of maturity to work towards becoming foundational. 

High Maturity Example 

EK’s collaboration with a major international retailer illustrates an example of the evolution towards a highly-mature semantic layer supported by a robust taxonomy. EK partnered with the retailer’s Learning Team to develop a Learning Content Database to enable an enterprise view of their learning content. Initially, the organization’s learning team lacked a standardized taxonomy. This made it difficult to identify obsolete content, update outdated content, or address training gaps. Without consistent terminology or content categorization, it was especially challenging to search effectively and identify existing learning content that could be improved, forcing the learning team to waste time creating new content. As a result, store associates struggled to search for the right instructional resources, hindering their ability to learn about new roles, understand procedures, and adhere to compliance requirements. 

To address these issues, EK first partnered with the learning team to develop a standardized taxonomy. The taxonomy crystallized brand-approved language which was then tagged to learning content. Next, EK developed a tailored governance plan to ensure the ongoing maintenance of the taxonomy, and provided guidance around taxonomy implementation to ensure optimal outcomes around reducing time spent searching for content and simplifying the process of tagging content with metadata. With the taxonomy at a sufficient stage of maturity, EK was then able to build the Learning Content Database, which enabled users to locate learning content across previously disparate, disconnected systems, now in a central location. 

Conclusion

Every taxonomy – from the basic starter taxonomy to the highly-developed taxonomy with robust semantic context connected to an ontology – can provide value to its organization. As a taxonomy grows in maturity, each next level of development unlocks increasingly complex solutions. From driving alignment around key terms for products and resources, supporting content audits, enabling complex data analytics across systems, or powering semantic search, the progressive advancement of a taxonomy’s complexity and semantic richness translates to tangible business value. These advancements can also act as a flywheel, where each improvement makes it easier to continue to drive buy-in, secure necessary resources, and achieve greater enhancements. 

If you are looking to learn more about how other organizations have benefitted from advanced taxonomy implementations, read more from our case studies. If you want additional guidance on how to take your organization’s taxonomy to the next level, contact us to learn more about our taxonomy design services and workshops.

The post Semantic Layer Maturity Framework Series: Taxonomy appeared first on Enterprise Knowledge.

The post Why Your Taxonomy Needs SKOS  appeared first on Enterprise Knowledge.

Participants learned:

• Ways to build buy-in by identifying “almost taxonomies;”

• Ways to illustrate how taxonomies can ease specific pain points, benefit end users, and drive cost savings;

• How to develop a working knowledge of generative AI, and establish a realistic way to integrate taxonomy; and

• How to communicate tangible results and value at each taxonomy development milestone.

The post Consulting from Within: Best Practices for the Solo Taxonomist appeared first on Enterprise Knowledge.