Today, most enterprises are managing multiple content and data systems or repositories, often with overlapping capabilities such as content authoring, document management, or data management (typically averaging three or more). This leads to fragmentation and data silos, creating significant inefficiencies. Finding and preparing content and data for analysis takes weeks, or even months, resulting in high failure rates for knowledge management, data analytics, AI, and big data initiatives. Ultimately, negativity impacting decision-making capabilities and business agility.

To address these challenges, over the last few years, the semantic layer has emerged as a framework and solution to support a wide range of use cases, including content and data organization, integration, semantic search, knowledge discovery, data governance, and automation. By connecting disparate data sources, a semantic layer enables richer queries and supports programmatic knowledge extraction and modernization.

A semantic layer functions by utilizing metadata and taxonomies to create structure, business glossaries to align on the meaning of terms, ontologies to define relationships, and a knowledge graph to uncover hidden connections and patterns within content and data. This combination allows organizations to understand their information better and unlock greater value from their knowledge assets. Moreover, AI is tapping into this structured knowledge to generate contextual, relevant, and explainable answers.

So, what are the specific problems and use cases organizations are solving with a semantic layer? The case studies and use cases highlighted in this article are drawn from our own experience from recent projects and lessons learned, and demonstrate the value of a semantic layer not just as a technical foundation, but as a strategic asset, bridging human understanding with machine intelligence.

 

 

Semantic Layer Advancing Search and Knowledge Discovery: Getting Answers with Organizational Context

Over the past two decades, we have completed 50-70 semantic layer projects across a wide range of industries. In nearly every case, the core challenges revolve around age-old knowledge management and data quality issues—specifically, the findability and discoverability of organizational knowledge. In today’s fast-paced work environment, simply retrieving a list of documents as ‘information’ is no longer sufficient. Organizations require direct answers to discover new insights. Most importantly, organizations are looking to access data in the context of their specific business needs and processes. Traditional search methods continue to fall short in providing the depth and relevance required to make quick decisions. This is where a semantic layer comes into play. By organizing and connecting data with context, a semantic layer enables advanced search and knowledge discovery, allowing organizations to retrieve not just raw files or data, but answers that are rich in meaning, directly tied to objectives, and action-oriented. For example, supported by descriptive metadata and explicit relationships, semantic search, unlike keyword search, understands the meaning and context of our queries, leading to more accurate and relevant results by leveraging relationships between entities and concepts across content, rather than just matching keywords. This powers enterprise search solutions and question-answering systems that can understand and answer complex questions based on your organization’s knowledge. 

Case Study: For our clients in the pharmaceuticals and healthcare sectors, clinicians and researchers often face challenges locating the most relevant medical research, patient records, or treatment protocols due to the vast amount of unstructured data. A semantic layer facilitates knowledge discovery by connecting clinical data, trials, research articles, and treatment guidelines to enable context-aware search. By extracting and classifying entities like patient names, diagnoses, medications, and procedures from unstructured medical records, our clients are advancing scientific discovery and drug innovation. They are also improving patient care outcomes by applying the knowledge associated with these entities in clinical research. Furthermore, domain-specific ontologies organize unstructured content into a structured network, allowing AI solutions to better understand and infer knowledge from the data. This map-like representation helps systems navigate complex relationships and generate insights by clearly articulating how content and data are interconnected. As a result, rather than relying on traditional, time-consuming keyword-based searches that cannot distinguish between entities (e.g., “drugs manufactured by GSK” vs. “what drugs treat GSK”?), users can perform semantic queries that are more relevant and comprehend meaning (e.g., “What are the side effects of drug X?” or “Which pathways are affected by drug Y?”), by leveraging the relationships between entities to obtain precise and relevant answers more efficiently.

 

Semantic Layer as a Data Product: Unlocking Insights by Aligning & Connecting Knowledge Assets from Complex Legacy Systems

The reality is that most organizations face disconnected data spread across complex, legacy systems. Despite well-intended investments and efforts in enterprise knowledge and data management efforts, typical repositories often remain outdated, including legacy applications, email, shared network drives, folders, and information saved locally on desktops or laptops. Global investment banks, for instance, struggle with multiple outdated record management, risk, and compliance tracking systems, while healthcare organizations continue to contend with disparate electronic health record (EHR) systems and/or Electronic Medical Records (EMRs). These challenges hinder the ability to communicate and share data with newer, more advanced systems, are typically not designed to handle the growing demands of modern data, and result in businesses grappling with siloed information in legacy systems that make regulatory reporting onerous, manual, and time-consuming. The solution to these issues lies in treating the semantic layer as an abstracted data product itself whereby organizations employ semantic models to connect fragmented data from legacy systems, align shared terms across these systems, provide descriptive metadata and meaning, and connect data to empower users to query and access data with additional context, relevance, and speed. This approach not only streamlines decision-making but also modernizes data infrastructure without requiring a complete overhaul of existing systems.

