A well-defined Semantic Layer strategy is essential to avoid costly missteps in planning or execution, secure stakeholder alignment and buy-in, and ensure long-term scalability of models and tooling.

This blog outlines the key components of a successful Semantic Layer strategy, explaining how each component supports a scalable implementation and contributes to unlocking greater value from your data.

What is a Semantic Layer?

The Semantic Layer is a framework that adds rich structure and meaning to data by applying categorization models (such as taxonomies and ontologies) and using semantic technologies like graph databases and data catalogs. Your Semantic Layer should be a connective tissue that leverages a shared language to unify information across systems, tools, and domains. 

Data-rich organizations often manage information across a growing number of siloed repositories, platforms, and tools. The lack of a shared structure for how data is described and connected across these systems ultimately slows innovation and undermines initiatives. Importantly, your semantic layer enables humans and machines to interpret data in context and lays the foundation for enterprise-wide AI capabilities.    

What is a Semantic Layer Strategy?

A Semantic Layer Strategy is a tailored vision outlining the value of using knowledge assets to enable new tools and create insights through semantic approaches. This approach ensures your organization’s semantic efforts are focused, feasible, and value-driven by aligning business priorities with technical implementation. 

Regardless of your organization’s size, maturity, or goals, a strong Semantic Layer Strategy enables you to achieve the following:

1. Articulate a clear vision and value proposition.

Without a clear vision, semantic layer initiatives risk becoming scattered and mismanaged, with teams pulling in different directions and value to the organization left unclear. The Semantic Layer vision serves as the “North Star,” or guiding principle for planning, design, and execution. Organizations can realize a variety of use cases via a Semantic Layer (including advanced search, recommendation engines, personalized knowledge delivery, and more), and Semantic Layer Strategy helps to define and align on what a Semantic Layer can solve for your organization.

The vision statement clearly answers three core questions:

• What is the business problem you are trying to solve?
• What outcomes and capabilities are you enabling?
• How will you measure success?

These three items create a strategic narrative that business and technical stakeholders alike can understand, and enable discussions to gain executive buy-in and prioritize initiative efforts. 

Enterprise Knowledge Case Study (Risk Mitigation for a Wall Street Bank): EK led the development of a  data strategy for operational risk for a bank seeking to create a unified view of highly regulated data dispersed across siloed repositories. By framing a clear vision statement for the Bank’s semantic layer, EK guided the firm to establish a multi-year program to expand the scope of data and continually enable new data insights and capabilities that were previously impossible. For example, users of a risk application could access information from multiple repositories in a single knowledge panel within the tool rather than hunting for it in siloed applications. The Bank’s Semantic Layer vision is contained in a single easy-to-understand one-pager  that has been used repeatedly as a rallying point to communicate value across the enterprise, win executive sponsorship, and onboard additional business groups into the semantic layer initiative. 

2. Assess your current organizational semantic maturity.

A semantic maturity assessment looks at the semantic structures, programs, processes, knowledge assets and overall awareness that already exist at your organization. Understanding where your organization lies on the semantic maturity spectrum is essential for setting realistic goals and sequencing a path to greater maturity. 

• Less mature organizations may lack formal taxonomies or ontologies, or may have taxonomies and ontologies that are outdated, inconsistently applied, or not integrated across systems. They have limited (or no) semantic tooling and few internal semantic champions. Their knowledge assets are isolated, inconsistently tagged (or untagged) documents that require human interpretation to understand and are difficult for systems to find or connect.
• More mature organizations typically have well-maintained taxonomies and/or ontologies, have established governance processes, and actively use semantic tooling such as knowledge graphs or business glossaries. More than likely, there are individuals or groups who advocate for the adoption of these tools and processes within the organization. Their knowledge assets are well-structured, consistently tagged, and interconnected pieces of content that both humans and machines can easily discover, interpret, and reuse.

Enterprise Knowledge Case Study (Risk Mitigation for a Wall Street Bank): EK conducted a comprehensive semantic maturity assessment of the current state of the Bank’s semantics program to uncover strengths, gaps, and opportunities. This assessment included:

• Knowledge Asset Assessment: Evaluated the connectedness, completeness, and consistency of existing risk knowledge assets, identifying opportunities to enrich and restructure them to support redesigned application workflows.
• Ontology Evaluation: Reviewed existing ontologies describing risk at the firm to assess accuracy, currency, semantic standards compliance, and maintenance practices.
• Category Model Evaluation: Created a taxonomy tracker to evaluate candidate categories for a unified category management program, focusing on quality, ownership, and ongoing governance.
• Architecture Gap Analysis and Tooling Recommendation : Reviewed existing applications, APIs, and integrations to determine whether components should be reused, replaced, or rebuilt.
• People & Roles Assessment: Designed a target operating model to identify team structures, collaboration patterns, and missing roles or skills that are critical for semantic growth.

Together, these evaluations provided a clear benchmark of maturity and guided a right-sized strategy for the bank. 

3. Create a shared conceptual knowledge asset model. 

When it comes to strategy, executive stakeholders don’t want to see exhaustive technical documentation–they want to see impact. A high-level visual model of what your Semantic Layer will achieve brings a Semantic Layer Strategy to life by showing how connected knowledge assets can enable better decisions and new insights. 

Your data model should show, in broad strokes, what kinds of data will be connected at the conceptual level. For example, your data model could show that people, business units, and sales reports can be connected to answer questions like, “How many people in the United States created documents about X Law?” or “What laws apply to me when writing a contract in Wisconsin?” 

In sum, it should focus on how people and systems will benefit from the relationships between data, enabling clearer communication and shared understanding of your Semantic Layer’s use cases. 

Enterprise Knowledge Case Study (Risk Mitigation for a Wall Street Bank): EK collaborated with data owners to map out core concepts and their relationships in a single, digestible diagram. The conceptual knowledge asset model served as a shared reference point for both business and technical stakeholders, grounding executive conversations about Semantic Layer priorities and guiding onboarding decisions for data and systems. 

By simplifying complex data relationships into a clear visual, EK enabled alignment across technical and non-technical audiences and built momentum for the Semantic Layer initiative.

4. Develop a practical and iterative roadmap for implementation and scale.

With your vision, assessment, and foundational conceptual model in place, the next step is translating your strategy into execution. Your Semantic Layer roadmap should be outcome-driven, iterative, and actionable. A well-constructed roadmap provides not only a starting point for your Semantic Layer initiative, but also a mechanism for continuous alignment as business priorities evolve. 

Importantly, your roadmap should not be a rigid set of instructions; rather, it should act as a living guide. As your semantic maturity increases and business needs shift, the roadmap should adapt to reflect new opportunities while keeping long-term goals in focus. While the roadmap may be more detailed and technically advanced for highly mature organizations, less mature organizations may focus their roadmap on broader strokes such as tool procurement and initial category modeling. In both cases, the roadmap should be tailored to the organization’s unique needs and maturity, ensuring it is practical, actionable, and aligned to real priorities.

Enterprise Knowledge Case Study (Risk Mitigation for a Wall Street Bank): EK led the creation of a roadmap focused on expanding the firm’s existing semantic layer. Through planning sessions, EK identified the necessary categories, ontologies, tooling, and architecture uplifts needed to chart forward on their Semantic Layer journey. Once a strong foundation was built, additional planning sessions centered on adding new categories, onboarding additional data concepts, and refining ontologies to increase coverage and usability. Through sessions with key stakeholders responsible for the growth of the program, EK prioritized high-value expansion opportunities and recommended governance practices to sustain long-term scale. This enabled the firm to confidently evolve its Semantic Layer while maintaining alignment with business priorities and demonstrating measurable impact across the organization.

