The way knowledge assets are defined is crucial for an organization because it directly impacts how they are managed, leveraged, and protected. This includes identifying which knowledge assets have strategic value, how to manage them to make them accessible for timely decision making, which management policies should be applied to ensure effective knowledge sharing, retention, continuity, and transfer, and which steps are necessary to comply with industry regulations.

This blog highlights the types of knowledge assets that are commonly found in organizations and provides industry-specific examples based on typical Knowledge Management (KM) Use Cases.

Examples Of Relevant Knowledge Asset Types Per Industry

As illustrated in the previous section describing the different types of knowledge assets, these assets encompass more than just content or data. They may include people’s expertise and experience, transaction records, policies, and even facilities or locations. Depending on the industry or organization type, certain knowledge assets may be prioritized in early use cases because they play a more central role in those specific contexts.

	A manufacturing company looking to improve its supply chain processes would benefit significantly from tagging, managing, leveraging, and protecting operational and logistical resources — such as equipment, facilities, and products — and linking them to reveal relationships and dependencies across the supply chain. By also tagging and connecting additional knowledge assets, such as structured data and analytical resources — including order history, transactions, and metrics — and content and documentation — such as process descriptions and reports — the company may gain deeper visibility into operational bottlenecks, enhance forecasting accuracy, and improve coordination across departments. This holistic approach can enable more agile decision-making, reducing downtime and supporting continuous improvement across the entire manufacturing lifecycle.
	A bank that is looking to maintain compliance, uphold governance standards, and minimize regulatory risk can benefit from managing, leveraging, and protecting its key knowledge assets in a standardized and connected way. By using key terminology to tag governance and compliance resources — such as corporate policies, industry regulations, and tax codes — alongside operational and logistical resources  — such as locations and facilities — and corresponding subject matter experts, the bank builds a foundation for semantic alignment. This will allow the bank not only to associate branches and operational sites with the specific policies and regulatory obligations they must meet, but also help ensure that the bank complies with jurisdiction-specific requirements, reduces audit exposure, and strengthens its ability to respond to regulatory changes with agility and confidence.
	A healthcare organization relies on clinical expertise and institutional memory to diagnose and treat patients. By capturing, tagging, and sharing expertise and experience from physicians and multidisciplinary teams, doctors, nurses, and other support personnel will be able to timely access the expert-based information they need to diagnose and treat their patients more accurately. Additionally, having access to content and documentation from clinical cases and structured data from research studies will also help improve decision-making for the personnel of this healthcare organization.

Do you know which priority knowledge assets and related KM use cases can transform your organization by empowering teams to surface hidden insights, accelerating decision-making, or fostering operational excellence? If you need help uncovering the most valuable use cases and the associated knowledge assets that unlock meaningful transformation in your organization, we can help. Contact us to learn more. 

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Organizations generate, source, and consume vast amounts of unstructured data every day, including emails, reports, research documents, technical documentation, marketing materials, learning content and customer interactions. However, this wealth of information often remains hidden and siloed, making it challenging to utilize without proper organization. Unlike structured data, which fits neatly into databases, unstructured data often lacks a predefined format, making it difficult to extract insights or apply advanced analytics effectively.

Integrating unstructured data into a knowledge graph is the right approach to overcome organizations’ challenges in structuring unstructured data. This approach allows businesses to move beyond traditional storage and keyword search methods to unlock knowledge intelligence. Knowledge graphs contextualize unstructured data by linking and structuring it, leveraging the business-relevant concepts and relationships. This enhances enterprise search capabilities, automates knowledge discovery, and powers AI-driven applications.

This blog explores why structuring unstructured data is essential; the challenges organizations face, and the right approach to integrate unstructured content into a graph-powered knowledge system. Additionally, this blog highlights real-world implementations demonstrating how we have applied his approach to help organizations unlock knowledge intelligence, streamline workflows, and drive meaningful business outcomes.

Why Structure Unstructured Data in a Graph

Unstructured data offers immense value to organizations if it can be effectively harnessed and contextualized using a knowledge graph. Structuring content in this way unlocks potential and drives business value. Below are three key reasons to structure unstructured data:

1. Knowledge Intelligence Requires Context

Unstructured data often holds valuable information, but is disconnected across different formats, sources, and teams. A knowledge graph enables organizations to connect these pieces by linking concepts, relationships, and metadata into a structured framework. For example, a financial institution can link regulatory reports, policy documents, and transaction logs to uncover compliance risks. With traditional document repositories, achieving knowledge intelligence may be impossible, or at least very resource intensive.