Case Study: We are currently working with a global financial firm to transform their risk management program. The firm manages 21 bespoke legacy applications, each handling different aspects of their risk processes where compiling a comprehensive risk report typically took up to two months, and answering key questions like, “What are the related controls and policies relevant to a given risk in my business?” was a complex, time-consuming task to tackle. The firm engaged with us to augment their data transformation initiatives with a semantic layer and ecosystem. We began by piloting a conceptual graph model of their risk landscape, defining core risk taxonomies to connect disparate data across the ecosystem. We used ontologies to explicitly capture the relationships between risks, controls, issues, policies, and more. Additionally, we leveraged large language models (LLMs) to summarize and reconcile over 40,000 risks, which had previously been described by assessors using free text.

This initiative provided the firm with a simplified, intuitive view where users could quickly look up a risk and find relevant information in seconds via a graph front-end. Just 1.5 years later, the semantic layer is powering multiple key risk management tools, including a risk library with semantic search and knowledge panels, four recommendation engines, and a comprehensive risk dashboard featuring threshold and tolerance analysis. The early success of the project was due to a strategic approach: rather than attempting to integrate the semantic data model across their legacy applications, the firm treated it as a separate data product. This allowed risk assessors and various applications to use the semantic layer as modular “Lego bricks,” enabling flexibility and faster access to critical insights without disrupting existing systems.

 

Semantic Layer for Data Standards and Interoperability: Navigating the Dynamism of Data & Vendor Limitations 

Various data points suggest that, today, the average tenure of an S&P 500 technology company has dropped dramatically from 85 years to just 12-15 years. This rapid turnover reflects the challenges organizations face with the constant evolution of technology and vendor solutions. The ability to adapt to new tools and systems, while still maintaining operational continuity and reducing risk, is a growing concern for many organizations. One key solution to this challenge is using frameworks and standards that are created to ensure data interoperability, offering the flexibility to navigate data organization and abstracting data from system and vendor limitations. A proper semantic layer employs universally adopted semantic web (W3C) and data modeling standards to design, model, implement, and govern knowledge and data assets within organizations and across industries. 

Case Study: A few years ago, one of our clients faced a significant challenge when their graph database vendor was acquired by another company, leading to a sharp increase in both license and maintenance fees. To mitigate this, we were able to swiftly migrate all of their semantic data models from the old graph database to a new one within less than a week (the fastest migration we’ve ever experienced). This move saved the client approximately $2 million over three years. The success of the migration was made possible because their data models were built using semantic web standards (RDF-based), ensuring standards based data models and interoperability regardless of the underlying database or vendor. This case study highlights a fundamental shift in how organizations approach data management. 

 

Semantic Layer as the Framework for a Knowledge Portal 

The growing volume of data, the need for efficient knowledge sharing, and the drive to enhance employee productivity and engagement are fueling a renewed interest in knowledge portals. Organizations are increasingly seeking a centralized, easily accessible view of information as they adopt more data-driven, knowledge-centric approaches. A modern Knowledge Portal consolidates and presents diverse types of organizational content, ranging from unstructured documents and structured data to connections with people and enterprise resources, offering users a comprehensive “Enterprise 360” view of related knowledge assets to support their work effectively.

While knowledge portals fell out of favor in the 2010s due to issues like poor content quality, weak governance, and limited usability, today’s technological advancements are enabling their resurgence. Enhanced search capabilities, better content aggregation, intelligent categorization, and automated integrations are improving findability, discoverability, and user engagement. At its core, a Knowledge Portal comprises five key components that are now more feasible than ever: a Web UI, API layers, enterprise search engine, knowledge graph, and taxonomy/ontology management tools—half of which form part of the semantic layer.