Conclusion

A successful Semantic Layer Strategy doesn’t come from technology alone; it comes from a clear vision, organizational alignment, and intentional design. Whether you’re just getting started on your semantics journey or refining your Semantic Layer approach, Enterprise Knowledge can support your organization. Contact us at info@enterprise-knowledge.com to discuss how we can help bring your Semantic Layer strategy to life.

The post Semantic Layer Strategy: The Core Components You Need for Successfully Implementing a Semantic Layer appeared first on Enterprise Knowledge.

The question of data AI readiness stems from the very real concern that when AI is pointed at data that isn’t correct or that doesn’t have the right context associated with it, organizations could face risks to their reputation, their revenue, or their customers’ privacy. With the additional nuance that data brings by often being presented in formats that require transformation, lacking in context, and frequently containing multiple duplicates or near-duplicates with little explanation of their meaning, data (although seemingly already structured and ready for machine consumption) requires greater care than other forms of knowledge assets to comprise part of a trusted AI solution. 

This blog focuses on the key actions an organization needs to perform to ensure their data is ready to be consumed by AI. By following the steps below, an organization can use AI-ready data to develop end-products that are trustworthy, reliable, and transparent in their decision making.

1) Understand What You Mean by “Data” (Data Asset and Scope Definition)

Data is more than what we typically picture it as. Broadly, data is any raw information that can be interpreted to garner meaning or insights on a certain topic. While the typical understanding of data revolves around relational databases and tables galore, often with esoteric metrics filling their rows and columns, data takes a number of forms, which can often be surprising. In terms of format, while data can be in traditional SQL databases and formats, NoSQL data is growing in usage, in forms ranging from key-value pairs to JSON documents to graph databases. Plain, unstructured text such as emails, social media posts, and policy documents are also forms of data, but traditionally not included within the enterprise definition. Finally, data comes from myriad sources—from live machine data on a manufacturing floor to the same manufacturing plant’s Human Resources Management System (HRMS). Data can also be categorized by its business role: operational data that drives day-to-day processes, transactional data that records business exchanges, and even purchased or third-party data brought in to enrich internal datasets. Increasingly, organizations treat data itself as a product, packaged and maintained with the same rigor as software, and rely on data metrics to measure quality, performance, and impact of business assets.

All these forms and types of data meet the definition of a knowledge asset—information and expertise that an organization can use to create value, which can be connected with other knowledge assets. No matter the format or repository type, ingested, AI-ready data can form the backbone of a valuable AI solution by allowing business-specific questions to be answered reliably in an explainable manner. This raises the question to organizational decision makers—what within our data landscape needs to be included in our AI solution? From your definition of what data is, start thinking of what to add iteratively. What systems contain the highest priority data? What datasets would provide the most value to end users? Select high-value data in easy-to-transform formats that allows end users to see the value in your solution. This can garner excitement across departments and help support future efforts to introduce additional data into your AI environment. 

2) Ensure Quality (Data Cleanup)

The majority of organizations we’ve worked with have experienced issues with not knowing what data they have or what it’s intended to be used for. This is especially common in large enterprise settings as the sheer scale and variety of data can breed an environment where data becomes lost, buried, or degrades in quality. This sprawl occurs alongside another common problem, where multiple versions of the same dataset exist, with slight variations in the data they contain. Furthermore, the issue is exacerbated by yet another frequent challenge—a lack of business context. When data lacks context, neither humans nor AI can reliably determine the most up-to-date version, the assumptions and/or conditions in place when said data was collected, or even if the data warrants retention.

Once AI is introduced, these potential issues are only compounded. If an AI system is provided data that is out of date or of low quality, the model will ultimately fail to provide reliable answers to user queries. When data is collected for a specific purpose, such as identifying product preferences across customer segments, but not labeled for said use, and an AI model leverages that data for a completely separate purpose, such as dynamic pricing models, harmful biases can be introduced into the results that negatively impact both the customer and the organization.

Thankfully, there are several methods available to organizations today that allow them to inventory and restructure their data to fix these issues. Examples include data dictionaries, master data (MDM data), and reference data that help standardize data across an organization and help point to what is available at large. Additionally, data catalogs are a proven tool to identify what data exists within an organization, and include versioning and metadata features that can help label data with their versions and context. To help populate catalogs and data dictionaries and to create MDM/reference data, performing a data audit alongside stewards can help rediscover lost context and label data for better understanding by humans and machines alike. Another way to deduplicate, disambiguate, and contextualize data assets is through lineage. Lineage is a feature included in many metadata management tools that stores and displays metadata regarding source systems, creation and modification dates, and file contributors. Using this lineage metadata, data stewards can select which version of a data asset is the most current or relevant for a specific use case and only expose said asset to AI. These methods to ensure data quality and facilitate data stewardship can aid in action towards a larger governance framework. Finally, at a larger scale, a semantic layer can unify data and its meaning for easier ingestion into an AI solution, assist with deduplication efforts, and break down silos between different data users and consumers of knowledge assets at large. 

Separately, for the elimination of duplicate/near-duplicate data, entity resolution can autonomously parse the content of data assets, deduplicate them, and point AI to the most relevant, recent, or reliable data asset to answer a question. 

3) Fill Gaps (Data Creation or Acquisition)

With your organization’s data inventoried and priorities identified, it’s time to start identifying what gaps exist in your data landscape in light of the business questions and challenges you are looking to address. First, ask use case-based questions. Based on your identified use cases, what data would an AI model need to answer topical questions that your organization doesn’t already possess?

At a higher level, gaps in use cases for your AI solution will also exist. To drive use case creation forward, consider the use of a data model, entity relationship diagram (ERD), or ontology to serve as the conceptual map on which all organizational data exists. With a complete data inventory, an ontology can help outline the process by which AI solutions would answer questions at a high level, thanks to being both machine and human-readable. By traversing the ontology or data model, you can design user journeys and create questions that form the basis of novel use cases.

Often, gaps are identified that require knowledge assets outside of data to fill. A data model or ontology can help identify related assets, as they function independently of their asset type. Moreover, standardized metadata across knowledge assets and asset types can enrich assets, link them to one another, and provide insights previously not possible. When instantiated in a solution alongside a knowledge graph, this forms a semantic layer where data assets, such as data products or metrics, gain context and maturity based on related knowledge assets. We were able to enhance the performance of a large retail chain’s analytics team through such an approach utilizing a semantic layer.

To fill these gaps, organizations can look to collect or create more data, as well as purchase publicly available/incorporate open-source datasets (build vs. buy). Another common method of filling identified organizational gaps is the creation of content (and other non-data knowledge assets) to identify a gap via the extraction of tacit organizational knowledge. This is a method that more chief data officers/chief data and AI officers (CDOs/CDAOs) are employing, as their roles expand and reliance on structured data alone to gather insights and solve problems is no longer feasible.

As a whole, this process will drive future knowledge asset collection, creation, and procurement efforts and consequently is a crucial step in ensuring data at large is AI ready. If no such data exists for AI to rely on for certain use cases, users will be presented unreliable, hallucination-based answers, or in a best-case scenario, no answer at all. Yet as part of a solid governance plan as mentioned earlier, the continuation of the gap analysis process post-solution deployment can empower organizations to continually identify—and close—knowledge gaps, continuously improving data AI readiness and AI solution maturity.

4) Add Structure and Context (Semantic Components)

A key component of making data AI-ready is structure—not within the data per se (e.g., JSON, SQL, Excel), but the structure relating the data to use cases. As a term, ‘structure’ added meaning to knowledge assets in our previous blog, but can introduce confusion as a misnomer in this section. Consequently, ‘structure’ will refer to the added, machine-readable context a semantic model adds to data assets, rather than the format of the data assets themselves, as data loses meaning once taken out of the structure or format it is stored in (e.g., as takes place when retrieved by AI).

