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!

The post Enterprise AI Architecture Series: How to Inject Business Context into Structured Data using a Semantic Layer (Part 3) appeared first on Enterprise Knowledge.

At EK, we strive to find a balance between the top-down and bottom-up perspectives during the scoping process. Our approach seeks to anticipate the most common needs of users while leaving the door open to meet the dynamic situations that emerge from the content or systems when users interact with them. There have been cases in which the information needed to spec out a solution is not available in business databases, policies, or content, and stakeholders don’t have any insights into user journeys due to the lack of concrete information emerging from the regular conduction of the business. 

When the organization doesn’t have representative business content to leverage, a resource that might provide just the right information to launch your scoping journey is architecture diagrams. In a previous blog we enticed the idea that Entity Relationship Diagrams (ERD) can be used as blueprints to navigate and leverage the data stored in relational databases. In this entry, we will dive a bit deeper into the information represented in diagrams, specifically in UML diagrams, and discuss some of the advantages and pitfalls of utilizing them in the early stages of your solution design. We will also share examples of the types of UML diagrams that we’ve found most helpful during the solution modeling process.

What are UML diagrams and how can they supplement your solution scope?

UML, shorthand for Unified Modeling Language, is a specification that allows IT professionals to represent the design and functionality of software systems through standard graphical elements: UML diagrams. There are fourteen UML diagram types grouped in two main categories: structural and behavioral. Structural diagrams show a system’s structure, behavioral diagrams show how a system should function.

Given their capability to convey information in a syntactic and conventional format, these pictorial representations are some of the most popular tools in every software engineer’s toolkit. For the business owners and information managers, UML diagrams allow teams to visualize how a process is working, the sequence of tasks, how the data flows from one platform to another, and the systems that produce it. Below you will find a summary of the notation for a class diagram.

As you embark on your scoping journey, consider stress testing your solution’s requirements, functionalities, and assumptions against the organizational ecosystem illustrated in the UML diagrams. You can compare your design to the design of systems elsewhere in the organization, identifying points of alignment as well as the unique features of your own solution. This iterative exercise will let you construct a more accurate and complete picture of the agents, processes, scenarios, behaviors and data that you scoped.

A piece of advice before you move further down the page. It is important to warn you against the temptation to simply take any existing UML model and replicate it in your scope. Instead, you should analyze UML diagrams just as you would any other piece of content that you receive: assessing their shortcomings in their current state as well as their capabilities to support your envisioned “end state” solution.

Where do I start?

Now that we know that there are business artifacts that can function as charts to navigate the organizational information ecosystem, we can go back to the original question of where to start. In order to capitalize on the information contained in a UML diagram, first and foremost, you need to set a baseline that represents what you expect from your solution, whatever this might be. A simple way of visualizing your thoughts is to draw a diagram. You can use pen and paper, a whiteboard, or a diagramming software. For your drawing, think of the “things” that you want your model to represent and how they relate to each other.

In the initial phase, your drawing doesn’t have to be complete or accurate. What is important is that you capture the information that reflects your current view and understanding of the environment you will be operating in and the outcomes you would like to see. You should expect challenges, questions, and expansions to your original drawing. Changes are part of the development cycle, particularly during the initial stages.

While you can start your definition process at any level of abstraction, try to think in “concrete” objects. As you grow in your modeling competency and knowledge of the environment, you will be able to incorporate more abstract concepts in your model. Some relatable examples might be customers, orders, and products. These will be your concepts and can be represented as labeled circles.

To represent relationships between concepts, you can simply draw a line from one circle to another and add a word or a short phrase that describes the connection.

Congratulations! You’ve successfully completed the first step in your scoping journey.

Talk to your stakeholders

At this point, you may want to show your diagram to stakeholders. The goal is to gather sufficient information that allows you to develop a functional definition for each one of the concepts and relationships that you originally came up with. This is particularly important if you are thinking about using your solution to support interoperable applications. 

In our example above, the definition of what a product is may differ by department, team, or source of revenue. For instance, does product encompass services? Who is a customer? Can both people and organizations be customers? What information do we collect about them? Is a customer the same as a client, a patron, or a shopper (all terms currently in use by different teams)?

It doesn’t matter if your solution is centered around taxonomies, ontologies, or knowledge graphs. They all are about meaning. Explicit meaning allows people and machines to collect, process, and exchange information more efficiently, and reduces the risk of misinterpretation and downtime due to data incompatibility. It is worth the time and effort you spend clarifying and documenting the variations and nuances of each piece of content that you analyze.