Case Study: A global investment firm managing over $250 billion in assets partnered with us to break down silos and improve access to critical information across its 50,000-employee organization. Investment professionals were wasting time searching for fragmented, inconsistent knowledge stored across disparate systems, often duplicating efforts and missing key insights. We designed and implemented a Knowledge Portal integrating structured and unstructured content, AI-powered search, and a semantic layer to unify data from over 12 systems including their primary CRM (DealCloud), additional internal/external systems, while respecting complex access permissions and entitlements. A big part of the portal involved a semantic layer architecture which included the rollout of metadata and taxonomy design, ontology and graph modeling and storage, and an agile development process that ensured high user engagement and adoption. Today, the portal connects staff to both information and experts, enabling faster discovery, improved collaboration, and reduced redundancy. As a result, the firm saw measurable gains in their productivity, staff and client onboarding efficiency, and knowledge reuse. The company continues to expand the solution to advanced use cases such as semantic search applications and robust global use cases.

 

Semantic Layer for Analytics-Ready Data 

For many large-scale organizations, it takes weeks, sometimes months, for analytics teams to develop “insights” reports and dashboards that fulfill data-driven requests from executives or business stakeholders. Navigating complex systems and managing vast data volumes has become a point of friction between established software engineering teams managing legacy applications and emerging data science/engineering teams focused on unlocking analytics insights or data products. Such challenges persist as long as organizations work within complex infrastructures and proprietary platforms, where data is fragmented and locked in tables or applications with little to no business context. This makes it extremely difficult to extract useful insights, handle the dynamism of data, or manage the rising volumes of unstructured data, all while trying to ensure that data is consistent and trustworthy. 

Picture this scenario and use case from a recent engagement: a global retailer, with close to 40,000 store locations across the globe had recently migrated its data to a data lake in an attempt to centralize their data assets. Despite the investment, they still faced persistent challenges when new data requests came from their leadership, particularly around store performance metrics. Here’s a breakdown of the issues:

• Each time a leadership team requested a new metric or report, the data team had to spin up a new project and develop new data pipelines.
• 5-6 months was required for a data analyst to understand the content/data related to these metrics—often involving petabytes of raw data.
• The process involved managing over 1500 ETL pipelines, which led to inefficiencies (what we jokingly called “death by 2,000 ETLs”).
• Producing a single dashboard for C-level executives cost over $900,000.
• Even after completing the dashboard, they often discovered that the metrics were being defined and used inconsistently. Terms like “revenue,” “headcount,” or “store performance” were frequently understood differently depending on who worked on the report, making output reports unreliable and unusable. 

This is one example of why organizations are now seeking and investing in a coherent, integrated way to bridge these gaps and understand their vast data ecosystems. Because organizations often work with complex systems, ranging from CRMs and ERPs to data lakes and cloud platforms, extracting meaningful insights from this data requires a coherent, integrated view that can bridge these gaps. This is where the semantic layer serves as a pragmatic tool that enables organizations to bridge these gaps, streamline processes, and transform how data is used across departments. Specifically for these use cases, semantic data is gaining significant traction across diverse pockets of the organization as the standard interpreter between complex systems and business goals. 

 

Semantic Layer for Delivering Knowledge Intelligence 

Another reality many organizations are grappling with today is that basic AI algorithms trained in public data sets may not work well on organization and domain-specific problems, especially in domains where industry preferences are relevant. Thus, organizational knowledge is a prerequisite for success, not just for generative AI, but for all applications of enterprise AI and data science solutions. This is where experience and best practices in knowledge and data management lend the AI space effective and proven approaches to sharing domain and institutional knowledge. Especially for technical teams that are tasked with making AI “work” or provide value for their organization, they are looking for programmatic ways for explicitly modeling relationships between various data entities, providing business context to tabular data, and extracting knowledge from unstructured content, ultimately delivering what we call Knowledge Intelligence.

A well-implemented semantic layer abstracts the complexities of underlying systems and presents a unified, business-friendly view of data. It transforms raw data into understandable concepts and relationships, as well as organizes and connects unstructured data. This makes it easier for both data teams and business users to query, analyze, and understand their data, while making this organizational knowledge machine-ready and readable. The semantic layer standardizes terminology and data models across the enterprise, and provides the required business context for the data. By unifying and organizing data in a way that is meaningful to the business, it ensures that key metrics are consistent, actionable, and aligned with the company’s strategic objectives and business definitions.