Although we touched on one type of semantic model in the previous step, there are three semantic models that work together to ensure data AI readiness: business glossaries, taxonomies, and ontologies. Adding semantics to data for the purpose of getting it ready for AI allows an organization to help users understand the meaning of the data they’re working with. Together, taxonomies, ontologies, and business glossaries imbue data with the context needed for an AI model to fully grasp the data’s meaning and make optimal use of it to answer user queries. 

Let’s dive into the business glossary first. Business glossaries define business context-specific terms that are often found in datasets in a plaintext, easy-to-understand manner. For AI models which are often trained generally, these glossary terms can further assist in the selection of the correct data needed to answer a user query. 

Taxonomies group knowledge assets into broader and narrower categories, providing a level of hierarchical organization not available with traditional business glossaries. This can help data AI readiness in manifold ways. By standardizing terminology (e.g., referring to “automobile,” “car,” and “vehicle” all as “Vehicles” instead of separately), data from multiple sources can be integrated more seamlessly, disambiguated, and deduplicated for clearer understanding. 

Finally, ontologies provide the true foundation for linking related datasets to one another and allow for the definition of custom relationships between knowledge assets. When combining ontology with AI, organizations can perform inferences as a way to capture explicit data about what’s only implied by individual datasets. This shows the power of semantics at work, and demonstrates that good, AI-ready data enriched with metadata can provide insights at the same level and accuracy as a human. 

Organizations who have not pursued developing semantics for knowledge assets before can leverage traditional semantic capture methods, such as business glossaries. As organizations mature in their curation of knowledge assets, they are able to leverage the definitions developed as part of these glossaries and dictionaries, and begin to structure that information using more advanced modeling techniques, like taxonomy and ontology development. When applied to data, these semantic models make data more understandable, both to end users and AI systems. 

5) Semantic Model Application (Labeling and Tagging) 

The data management community has more recently been focused on the value of metadata and metadata-first architecture, and is scrambling to catch up to the maturity displayed in the fields of content and knowledge management. Through replicating methods found in content management systems and knowledge management platforms, data management professionals are duplicating past efforts. Currently, the data catalog is the primary platform where metadata is being applied and stored for data assets. 

To aggregate metadata for your organization’s AI readiness efforts, it’s crucial to look to data stewards as the owners of, and primary contributors to, this effort. Through the process of labeling data by populating fields such as asset descriptions, owner, assumptions made upon collection, and purposes, data stewards help to drive their data towards AI readiness while making tacit knowledge explicit and available to all. Additionally, metadata application against a semantic model (especially taxonomies and ontologies) contextualizes assets in business context and connects related assets to one another, further enriching AI-generated responses to user prompts. While there are methods to apply metadata to assets without the need for as much manual effort (such as auto-classification, which excels for content-based knowledge assets), structured data usually dictates the need for human subject matter experts to ensure accurate classification. 

With data catalogs and recent investments in metadata repositories, however, we’ve noticed a trend that we expect will continue to grow and spread across organizations in the near future. Data system owners are more and more keen to manage metadata and catalog their assets within the same systems that data is stored/used, adopting features that were previously exclusive to a data catalog. Major software providers are strategically acquiring or building semantic capabilities for this purpose. This has been underscored by the recent acquisition of multiple data management platforms by the creators of larger, flagship software products. With the features of the data catalog being adapted from a full, standalone application that stores and presents metadata to a component of a larger application that focuses as a metadata store, the metadata repository is beginning to take hold as the predominant metadata management platform.

6) Address Access and Security (Unified Entitlements)

Applying semantic metadata as described above helps to make data findable across an organization and contextualized with relevant datasets—but this needs to be balanced alongside security and entitlements considerations. Without regard to data security and privacy, AI systems risk bringing in data they shouldn’t have access to because access entitlements are mislabeled or missing, leading to leaks in sensitive information.

A common example of when this can occur is with user re-identification. Data points that independently seem innocuous, when combined by an AI system, can leak information about customers or users of an organization. With as few as just 15 data points, information that was originally collected anonymously can be combined to identify an individual. Data elements like ZIP code or date of birth would not be damaging on their own, but when combined, can expose information about a user that should have been kept private. These concerns become especially critical in industries with small population sizes for their datasets, such as rare disease treatment in the healthcare industry.

EK’s unified entitlements work is focused on ensuring the right people and systems view the correct knowledge assets at the right time. This is accomplished through a holistic architectural approach with six key components. Components like a policy engine capture can enforce whether access to data should be given, while components like a query federation layer ensure that only data that is allowed to be retrieved is brought back from the appropriate sources. 

The components of unified entitlements can be combined with other technologies like dark data detection, where a program scrapes an organization’s data landscape for any unlabeled information that is potentially sensitive, so that both human users and AI solutions cannot access data that could result in compliance violations or reputational damage. 

As a whole, data that exposes sensitive information to the wrong set of eyes is not AI-ready. Unified entitlements can form the layer of protection that ensures data AI readiness across the organization.

7) Maintain Quality While Iteratively Improving (Governance)

Governance serves a vital purpose in ensuring data assets become, and remain, AI-ready. With the introduction of AI to the enterprise, we are now seeing governance manifest itself beyond the data landscape alone. As AI governance begins to mature as a field of its own, it is taking on its own set of key roles and competencies and separating itself from data governance. 

While AI governance is meant to guide innovation and future iterations while ensuring compliance with both internal and external standards, data governance personnel are taking on the new responsibility of ensuring data is AI-ready based on requirements set by AI governance teams. Barring the existence of AI governance personnel, data governance teams are meant to serve as a bridge in the interim. As such, your data governance staff should define a common model of AI-ready data assets and related standards (such as structure, recency, reliability, and context) for future reference. 

Both data and AI governance personnel hold the responsibility of future-proofing enterprise AI solutions, in order to ensure they continue to align to the above steps and meet requirements. Specific to data governance, organizations should ask themselves, “How do you update your data governance plan to ensure all the steps are applicable in perpetuity?” In parallel, AI governance should revolve around filling gaps in their solution’s capabilities. Once the AI solutions launch to a production environment and user base, more gaps in the solution’s realm of expertise and capabilities will become apparent. As such, AI governance professionals need to stand up processes to use these gaps to continue identifying new needs for knowledge assets, data or otherwise, in perpetuity.

Conclusion

As we have explored throughout this blog, data is an extremely varied and unique form of knowledge asset with a new and disparate set of considerations to take into account when standing up an AI solution. However, following the steps listed above as part of an iterative process for implementation of data assets within said solution will ensure data is AI-ready and an invaluable part of an AI-powered organization.

If you’re seeking help to ensure your data is AI-ready, contact us at info@enterprise-knowledge.com.

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The potential of AI agents for productivity gains within organizations is undeniable—and it’s no longer a distant future. However, the key question today is: when is the right time to build and deploy an AI agent, and when is simpler automation the more effective choice?

While the idea of a fully autonomous assistant handling routine tasks is appealing, AI agents require a complex framework to succeed. This includes breaking down silos, ensuring knowledge assets are AI-ready, and implementing guardrails to meet enterprise standards for accuracy, trust, performance, ethics, and security.

Over the past couple of years, we’ve worked closely with executives who are navigating what it truly means for their organizations to be “AI-ready” or “AI-powered”, and as AI technologies evolve, this challenge has only become more complex and urgent for all of us.