Additionally, organizations must ensure that domain-specific knowledge informs AI systems to improve relevance and accuracy. Injecting organizational knowledge into AI models, enhances AI-driven decision-making by grounding models in enterprise-specific data.

2. Enhancing Findability and Discovery

Unstructured data lacks standard metadata, making traditional search and retrieval inefficient. Knowledge graphs power semantic search by linking related concepts, improving content recommendations, and eliminating reliance on simple keyword matching. For example, in the financial industry, investment analysts often struggle to locate relevant market reports, regulatory updates, and historical trade data buried in siloed repositories. A knowledge graph-powered system can link related entities, such as companies, transactions, and market events, allowing analysts to surface contextually relevant information with a single query, rather than sifting through disparate databases and document archives.

3. Powering Explainable AI and Generative Applications

Generative AI and Large Language Models (LLMs) require structured, contextualized data to produce meaningful and accurate responses. A graph-enhanced AI pipeline allows enterprises to:

A. Retrieve verified knowledge rather than relying on AI-generated assumptions likely resulting in hallucinations.

B. Trace AI-generated insights back to trusted enterprise data for validation.

C. Improve explain ability and accuracy in AI-driven decision-making.

Challenges of Handling Unstructured Data in a Graph

While structured data neatly fits into predefined models, facilitating easy storage and retrieval of unstructured data presents a stark contrast. Unstructured data, encompassing diverse formats such as text documents, images, and videos lack the inherent organization and standardization to facilitate machine understanding and readability. This lack of structure poses significant challenges for data management and analysis, hindering the ability to extract valuable insights. The following key challenges highlight the complexities of handling unstructured data:

1. Unstructured Data is Disorganized and Diverse

Unstructured data is frequently available in multiple formats, including PDF documents, slide presentations, email communications, or video recordings. However, these diverse formats lack a standardized structure, making extracting and organizing data challenging. Format inconsistency can hinder effective data analysis and retrieval, as each type presents unique obstacles for seamless integration and usability.

2. Extracting Meaningful Entities and Relationships

Turning free text into structured graph nodes and edges requires advanced Natural Language Processing (NLP) to identify key entities, detect relationships, and disambiguate concepts. Graph connections may be inaccurate, incomplete, or irrelevant without proper entity linking.

3. Managing Scalability and Performance

Storing large-scale unstructured data in a graph requires efficient modeling, indexing, and processing strategies to ensure fast query performance and scalability.

Complementary Approaches to Unlocking Knowledge Intelligence from Unstructured Data

A strategic and comprehensive approach is essential to unlock knowledge intelligence from unstructured data. This involves designing a scalable and adaptable knowledge graph schema, deconstructing and enriching unstructured data with metadata, leveraging AI-powered entity and relationship extraction, and ensuring accuracy with human-in-the-loop validation and governance.

1. Knowledge Graph Schema Design for Scalability

A well-structured schema efficiently models entities, relationships, and metadata. As outlined in our best practices for enterprise knowledge graph design, a strategic approach to schema development ensures scalability, adaptability, and alignment with business needs. Enriching the graph with structured data sources (databases, taxonomies, and ontologies) improves accuracy. It enhances AI-driven knowledge retrieval, ensuring that knowledge graphs are robust and optimized for enterprise applications.

2. Content Deconstruction and Metadata Enrichment

Instead of treating documents as static text, break them into structured knowledge assets, such as sections, paragraphs, and sentences, then link them to relevant concepts, entities, and metadata in a graph. Our Content Deconstruction approach helps organizations break large documents into smaller, interlinked knowledge assets, improving search accuracy and discoverability.

3. AI-Powered Entity and Relationship Extraction

Advanced NLP and machine learning techniques can extract insights from unstructured text data. These techniques can identify key entities, categorize documents, recognize semantic relationships, perform sentiment analysis, summarize text, translate languages, answer questions, and generate text. They offer a powerful toolkit for extracting insights and automating tasks related to natural language processing and understanding.

A well-structured knowledge graph enhances AI’s ability to retrieve, analyze, and generate insights from content. As highlighted in How to Prepare Content for AI, ensuring content is well-structured, tagged, and semantically enriched is crucial for making AI outputs accurate and context-aware.

4. Human-in-the-loop for Validation and Governance

AI models are powerful but have limitations and can produce errors, especially when leveraging domain-specific taxonomies and classifications. AI-generated results should be reviewed and refined by domain experts to ensure alignment with standards, regulations, and subject matter nuances. Combining AI efficiency with human expertise maximizes data accuracy and reliability while minimizing compliance risks and costly errors.