Case Study: With the aforementioned global retailer, as their data and analytics teams worked to integrate siloed data and unstructured content, we partnered with them to build a semantic ecosystem that streamlined processes and provided the business context needed to make sense of their vast data. Our approach included: 

• Standardized Metadata and Vocabularies: Developed standardized metadata and vocabularies to describe their key enterprise data assets, especially for store metrics like sales performance, revenue, etc. This ensured that everyone in the organization used the same definitions and language when discussing key metrics. 
• Explicitly Defined Concepts and Relationships: We used ontologies and graphs to define the relationships between various domains such as products, store locations, store performance, etc. This created a coherent and standardized model that allowed data teams to work from a shared understanding of how different data points were connected.
• Data Catalog and Data Products: We helped the retailer integrate these semantic models into a data catalog that made data available as “data products.” This allowed analysts to access predefined, business-contextualized data directly, without having to start from scratch each time a new request was made.

This approach reduced report generation steps from 7 to 4 and cut development time from 6 months to just 4-5 weeks. Most importantly, it enabled the discovery of previously hidden data, unlocking valuable insights to optimize operations and drive business performance.

 

Semantic Layer as a Foundation for Reliable AI: Facilitating Human Reasoning and Explainable Decisions

Emerging technologies (like GenAI or Agentic AI) are democratizing access to information and automation, but they also contribute to the “dark data” problem—data that exists in an unstructured or inaccessible format but contains valuable, sensitive, or bad information. While LLMs have garnered significant attention in conversational AI and content generation, organizations are now recognizing that their data management challenges require more specialized, nuanced, and somewhat ‘grounded’ approaches that address the gaps in explainability, precision, and the ability to align AI with organizational context and business rules. Without this organizational context, raw data or text is often messy, outdated, redundant, and unstructured, making it difficult for AI algorithms to extract meaningful information. The key step to addressing this AI problem involves the ability to connect all types of organizational knowledge assets, i.e., using shared language, involving experts, related data, content, videos, best practices, lessons learned, and operational insights from across the organization. In other words, to fully benefit from an organization’s knowledge and information, both structured and unstructured information, as well as expert knowledge, must be represented and understood by machines. A semantic layer provides AI with a programmatic framework to make organizational context, content, and domain knowledge machine-readable. Techniques such as data labeling, taxonomy development, business glossaries, ontology, and knowledge graph creation make up the semantic layer to facilitate this process. 

Case Study: We have been working with a global foundation that had previously been through failed AI experiments as part of a mandate from their CEO for their data teams to “figure out a way” to adopt LLMs to evaluate the impact of their investments on strategic goals by synthesizing information from publicly available domain data, internal investment documents, and internal investment data. The challenge for previously failed efforts lay in connecting diverse and unstructured information to structured data and ensuring that the insights generated were precise, explainable, reliable, and actionable for executive stakeholders. To address these challenges, we took a hybrid approach that leveraged LLMs that were augmented through advanced graph technology and a semantic RAG (Retrieval Augmented Generation) agentic workflow. To provide the relevant organizational metrics and connection points in a structured manner, the solution leveraged an Investment Ontology as a semantic backbone that underpins their disconnected source systems, ensuring that all investment-related data (from structured datasets to narrative reports) is harmonized under a common language. This semantic backbone supports both precise data integration and flexible query interpretation. To effectively convey the value of this hybrid approach, we leveraged a chatbot that served as a user interface to toggle back and forth between the basic GPT model vs. the graph RAG solution. The solution consistently outperformed the basic/naive LLMs for complex questions, demonstrating the value of semantics for providing organizational context and alignment and ultimately, delivering coherent and explainable insights that bridged structured and unstructured investment data, as well as provided a transparent AI mapping that allowed stakeholders to see exactly how each answer was derived.

 

Closing 

Now more than ever, the understanding and application of semantic layers are rapidly advancing. Organizations across industries are increasingly investing in solutions to enhance their knowledge and data management capabilities, driven in part by the growing interest to benefit from advanced AI capabilities. 

The days of relying on a single, monolithic tool are behind us. Enterprises are increasingly investing in semantic technologies to not only work with the systems of today but also to future-proof their data infrastructure for the solutions of tomorrow. A semantic layer provides the standards that act as a universal “music sheet,” enabling data to be played and interpreted by any instrument, including emerging AI-driven tools. This approach ensures flexibility, reduces vendor lock-in, and empowers organizations to adapt and evolve without being constrained by legacy systems.

If you are looking to learn more about how organizations are approaching semantic layers at scale or are you seeking to unstick a stalled initiative, you can learn more from our case studies or contact us if you have specific questions.