To move forward effectively, it’s crucial to understand the role of AI agents compared to traditional or narrow AI, automation, or augmentation solutions. Specifically, it is important to recognize the unique advantages of agent-based AI solutions, identify the right use cases, and ensure organizations have the best foundation to scale effectively.

In the first part of this two-part series, I’ll outline the core building blocks for organizations looking to integrate AI agents. The goal of this series is to provide insights that help set realistic expectations and contribute to informed decisions around AI agent integration—moving beyond technical experiments—to deliver meaningful outcomes and value to the organization.

Understanding AI Agents

Components of Agentic AI Framework

1. Relevant Language and AI Models

Language models are the agent’s cognitive core, essentially its “brain”, responsible for reasoning, planning, and decision-making. While not every AI agent requires a Large Language Model (LLM), most modern and effective agents rely on LLMs and reinforcement learning to evaluate strategies and select the best course of action. LLM-powered agents are especially adept at handling complex, dynamic, and ambiguous tasks that demand interpretation and autonomous decision-making.

Choosing the right language model also depends on the use case, task complexity, desired level of autonomy, and the organization’s technical environment. Some tasks are better served to remain simple, with more deterministic workflows or specialized algorithms. For example, an expertise-focused agent (e.g., a financial fraud detection agent) is more effective when developed with purpose-built algorithms than with a general-purpose LLM because the subject area requires hyper-specific, non-generalizable knowledge. On the other hand, well-defined, repetitive tasks, such as data sorting, form validation, or compliance checks, can be handled by rule-based agents or classical machine learning models, which are cheaper, faster, and more predictable. LLMs, meanwhile, add the most value in tasks that require flexible reasoning and adaptation, such as orchestrating integration with multiple tools, APIs, and databases to perform real-world actions like dynamic customer service process, placing trades or interpreting incomplete and ambiguous information. In practice, we are finding that a hybrid approach works best.

2. Semantic Layer and Unified Business Logic

AI agents need access to a shared, consistent view of enterprise data to avoid conflicting actions, poor decision-making, or the reinforcement of data silos. Increasingly, agents will also need to interact with external data and coordinate with other agents, which compounds the risk of misalignment, duplication, or even contradictory outcomes. This is where a semantic layer becomes critical. By standardizing definitions, relationships, and business context across knowledge and data sources, the semantic layer provides agents with a common language for interpreting and acting on information, connecting agents to a unified business logic. Across several recent projects, implementing a semantic layer has improved the accuracy and precision of initial AI results from around 50% to between 80% and 95%, depending on the use case.

The semantic layer includes metadata management, business glossaries, and taxonomy/ontology/graph data schemas that work together to provide a unified and contextualized view of data across typically siloed systems and business units, enabling agents to understand and reason about information within the enterprise context. These semantic models define the relationships between data entities and concepts, creating a structured representation of the business domain the agent is operating in. Semantic models form the foundation for understanding data and how it relates to the business. By incorporating two or more of these semantic model components, the semantic layer provides the foundation for building robust and effective agentic perception, cognition, action, and learning that can understand, reason, and act on org-specific business data. For any AI, but specifically for AI agents, a semantic layer is critical in providing access to:

• Organizational context and meaning to raw data to serve as a grounding ‘map’ for accurate interpretation and agent action;
• Standardized business terms that establish a consistent vocabulary for business metrics (e.g., defining “revenue” or “store performance” ), preventing confusion and ensuring the AI uses the same definitions as the business; and
• Explainability and trust through metadata and lineage to validate and track why agent recommendations are compliant and safe to adopt.

Overall, the semantic layer ensures that all agents are working from the same trusted source of truth, and enables them to exchange information coherently, align with organizational policies, and deliver reliable, explainable results at scale. Specifically, in a multi-agent system with multiple domain-specific agents, all agents may not work off the same semantic layer, but each will have the organizational business context to interpret messages from each other as courtesy of the domain-specific semantic layers.

The bottom line is that, without this reasoning layer, the “black box” nature of agents’ decision-making processes erodes trust, making it difficult for organizations to adopt and rely on these source systems.

3. Access to AI-Ready Knowledge Assets and Sources

Agents require accurate, comprehensive, and context-rich organizational knowledge assets to make sound decisions. Without access to high-quality, well-structured data, agents, especially those powered by LLMs, struggle to understand complex tasks or reason effectively, often leading to unreliable or “hallucinated” outputs. In practice, this means organizations making strides with effective AI agents need to:

• Capture and codify expert knowledge in a machine-readable form that is readily interpretable by AI models so that tacit know-how, policies, and best practices are accessible to agents, not just locked in human workflows or static documents;
• Connect structured and unstructured data sources, from databases and transactional systems to documents, emails, and wikis, into a connected, searchable layer that agents can query and act upon; 
• Provide semantically enriched assets with well-managed metadata, consistent labels, and standardized formats to make them interoperable with common AI platforms; 
• Align and organize internal and external data so agents can seamlessly draw on employee-facing knowledge (policies, procedures, internal systems) as well as customer-facing assets (product documentation, FAQs, regulatory updates) while maintaining consistency, compliance, and brand integrity; and
• Enable access to AI assets and systems while maintaining strict controls over who can use it, how it is used, and where it flows.

This also means, beyond static access to knowledge, agents must also query and interact dynamically with various sources of data and content. Doing this includes connecting to applications, websites, content repositories, and data management systems, and taking direct actions, such as reading/writing into enterprise applications, updating records, or initiating workflows.

Enabling this capability requires a strong design and engineering foundation, allowing agents to integrate with external systems and services through standard APIs, operate within existing security protocols, and respect enterprise governance and record compliance requirements. A unified approach, bringing together disparate data sources into a connected layer (see semantic layer component above), helps break down silos and ensures agents can operate with a holistic, enterprise-wide view of knowledge.

4. Instructions, Guardrails, and Observability

Organizations are largely unprepared for agentic AI due to several factors: the steep leap from traditional, predictable AI to complex multi-agent orchestration, persistent governance gaps, a shortage of specialized expertise, integration challenges, and inconsistent data quality, to name a few. Most critically, the ability to effectively control and monitor agent autonomy remains a fundamental barrier—posing significant security, compliance, and privacy risks. Recent real-world cases highlight how quickly things can go wrong, including tales of agents deleting valuable data, offering illegal or unethical advice, and amplifying bias in hiring decisions or in public-sector deployments. These failures underscore the risks of granting autonomous AI agents high-level permissions over live production systems without robust oversight, guardrails, and fail-safes. Until these gaps are addressed, autonomy without accountability will remain one of the greatest barriers to enterprise readiness in the agentic AI era.

As such, for AI agents to operate effectively within the enterprise, they must be guided by clear instructions, protected by guardrails, and monitored through dedicated evaluation and observability frameworks.

• Instructions: Instructions define an AI agent’s purpose, goals, and persona. Agents don’t inherently understand how a specific business or organization operates. Instead, that knowledge comes from existing enterprise standards, such as process documentation, compliance policies, and operating models, which provide the foundational inputs for guiding agent behavior. LLMs can interpret these high-level standards and convert them into clear, step-by-step instructions, ensuring agents act in ways that align with organizational expectations. For example, in a marketing context, an LLM can take a general directive like, “All published content must reflect the brand voice and comply with regulatory guidelines”, and turn it into actionable instructions for a marketing agent. The agent can then assist the marketing team by reviewing a draft email campaign, identifying tone or compliance issues, and suggesting revisions to ensure the content meets both brand and regulatory standards.
• Guardrails: Guardrails are safety measures that act as the protective boundaries within which agents operate. Agents need guardrails across different functions to prevent them from producing harmful, biased, or inappropriate content and to enforce security and ethical standards. These include relevance and output validation guardrails, personally identifiable information (PII) filters that detect unsafe inputs or prevent leakage of PII, reputation and brand alignment checks, privacy and security guardrails that enforce authentication, authorization, and access controls to prevent unauthorized data exposure, and guardrails against prompt attacks and content filters for harmful topics. 
• Observability: Even with strong instructions and guardrails, agents must be monitored in real time to ensure they behave as expected. Observability includes logging actions, tracking decision paths, monitoring model outputs, cost monitoring and performance optimization, and surfacing anomalies for human review. A good starting point for managing agent access is mapping operational and security risks for specific use cases and leveraging unified entitlements (identity and access control across systems) to apply strict role-based permissions and extend existing data security measures to cover agent workflows.