From Unstructured Data to Knowledge Intelligence: Real-World Implementations and Case Studies

Our innovative approach addresses the challenges organizations face in managing and leveraging their vast knowledge assets. By implementing AI-driven recommendation engines, knowledge portals, and content delivery systems, we empower businesses to unlock the full potential of their unstructured data, streamline processes, and enhance decision-making. The following case studies illustrate how organizations have transformed their data ecosystems using our enterprise AI and knowledge management solutions which incorporate the four components discussed in the previous section.

• AI-Driven Learning Content and Product Recommendation Engine
  A global enterprise learning and product organization struggled with the searchability and accessibility of its vast unstructured marketing and learning content, causing inefficiencies in product discovery and user engagement. Customers frequently left the platform to search externally, leading to lost opportunities and revenue. To solve this, we developed an AI-powered recommendation engine that seamlessly integrated structured product data with unstructured content through a knowledge graph and advanced AI algorithms. This solution enabled personalized, context-aware recommendations, improving search relevance, automating content connections, and enhancing metadata application. As a result, the company achieved increased customer retention and better product discovery, leading to six figures in closed revenue.
• Knowledge Portal for a Global Investment Firm
  A global investment firm faced challenges leveraging its vast knowledge assets due to fragmented information spread across multiple systems. Analysts struggled with duplication of work, slow decision-making, and unreliable investment insights due to inconsistent or missing context. To address this, we developed Discover, a centralized knowledge portal powered by a knowledge graph that integrates research reports, investment data, and financial models into a 360-degree view of existing resources. The system aggregates information from multiple sources, applies AI-driven auto-tagging for enhanced search, and ensures secure access control to maintain compliance with strict data governance policies. As a result, the firm achieved faster decision-making, reduced duplicate efforts, and improved investment reliability, empowering analysts with real-time, contextualized insights for more informed financial decisions.
• Knowledge AI Content Recommender and Chatbot
  A leading development bank faced challenges in making its vast knowledge capital easily discoverable and delivering contextual, relevant content to employees at the right time. Information was scattered across multiple systems, making it difficult for employees to find critical knowledge and expertise when performing research and due diligence. To solve this, we developed an AI-powered content recommender and chatbot, leveraging a knowledge graph, auto-tagging, and machine learning to categorize, structure, and intelligently deliver knowledge. The knowledge platform was designed to ingest data from eight sources, apply auto-tagging using a multilingual taxonomy with over 4,000 terms, and proactively recommend content across eight enterprise systems. This approach significantly improved enterprise search, automated knowledge delivery, and minimized time spent searching for information. Bank leadership recognized the initiative as “the most forward-thinking project in recent history.”
• Course Recommendation System Based on a Knowledge Graph
  A healthcare workforce solutions provider faced challenges in delivering personalized learning experiences and effective course recommendations across its learning platform. The organization sought to connect users with tailored courses that would help them master key competencies, but its existing recommendation system struggled to deliver relevant, user-specific content and was difficult to maintain. To address this, we developed a cloud-hosted semantic course recommendation service, leveraging a healthcare-oriented knowledge graph and Named Entity Recognition (NER) models to extract key terms and build relationships between content components. The AI-powered recommendation engine was seamlessly integrated with the learning platform, automating content recommendations and optimizing learning paths. As a result, the new system outperformed accuracy benchmarks, replaced manual processes, and provided high-quality, transparent course recommendations, ensuring users understood why specific courses were suggested.

Conclusion

Unstructured data holds immense potential, but without structure and context, it remains difficult to navigate. Unlike structured data, which is already organized and easily searchable, unstructured data requires advanced techniques like knowledge graphs and AI to extract valuable insights. However, both data types are complementary and essential for maximizing knowledge intelligence. By integrating structured and unstructured data, organizations can connect fragmented content, enhance search and discovery, and fuel AI-powered insights. 

At Enterprise Knowledge, we know success requires a well-planned strategy, including preparing content for AI,  AI-driven entity and relationship extraction, scalable graph modeling or enterprise ontologies, and expert validation. We help organizations unlock knowledge intelligence by structuring unstructured content in a graph-powered ecosystem. If you want to transform unstructured data into actionable insights, contact us today to learn how we can help your business maximize its knowledge assets.

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AI has attracted significant attention in recent years, prompting me to explore enterprise AI architectures through a multi-part blog series this year. Part 1 of this series introduced the key technical components required for implementing an enterprise AI architecture. Part 2 discussed our typical approaches and experiences in structuring unstructured content with a semantic layer. In the third installment, we will focus on leveraging structured data to power enterprise AI use cases.