Together, instructions, guardrails, and observability form a governance layer that ensures agents operate not only autonomously, but also responsibly and in alignment with organizational goals. To achieve this, it is critical to plan for and invest in AI management platforms and services that define agent workflows, orchestrate these interactions, and supervise AI agents. Key capabilities to look for in an AI management platform include: 

• Prompt chaining where the output of one LLM call feeds the next, enabling multi-step reasoning; 
• Instruction pipelines to standardize and manage how agents are guided;
• Agent orchestration frameworks for coordinating multiple agents across complex tasks; and 
• Evaluation and observability (E&O) monitoring solutions that offer features like content and topic moderation, PII detection and redaction, and protection against prompt injection or “jailbreaking” attacks. Furthermore, because training models involve iterative experimentation, tuning, and distributed computation, it is paramount to have benchmarks and business objectives defined from the onset in order to optimize model performance through evaluation and validation.

In contrast to the predictable expenses of standard software, AI project costs are highly dynamic and often underestimated during initial planning. Many organizations are grappling with unexpected AI cost overruns due to hidden expenses in data management, infrastructure, and maintenance for AI. This can severely impact budgets, especially for agentic environments. Tracking system utilization, scaling resources dynamically, and implementing automated provisioning allows organizations to maintain consistent performance and optimization for agent workloads, even under variable demand, while managing cost spikes and avoiding any surprises.

Many traditional enterprise observability tools are now extending their capabilities to support AI-specific monitoring. Lifecycle management tools such as MLflow, Azure ML, Vertex AI, or Databricks help with the management of this process at enterprise scale by tracking model versions, automating retraining schedules, and managing deployments across environments. As with any new technology, the effective practice is to start with these existing solutions where possible, then close the gaps with agent-specific, fit-for-purpose tools to build a comprehensive oversight and governance framework.

5. Humans and Organizational Operating Models

There is no denying it—the integration of AI agents will transform ways of working worldwide. However, a significant gap still exists between the rapid adoption plans for AI agents and the reality on the ground. Why? Because too often, AI implementations are treated as technological experiments, with a focus on performance metrics or captivating demos. This approach frequently overlooks the critical human element needed for AI’s long-term success. Without a human-centered operating model, AI deployments continue to run the risk of being technologically impressive but practically unfit for organizational use.

Human Intervention and Human-In-the-Loop Validation: One of the most pressing considerations in integrating AI into business operations is the role of humans in overseeing, validating, and intervening in AI decisions. Agentic AI has the power to automate many tasks, but it still requires human oversight, particularly in high-risk or high-impact decisions. A transparent framework for when and how humans intervene is essential for mitigating these risks and ensuring AI complies with regulatory and organizational standards. Emerging practices we are seeing are showing early success when combining agent autonomy with human checkpoints, wherein subject matter experts (SMEs) are identified and designated as part of the “AI product team” from the onset to define the requirements for and ensure that AI agents consistently focus on and meet the right organizational use cases throughout development. 

Shift in Roles and Reskilling: For AI to truly integrate into an organization’s workflow, a fundamental shift in the fabric of an organization’s roles and operating model is becoming necessary. Many roles as we know them today are shifting—even for the most seasoned software and ML engineers. Organizations are starting to rethink their structure to blend human expertise with agentic autonomy. This involves redesigning workflows to allow AI agents to automate routine tasks while humans focus on strategic, creative, and problem-solving roles. 

Implementing and managing agentic AI requires specialized knowledge in areas such as AI model orchestration, agent–human interaction design, and AI operations. These skill sets are often underdeveloped in many organizations and, as a result, AI projects are failing to scale effectively. The gap isn’t just technical; it also includes a cultural shift toward understanding how AI agents generate results and the responsibility associated with their outputs. To bridge this gap, we are seeing organizations start to invest in restructuring data, AI, content, and knowledge operations/teams and reskilling their workforce in roles like AI product management, knowledge and semantic modeling, and AI policy and governance.

Ways of Working: To support agentic AI delivery at scale, it is becoming evident that agile methodologies must also evolve beyond their traditional scope of software engineering and adapt to the unique challenges posed by AI development lifecycles. Agentic AI, requires an agile framework that is flexible, experimental, and capable of iterative improvements. This further requires deep interdisciplinary collaboration across data scientists, AI engineers, software engineers, domain experts, and business stakeholders to navigate complex business and data environments.

Furthermore, traditional CI/CD pipelines, which focus on code deployment, need to be expanded to support continuous model training, testing, human intervention, and deployment. Integrating ML/AI Ops is critical for managing agent model drift and enabling autonomous updates. The successful development and large-scale adoption of agentic AI hinges on these evolving workflows that empower organizations to experiment, iterate, and adapt safely as both AI behaviors and business needs evolve.

Conclusion 

Agentic AI will not succeed through technology advancements alone. Given the inherent complexity and autonomy of AI agents, it is essential to evaluate organizational readiness and conduct a thorough cost-benefit analysis when determining whether an agentic capability is essential or merely a nice-to-have.

Success will ultimately depend on more than just cutting-edge models and algorithms. It also requires dismantling artificial, system-imposed silos between business and technical teams, while treating organizational knowledge and people as critical assets in AI design. Therefore, a thoughtful evolution of the organizational operating model and the seamless integration of AI into the business’s core is critical. This involves selecting the right project management and delivery frameworks, acquiring the most suitable solutions, implementing foundational knowledge and data management and governance practices, and reskilling, attracting, hiring, and retaining individuals with the necessary skill sets. These considerations make up the core building blocks for organizations to begin integrating AI agents.

The good news is that when built on the right foundations, AI solutions can be reused across multiple use cases, bridge diverse data sources, transcend organizational silos, and continue delivering value beyond the initial hype. 

Is your organization looking to evaluate AI readiness? How well does it measure up against these readiness factors? Explore our case studies and knowledge base on how other organizations are tackling this or get in touch to learn more about our approaches to content and data readiness for AI.

The post When Should You Use An AI Agent? Part One: Understanding the Components and Organizational Foundations for AI Readiness appeared first on Enterprise Knowledge.

Just as a physical mall provides a cohesive structure for shoppers to find stores, browse items, and make purchases, a semantic layer creates a unified environment for business users. It allows them to easily discover datasets from diverse sources, review connected information, and gain actionable insights. It brings together a variety of data providers (our “stores”), and their data (their “goods”) into a single, intuitive location, enabling end-users, including people, analytics tools, and agentic solutions (our “shoppers”) to find and intake precisely what they need to excel in their roles. 

This analogy of the Semantic Layer as an Information Shopping Mall has proven incredibly helpful for our teams and clients. In this blog post, we’ll use this familiar background to explore the foundational elements required to build your own Semantic Layer Shopping Mall and share key lessons learned along the way. 

1. Building the Mall: Creating the Structural Foundations

Before any stores can open their doors, a shopping mall needs fundamental structural elements: floors, walls, escalators, and walkways. Similarly, a semantic layer demands a well-designed technology architecture to support a seamless, connected data experience.

The core infrastructure of your semantic layer is formed by powerful tools such as Graph Databases, which connect complex relationships; Taxonomy Management Systems, for organizing data with consistent vocabularies; and Data Catalogs, which provide a directory of your data assets. Just like physical malls, no two semantic layers are identical. The unique goals and existing technological landscape of your organization will dictate the specific architecture required to build your bespoke information shopping mall. For example, an organization with a variety of data sensitivity levels and goals of creating agentic solutions may require an Identity and Access Management solution to ensure security across uses, or an organization that is keen on creating fraud detection solutions on top of a plethora of information may require a graph analytics tool. 

2. Creating the Directory: Developing Categorization Models

Your semantic layer needs precisely this type of robust core categorization model to direct your tools, systems, and people to the specific information they seek. This is achieved by establishing and consistently applying a common vocabulary across all of your systems. Within the semantic layer context, we leverage taxonomies (hierarchical lists of values) and ontologies (formal maps of concepts and their relationships) to provide this essential direction. Taxonomies may be used in cases where we are looking to categorize stores as alike–Payless, DSW, and Foot Locker may be interchangeable as shoe stores–whereas ontologies, thanks to their multi-relational nature, can help tell us stores that make sense to visit for a certain occasion–Staples for school supplies followed by Gap for back-to-school clothes.  

Developing an effective semantic layer directory demands two key considerations: 

• Achieving a Consensus on Terminology: Imagine a mall directory where “Footwear” and “Shoes” are used in different sections, or where “Electronics” and “Gadgets” demand their own spaces. This negates the purpose of categorization and causes confusion. A semantic layer requires careful negotiation with stakeholders to agree on common concepts. Investing the time to navigate organizational differences and build consensus on metadata and taxonomy terms before implementation significantly mitigates technical challenges down the line. 
• Designing an Extensible Model: For a semantic layer to thrive, its underlying data model must be capable of growing over time. As new data providers (“stores”) join your mall and new use cases emerge, the model must seamlessly integrate without ‘breaking’ previous work. Employing ontology design best practices and engaging with seasoned professionals ensures that your semantic layer is an accurate reflection of your organization’s reality and can evolve flexibly with both new information and demands. 

At Enterprise Knowledge, we advocate for initiating this phase with a small group of pilot use cases. These pilots typically focus on building out scoped taxonomies or ontologies tied to high-value, priority use cases and serve as a proving ground for onboarding initial data providers. Starting small allows for agile iteration, refinement, and stakeholder alignment before scaling. 

3. Store Tenant Recruitment: Driving Adoption & Buy-In

When deploying your semantic layer, robust stakeholder buy-in is key. Strategically position your semantic layer initiative as an effort to significantly enhance your knowledge-connectedness and enable decision-making across the organization. Summarizing this information in a cohesive Semantic Layer Strategy is key to quickly convincing providers and customers. 

An effective Semantic Layer Strategy should focus on: 

• Establishing a Clear Product Vision: To attract both data providers and consumers, the strategy must have a well-defined product vision. This involves articulating what the semantic layer will become, who it will serve, and what core problems it will solve. This strategic clarity ensures that all stakeholders understand the overarching purpose and direction, fostering alignment and shared purpose.
• Defining Measurable Outcomes: To truly gain adoption, your strategy should demonstrably link to tangible business outcomes. It is paramount to build compelling reasons for stakeholders to both contribute information and consume insights from the semantic layer. This involves identifying and communicating the specific, high-impact results (e.g., increased efficiency, reduced risk, enhanced insights) that the semantic layer will deliver.

4. Grand Opening: Populating Data & Unveiling Use Cases

With the foundation built, the directory mapped, and the tenants recruited, it’s finally time for the grand unveiling of your Information Shopping Mall. This phase involves connecting applications to your semantic layer and populating it with data.

A successful grand opening requires:

• Robust Data Pipelines: Just like a mall needs efficient distributors to stock its stores, your semantic layer needs APIs and data transformation pipelines. These are critical conduits that connect various source applications (like CRMs, Content Management Systems, and traditional databases) to your semantic layer, ensuring a continuous flow of high-quality data.
• Secure Entitlement Structures: Paramount to any successful mall is ensuring security of its goods. For your semantic layer, this translates to establishing secure entitlement structures. This involves defining who has access to what information and ensuring sensitive information remains protected while still enabling necessary access for relevant business users.
• Coordinated Capability Development: A seamless launch is the result of close coordination between technology teams, product owners, and stakeholders. This collaboration is vital for building the necessary technical capabilities, shaping an intuitive user experience, and managing expectations across the organization as new semantic-power use cases arise. 

Conclusion 

Building an Information Shopping Mall – your Semantic Layer – transforms disjointed data into an invaluable, accessible asset. This empowers your business with clarity, efficiency, and insight.

At Enterprise Knowledge, we specialize in guiding organizations through every phase of this complex journey, turning the vision of truly connected knowledge into a tangible reality. For more information, reach out to us at info@enterprise-knowledge.com.

The post Building Your Information Shopping Mall – A Semantic Layer Guide appeared first on Enterprise Knowledge.

Drawing from multiple client engagements across sectors, Tesfaye and DeMay shared patterns and lessons learned from initiatives where KM, Data Management, and AI teams are working together to create a more connected and intelligent enterprise. They highlighted the growing need for integrated strategies that bring together semantic modeling, content management, and metadata governance to enable intelligent automation and more effective knowledge discovery.

The presentation emphasized how KM professionals can lead the way in designing sustainable semantic architectures, building cross-functional partnerships, and aligning programs with organizational priorities and AI investments. Presenters also explored how roles are shifting from traditional content stewards to strategic enablers of enterprise intelligence.

Session attendees walked away with:

• Insight into how KM roles are expanding to meet enterprise-wide data and AI needs;
• Examples of how semantic layers can enhance findability, improve reuse, and enable automation;
• Lessons from organizations integrating KM, Data Governance, and AI programs; and
• Practical approaches to designing cross-functional operating models and governance structures that scale.

The post The Evolution of Knowledge Management & Organizational Roles: Integrating KM, Data Management, and Enterprise AI through a Semantic Layer appeared first on Enterprise Knowledge.

The Solution

EK partnered with the professional association to establish the foundational content strategy, semantic models, and solution architecture to enable their goals for content discovery and analytics. First, EK conducted a current state analysis and target state definition, as well as a semantic maturity assessment. This helped EK understand the factors that could be leveraged to help the association realize its goals. EK subsequently completed three parallel workstreams:

1. Content Assessment: EK audited a sample of assets on priority web properties to understand the condition of the association’s content and semantic practices. EK identified recommendations for how to enhance the performance, governance, and discoverability of content. Based on these recommendations, EK provided step-by-step procedures to support the association in completing a comprehensive audit to enhance their content quality and aid in future findability enhancement and content personalization efforts.
2. Taxonomy and Ontology Development: EK developed an enterprise taxonomy and ontology framework for the association—to provide a standardized vocabulary for use across the association’s systems, and increase the maturity of the association’s semantic models. The enterprise taxonomy included 12 facets to support 12 metadata fields, with a cumulative total of over 900 concepts. An ontology identified key relationships between the different taxonomy facets, establishing a foundation for identifying related content and supporting auto-tagging.
3. Semantic Layer Architecture: EK provided recommendations for maturing the association’s tooling and integrations in support of their goals. Specifically, EK developed a solution architecture to integrate taxonomy, ontology, and auto-tagging across content, asset, and learning management systems, in order to inform a variety of content analytics, discovery, recommendation, and assembly applications. This architecture was designed to form the basis of a semantic layer that the association could later use to connect and relate content enterprise-wide. The architecture included the addition of a taxonomy and ontology management system (TOMS) to centralize semantic model management and to introduce auto-tagging capabilities. Alongside years of experience in tool evaluation, EK leveraged their proprietary TOMS evaluation matrix to score candidate vendors and TOMS solutions, supporting the association in selecting a tool that was the best fit for their needs.
4. Auto-Tagging Proof of Concept: Building on these efforts, EK conducted an auto-tagging proof of concept (PoC), to support the association in applying the taxonomy to their content. The PoC automatically tagged all content assets in 2 priority CMSes with concepts from 2 prioritized topic taxonomy facets. The EK team prepared the processing pipeline for the auto-tagging effort, including pre-processing the content and conducting analysis of the tags to gauge quality and improvement over time.