Today, many organizations have developed the technical ability to capture enormous amounts of data to power improved business operations or compliance with regulatory bodies. For large organizations, this data collection process is typically decentralized so that organizations can move quickly in the face of competition and regulations. Over time, such decentralization results in increased complexities with data management, such as inconsistent data formats across various data platforms and multiple definitions for the same data concept. A common example in EK’s engagements includes reviewing customer data from different sources with variations in spelling and abbreviations (such as “Bob Smith” vs. “Robert Smith” or “123 Main St” vs. “123 Main Street”), or seeing the same business concept (such as customer or supplier) being referred to differently across various departments in an organization.  Obviously, with such extensive data quality and inconsistency issues, it is often impossible to integrate and harmonize data from the diverse underlying systems for a 360-degree view of the enterprise and enable cross-functional analysis and reporting. This is exactly the problem a semantic layer solves.  

A semantic layer is a business representation of data that offers a unified and consolidated view of data across an organization. It establishes common data definitions, metadata, categories and relationships, thereby enabling data mapping and interpretation across all organizational data assets. A semantic layer injects intelligence into structured data assets in an organization by providing standardized meaning and context to the data in a machine-readable format, which can be readily leveraged by Artificial Intelligence (AI) systems. We call this process of embedding business context into organizational data assets for effective use by AI systems knowledge intelligence (KI).  Providing a common understanding of structured data using a semantic layer will be the focus of this blog. 

How a Semantic Layer Provides Context for Structured Data 

A semantic layer provides AI with a programmatic framework to make organizational context and domain knowledge machine readable. It does so by using one or more components such as metadata, business glossary, taxonomy, ontology and knowledge graph. Specifically, it helps enterprise AI systems:

• Leverage metadata to power understanding of the operational context;
• Improve shared understanding of organizational nomenclature using business glossaries;
• Provide a mechanism to categorize and organize the same data through taxonomies and controlled vocabularies;
• Encode domain-specific business logic and rules in ontologies; and
• Enable a normalized view of siloed datasets via knowledge graphs 

Embedding Business Context into Structured Data: An Architectural Perspective

The figure below illustrates how the semantic layer components work together to enable Enterprise AI. This shows the key integration patterns via which structured data sources can be connected using a knowledge graph in the KI layer,including batch and incremental data pull using declarative and custom data mappings, as well as data virtualization.

Enterprise AI Architecture: Injecting Business Content into Structured Data using a Semantic Layer

AI models can reason and infer based on explicit knowledge encoded in the graph. This is achieved when both the knowledge or data schema (e.g. ontology) and its instantiation are represented in the knowledge graph. This representation is made possible through a custom service that allows the ontology to be synchronized with the graph (labeled as Ontology Sync with Graph in the figure) and graph construction pipelines described above.

Enterprise AI can derive additional context on linked data when taxonomies are ingested into the same graph via a custom service that allows the taxonomy to be synchronized with the graph (labeled as Taxonomy Sync with Graph in the figure). This is because taxonomies can be used to consistently organize this data and provide clear relationships between different data points. Finally, technical metadata collected from structured data sources can be connected with other semantic assets in the knowledge graph through a custom service that allows this metadata to be loaded into the graph (labeled as Metadata Load into Graph in the figure). This brings in additional context regarding data sourcing, ownership, versioning, access levels, entitlements, consuming systems and applications into a single location.

As is evident from the figure above ,a semantic layer enables data from different sources to be quickly mapped and connected using a variety of mapping techniques, thus enabling a unified, consistent, and single view of data for use in advacned analytics. In addition, by injecting business context into this unified view via semantic assets such as taxonomies, ontologies and glossaries, organizations can power AI applications ranging from semantic recommenders and knowledge panels to traditional machine learning (ML) model training and LLM-powered AI agents.

Case Studies & Enterprise Applications

In many engagements, EK has used semantic layers with structured data to power various use cases, from enterprise 360 to AI enablement. As part of enterprise AI engagements, a common issue we’ve seen is a lack of business context surrounding data. AI engineers continue to struggle to locate relevant data and ensure its suitability for specific tasks, hindering model selection and leading to suboptimal results and abandoned AI initiatives. These experiences show that raw data lacks inherent value; it becomes valuable only when contextualized for its users. Semantic layers provide this context to both AI models and AI teams, driving successful Enterprise AI endeavors.

Last year, a global retailer partnered with EK to overcome delays in retrieving store performance metrics and creating executive dashboards. Their centralized data lakehouse lacked sufficient metadata, hindering engineers from locating and understanding crucial metrics. By standardizing metadata, aligning business glossaries, and establishing taxonomy, we empowered their data visualization engineers to perform self-service analytics and rapidly create dashboards. This streamlined their insight generation without relying on source data system owners and IT teams. You can read more about how we helped this organization democratize their AI efforts using a semantic layer here.