To determine the exact level of improvement, EK worked with subject matter experts to establish a gold standard set of expected tags for a sample of content assets. The tags produced by the auto-tagger were compared to the expected tag set, to generate measures of recall, precision, and accuracy. EK used the analytics to inform adjustments to the taxonomy facets and to fine-tune and improve the auto-tagger’s performance over successive rounds.

To support the association in continuing to grow and leverage their semantic maturity, EK provided a detailed semantic maturity implementation roadmap. The roadmap identified five target outcomes for semantic enrichment, including: enhancing analytics to provide insights into content use and content gaps; and recommending content by using content tags to suggest related resources. For each outcome, EK detailed the requisite goals, business value, tasks, and dependencies—providing the association with the guidance they needed to realize each outcome and further advance their semantic maturity.

The EK Difference

EK was uniquely positioned to help the association improve their semantic maturity. As thought leaders in the semantic space, EK had the expertise and experience to assess the association’s semantic maturity, identify opportunities for growth, and define a vision and roadmap to help the association realize its business priorities. Further, EK has a deep understanding of the semantic technology landscape. This positioned EK to deliver tailored solutions that reflect the specific needs of the association, ensuring the solutions contribute to the association’s long-term technology roadmap.

EK leveraged a holistic approach to assessing and advancing the association’s semantic maturity. EK’s proprietary semantic maturity assessment accounts for the varied factors that influence an organization’s semantic maturity, including considerations for people, process, content, models, and technology. This positions the association to develop the capabilities required for semantic maturity across all contributing factors. Building off of the semantic maturity assessment, EK delivered end-to-end services that supported the entire semantic lifecycle, from strategy through design, implementation, and governance. This provided the association with the semantic infrastructure to realize near-term value; for instance, developing an enterprise taxonomy and applying it to their content assets using auto-tagging. By using proprietary, industry-leading approaches, EK was able to deliver these end-to-end services with tangible results within 4 months.

The Results

EK delivered a semantic strategy and solution architecture, as well as a content clean-up strategy and initial taxonomy and ontology designs, that helped the professional association establish a foundation for realizing their goals. This effort culminated in the implementation of an auto-tagging PoC. The PoC included configuring the selected TOMS, establishing system integrations, and developing processing pipelines and quality evaluations. Ultimately, the PoC captured tags for over 23,000 content assets using more than 600 concepts from 2 priority taxonomy facets. This foundational work helped the professional association establish the initial components required for a semantic layer. A final roadmap and recommendations report provided detailed next steps, with specific tasks, dependencies, and pilots, to guide the professional association in leveraging and extending their foundational semantic layer. The first engagement was deemed a success by association leadership, and the roadmap was approved for phased implementation, which EK is now supporting. This continued partnership is enabling the association to begin realizing its goals of enhancing member engagement with content by improving content discovery and overall user experience.

Want to improve your organization’s content discovery capabilities? Interested in learning more about the semantic layer? Learn more from our experience or contact us today!

 

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The post Semantic Layer for Content Discovery, Personalization, and AI Readiness appeared first on Enterprise Knowledge.

Enterprise Knowledge is continuing our new webinar series, The Semantic Exchange with the fourth session. This session is designed for a variety of audiences, ranging from those working in the semantic space as taxonomists or ontologists, to folks who are just starting to learn about structured data and content, and how they may fit into broader initiatives around artificial intelligence or knowledge graphs.

This 30-minute session invites you to engage with Yumiko Saito’s case study, A Semantic Layer to Enable Risk Management at a Multinational Bank. Come ready to hear and ask about:

• The challenges financial firms encounter with risk management;
• The semantic solutions employed to mitigate these challenges; and
• The value created by employing semantic layer solutions.

This webinar will take place on Thursday July 17th, from 1:00 – 1:30PM EDT. Can’t make it? The session will also be recorded and published to registered attendees. View the recording here!

The post The Semantic Exchange: A Semantic Layer to Enable Risk Management at a Multinational Bank appeared first on Enterprise Knowledge.

Enterprise Knowledge is pleased to introduce a new webinar series, The Semantic Exchange. This session is the third of a five part series where we invite fellow practitioners to tune in and hear more about work we’ve published from the authors themselves. In these moderated sessions, we invite you to ask the authors questions in a short, accessible format. Think of the series as a chance for a little semantic snack!

This session is designed for a variety of audiences, ranging from those working in the semantic space as taxonomists or ontologists, to folks who are just starting to learn about structured data and content, and how they may fit into broader initiatives around artificial intelligence or knowledge graphs.

This 30-minute session invites you to engage with Kathleen Gollner’s blog, Metadata Within the Semantic Layer. Come ready to hear and ask about:

• Why metadata is foundational for a semantic layer;
• How to optimize metadata for use across knowledge assets, systems, and use cases; and
• How metadata can be leveraged in AI solutions.

This webinar will take place on Wednesday July 9th, from 1:00 – 1:30PM EDT. Can’t make it? The session will also be recorded and published to registered attendees. View the recording here!

The post The Semantic Exchange: Metadata Within the Semantic Layer 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.

As enterprises accelerate AI adoption, the semantic layer has become essential for unifying siloed data and delivering actionable, contextualized insights. Graph analytics plays a pivotal role within this architecture, serving as the analytical engine that reveals patterns and relationships often missed by traditional data analysis approaches. By integrating metadata graphs, knowledge graphs, and analytics graphs, organizations can bridge disparate data sources and empower AI-driven decision-making. With recent technological advances in graph-based technologies, including knowledge graphs, property graphs, Graph Neural Networks (GNNs), and Large Language Models (LLMs), the semantic layer is evolving into a core enabler of intelligent, explainable, and business-ready insights

The Semantic Layer: Foundation for Connected Intelligence

A semantic layer acts as an enterprise-wide framework that standardizes data meaning across both structured and unstructured sources. Unlike traditional data fabrics, it integrates content, media, data, metadata, and domain knowledge through three main interconnected components:

1. Metadata Graphs capture the data about data. They track business, technical, and operational metadata – from data lineage and ownership to security classifications – and interconnect these descriptors across the organization. In practice, a metadata graph serves as a unified catalog or map of data assets, making it ideal for governance, compliance, and discovery use cases. For example, a bank might use a metadata graph to trace how customer data flows through dozens of systems, ensuring regulatory requirements are met and identifying duplicate or stale data assets.

2. Knowledge Graphs encode the business meaning and context of information. They integrate heterogeneous data (structured and unstructured) into an ontology-backed model of real-world entities (customers, accounts, products, and transactions) and the relationships between them. A knowledge graph serves as a semantic abstraction layer over enterprise data, where relationships are explicitly defined using standards like RDF/OWL for machine understanding. For example, a retailer might utilize a knowledge graph to map the relationships between sources of customer data to help define a “high-risk customer”. This model is essential for creating a common understanding of business concepts and for powering context-aware applications such as semantic search and question answering.