In a separate case, EK facilitated the rapid development of AI models for a multinational financial institution by integrating business context into the company’s structured risk data through a semantic layer. The semantic layer expedited data exploration, connection, and feature extraction for the AI team, leading to the efficient implementation of enterprise AI systems like intelligent search engines, recommendation engines, and anomaly detection applications. EK also integrated AI model outputs into the risk management graph, enabling the development of proactive alerts for critical changes or potential risks, which, in turn, improved the productivity and decision-making of the risk assessment team.

Finally, the significant role a semantic layer plays in reducing data cleaning efforts and streamlining data management. Research consistently shows AI teams spend more time cleaning data than modeling it to produce valuable insights. By connecting previously siloed data using an identity graph, EK helped a large digital marketing firm gain a deeper understanding of its customer base through behavior and trend analytics. This solution resolved the discrepancy between 2 billion distinct records in their relational databases and the actual user base of 240 million.

Closing

Semantic layers effectively represent complex relationships between data objects, unlike traditional applications built for structured data. This allows them to support highly interconnected use cases like analyzing supply chains and recommendation systems. To adopt this framework, organizations must shift from an application-centric to a data-centric enterprise architecture. A semantic layer ensures that data retains its meaning and context when extracted from a relational database. In the AI era, this metadata-first framework is crucial for staying competitive. Organizations need to provide their AI systems with a consolidated, context-rich view of all transactional data for more accurate predictions. 

This article completes our discussion about the technical integration between semantic layers and enterprise AI, introduced here. In the next segment of this KI architecture blog series, we will move onto the second KI component and discuss the technical approaches for encoding expert knowledge into enterprise AI systems.

To get started with leveraging structured data, building a semantic layer, and the KI journey at your organization, contact EK!

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The Solution

Alongside Enterprise Knowledge (EK), the organization embarked on a phased approach to develop a scalable knowledge, data, and information management strategy. EK began by designing a global content and data strategy in parallel with an enterprise search redesign effort that featured an information architecture overhaul. A taxonomy and corresponding content types were designed to support auto-tagging and the automated organization of unstructured content, while also allowing for the transformation of the organization’s content into a machine-readable format.

The second half of the approach included identifying scaled integration points across the organization’s content, allowing for advanced inter-content relationships to be utilized by recommendation engines in the future. Ontologies and knowledge graphs were introduced as a means of automating the application of these relationships while also optimizing the use and reuse of the organization’s data and information. To further support the management and scalability of the strategy and design efforts over time, an organizational model and governance plan were developed to support change management, implementation, and adoption.

The EK Difference

Because this large, global organization was seeking to successfully complete an initiative that traversed multiple departments, the effort required alignment and support from department leads, staff, and executives. EK leveraged our proven facilitation and prioritization approaches tailored specifically to information and data management strategy and led strategic discussions with the company’s executives, global program leadership, and staff to align on the “as-is” and “to-be” states of the effort. We developed relevant business impact and ROI measures by identifying prioritized success and performance factors that were evaluated and adjusted consistently throughout the effort. 

EK further leveraged our expertise in ontology and enterprise knowledge graphs to design an information architecture that defined the relationships across disparate content and built the foundation for advanced capabilities, such as automated tagging, content governance, natural language search, data analytics, and future AI and Machine Learning (ML) capabilities.

The Results

The knowledge and information management program allowed the organization to better understand and capitalize on their market insights and, as a result, discover and utilize otherwise inaccessible data. Connections between knowledge assets are now defined and the information architecture and content strategy benefit from a taxonomy and metadata design that account for both structured and unstructured data. 

EK also revamped the company’s internal search experience by redesigning indexing processes and leading Design Thinking sessions to inform both UI and UX search design decisions, ultimately integrating action-oriented results across the intranet. Consequently, users found that returned results were more relevant to their queries and a user-friendly interface personalized for the organization’s staff facilitated system access and ease-of-use.

The KM organizational structure will ensure that stakeholders are enabled to make informed investment decisions about their data and content management systems and will better understand the relationships required to bring them all together. As AI capabilities become more advanced and accessible on a global scale, the organization will not only be operating ahead of the curve, but will be able to adapt and apply these capabilities on a regular basis.

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If you are exploring ways your own enterprise can benefit from implementing AI capabilities, we can help! EK has deep experience in designing and implementing solutions that optimizes the way you use your knowledge, data, and information, and can produce actionable and personalized recommendations for you. Please feel free to contact us for more information.

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