3. Analytics Graphs focus on connected data analysis. They are often implemented as property graphs (LPGs) and used to model relationships among data points to uncover patterns, trends, and anomalies. Analytics graphs enable data scientists to run sophisticated graph algorithms – from community detection and centrality to pathfinding and similarity – on complex networks of data that would be difficult to analyze in tables. Common use cases include fraud detection/prevention, customer influence networks, recommendation engines, and other link analysis scenarios. For instance, fraud analytics teams in financial institutions have found success using analytics graphs to detect suspicious patterns that traditional SQL queries missed. Analysts frequently use tools like Kuzu and Neo4J, which have built-in graph data science modules, to store and query these graphs at scale. In contrast, graph visualization tools (Linkurious and Hume) help analysts explore the relationships intuitively.

Together, these layers transform raw data into knowledge intelligence; read more about these types of graphs here.

Driving Insights with Graph Analytics: From Knowledge Representation to Knowledge Intelligence with the Semantic Layer

• Relationship Discovery
  Graph analytics reveals hidden, non-obvious connections that traditional relational analysis often misses. It leverages network topology, how entities relate across multiple hops, to uncover complex patterns. Graph algorithms like pathfinding, community detection, and centrality analysis can identify fraud rings, suspicious transaction loops, and intricate ownership chains through systematic relationship analysis. These patterns are often invisible when data is viewed in tables or queried without regard for structure. With a semantic layer, this discovery is not just technical, it enables the business to ask new types of questions and uncover previously inaccessible insights.
  
• Context-Aware Enrichment
  While raw data can be linked, it only becomes usable when placed in context. Graph analytics, when layered over a semantic foundation of ontologies and taxonomies, enables the enrichment of data assets with richer and more precise information. For example, multiple risk reports or policies can be semantically clustered and connected to related controls, stakeholders, and incidents. This process transforms disconnected documents and records into a cohesive knowledge base. With a semantic layer as its backbone, graph enrichment supports advanced capabilities such as faceted search, recommendation systems, and intelligent navigation.
  
• Dynamic Knowledge Integration
  Enterprise data landscapes evolve rapidly with new data sources, regulatory updates, and changing relationships that must be accounted for in real-time. Graph analytics supports this by enabling incremental and dynamic integration. Standards-based knowledge graphs (e.g., RDF/SPARQL) ensure portability and interoperability, while graph platforms support real-time updates and streaming analytics. This flexibility makes the semantic layer resilient, future-proof, and always current. These traits are crucial in high-stakes environments like financial services, where outdated insights can lead to risk exposure or compliance failure.
  

These mechanisms, when combined, elevate the semantic layer from knowledge representation to a knowledge intelligence engine for insight generation. Graph analytics not only helps interpret the structure of knowledge but also allows AI models and human users alike to reason across it.

Business Impact and Case Studies

Enterprise Knowledge’s implementations demonstrate how organizations leverage graph analytics within semantic layers to solve complex business challenges. Below are three real-world examples from their case studies:
1. Global Investment Firm: Unified Knowledge Portal

A global investment firm managing over $250 billion in assets faced siloed information across 12+ systems, including CRM platforms, research repositories, and external data sources. Analysts wasted hours manually piecing together insights for mergers and acquisitions (M&A) due diligence.

Enterprise Knowledge designed and deployed a semantic layer-powered knowledge portal featuring:

• A knowledge graph integrating structured and unstructured data (research reports, market data, expert insights)
• Taxonomy-driven semantic search with auto-tagging of key entities (companies, industries, geographies)
• Graph analytics to map relationships between investment targets, stakeholders, and market trends

Results

• Single source of truth for 50,000+ employees, reducing redundant data entry
• Accelerated M&A analysis through graph visualization of ownership structures and competitor linkages
• AI-ready foundation for advanced use cases like predictive market trend modeling

2. Insurance Fraud Detection: Graph Link Analysis

A national insurance regulator struggled to detect synthetic identity fraud, where bad actors slightly alter personal details (e.g., “John Doe” vs “Jon Doh”) across multiple claims. Traditional relational databases failed to surface these subtle connections.

Enterprise Knowledge designed a graph-powered semantic layer with the following features:

• Property graph database modeling claimants, policies, and claim details as interconnected nodes/edges
• Link analysis algorithms (Jaccard similarity, community detection) to identify fraud rings
• Centrality metrics highlighting high-risk networks based on claim frequency and payout patterns

Results

• Improved detection of complex fraud schemes through relationship pattern analysis
• Dynamic risk scoring of claims based on graph-derived connection strength
• Explainable AI outputs via graph visualizations for investigator collaboration

3. Government Linked Data Investigations: Semantic Layer Strategy

A government agency investigating cross-border crimes needed to connect fragmented data from inspection reports, vehicle registrations, and suspect databases. Analysts manually tracked connections using spreadsheets, leading to missed patterns and delayed cases.

Enterprise Knowledge delivered a semantic layer solution featuring:

• Entity resolution to reconcile inconsistent naming conventions across systems
• Investigative knowledge graph linking people, vehicles, locations, and events
• Graph analytics dashboard with pathfinding algorithms to surface hidden relationships

Results

• 30% faster case resolution through automated relationship mapping
• Reduced cognitive load with graph visualizations replacing manual correlation
• Scalable framework for integrating new data sources without schema changes
  

Implementation Best Practices

Enterprise Knowledge’s methodology emphasizes several critical success factors :

1. Standardize with Semantics
Establishing a shared semantic foundation through reusable ontologies, taxonomies, and controlled vocabularies ensures consistency and scalability across domains, departments, and systems. Standardized semantic models enhance data alignment, minimize ambiguity, and facilitate long-term knowledge integration. This practice is critical when linking diverse data sources or enabling federated analysis across heterogeneous environments.

2. Ground Analytics in Knowledge Graphs
Analytics graphs risk misinterpretation when created without proper ontological context. Enterprise Knowledge’s approach involves collaboration with intelligence subject matter experts to develop and implement ontology and taxonomy designs that map to Common Core Ontologies for a standard, interoperable foundation.

3. Adopt Phased Implementation
Enterprise Knowledge develops iterative implementation plans to scale foundational data models and architecture components, unlocking incremental technical capabilities. EK’s methodology includes identifying starter pilot activities, defining success criteria, and outlining necessary roles and skill sets.

4. Optimize for Hybrid Workloads
Recent research on Semantic Property Graph (SPG) architectures demonstrates how to combine RDF reasoning with the performance of property graphs, enabling efficient hybrid workloads. Enterprise Knowledge advises on bridging RDF and LPG formats to enable seamless data integration and interoperability while maintaining semantic standards.

Conclusion

The semantic layer achieves transformative impact when metadata graphs, knowledge graphs, and analytics graphs operate as interconnected layers within a unified architecture. Enterprise Knowledge’s implementations demonstrate that organizations adopting this triad architecture achieve accelerated decision-making in complex scenarios. By treating these components as interdependent rather than isolated tools, businesses transform static data into dynamic, context-rich intelligence.

Graph analytics is not a standalone tool but the analytical core of the semantic layer. Grounded in robust knowledge graphs and aligned with strategic goals, it unlocks hidden value in connected data. In essence, the semantic layer, when coupled with graph analytics, becomes the central knowledge intelligence engine of modern data-driven organizations.
If your organization is interested in developing a graph solution or implementing a semantic layer, contact us today!

The post Graph Analytics in the Semantic Layer: Architectural Framework for Knowledge Intelligence appeared first on Enterprise Knowledge.