Ask the DBAs for their UML diagrams

It is now time to use your knowledge of UML diagrams. In addition to your business owners, database administrators (DBAs) are a group you will want to reach out to during the early phases of your scope definition. Since their function is to design databases and structures for new and existing applications, DBAs usually have a grounded perspective of the overall application ecosystem and infrastructure of an organization. You should definitely seek their expertise to inform your solution design.

You need to be strategic and determine the type of diagram that could provide the information that you need to consolidate your initial design. When you meet with the DBAs, ask them to walk you through their UML diagrams. Do they validate the concepts or relationships  that came out of your initial “pen and paper” drawing?

If you go back to the types of diagrams listed at the beginning of this post, you will notice that there is a diagram that allows software engineers to represent classes and relationships: the Class diagram. The following image is representing the same concepts from our example above: customer, order, and product.

By studying these drawings, you can start filling gaps in your knowledge, refining or reassuring your initial assumptions and understanding. Once again, you must make the extra effort not to replicate what currently exists. The goal with this analysis is to identify reusable elements but, more importantly, to make note of any missing pieces that are critical to the success of your solution. In our original drawing for instance, we had considered concepts for customer, product, and order. By inspecting the UML diagram we realize that it might be worth considering adding the concept of payment to our ontology or breaking down the product concept into more specific subconcepts. We can also reach the conclusion that our solution does not require distinguishing between domestic and international customers.

Another common situation is when a solution demands that you specify the causes for an event. In those cases, an Activity diagram might be a more appropriate artifact to analyze since they denote the actions, decision points, flows, start and end points of a given process.

In the diagram below, we are showcasing the standard notation for Activity diagrams and specifying what happens when a customer places an order. Once again, compare against your “pen and paper” drawing. Do you need a concept for the shipping company or the agent that processes, fills, and closes the order?

As a final word, we would like to reiterate that it is extremely easy to get caught on the details of what currently exists. Try to steer clear from discussing particulars such as the names of tables, columns, and data types, or to try to pin down the specifics of any existing platforms and deployments. At the onset, you should strive to define your solution in system-agnostic terms and detach it from specific implementations. Your ultimate goal when consulting UML diagrams is to help you determine what’s in and out of scope as well as the essential components of your desired solution. This is how you start assessing information gaps, prioritizing systems, clarifying processes, and identifying key partners and stakeholders to collect additional input. Not a bad place to start!

Conclusion

Many of our projects have benefited from incorporating UML diagrams as part of the initial information gathering activities. This is no surprise. UML diagrams are concise sources of information that can augment, validate, prove or disprove the assumptions, preconditions, and requirements established in preliminary scopes or solution designs.

For more information on case studies where we’ve leveraged these techniques to scope solutions or to get help doing so for your organization, contact us at info@enterprise-knowledge.com.

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The Solution

Enterprise Knowledge (EK) worked with the agency to define a Semantic Enterprise Architecture strategy that provided a big picture view of the constraints and limitations imposed by duplicate application functionalities, data assets, and information assets. The development of the agency’s enterprise architecture strategy consisted of working with the agency’s numerous directorates to define the current state architecture and assess existing applications against a maturity matrix. The current state definition of architecture consisted of defining relationships between applications, data assets, information assets, business processes, and organizational roles. By defining the current state architecture, the agency had a clear understanding of the applications that existed across the enterprise and their purpose. This maturity assessment provided the agency with clear opportunities for improvement to help determine applications with overlapping purposes and modernize applications with functionalities that did not align with the agency’s strategic goals.

The EK Difference

Using a hybrid analysis approach, consisting of a combination of user-driven research (facilitated workshops, focus groups, and interviews) and technology-driven research (in-depth analysis of the existing technology), EK captured the current Enterprise Architecture using our Semantic Enterprise Architecture metamodel inspired by The Open Group Architecture Framework (TOGAF). In addition to the EA maturity assessment matrix, EK captured the architecture using a knowledge graph repository. Such a knowledge graph driven approach to Enterprise Architecture allowed us to iteratively capture relationships between the application layer, business layer, information layer, and technology layer. Using these approaches,  EK was able to capture critical information about systems and application functionality within the agency’s application portfolio and enterprise infrastructure including: 

• Data usage and storage; 
• Security; and 
• Integration. 

The maturity matrix provided valuable information on the relevance of existing applications. 

The Results

Leveraging EK’s Semantic Enterprise Architecture approach in combination with the maturity matrix, the agency has clear architectural descriptions of applications, information assets, data assets, business processes, and organizational roles cleanly organized in a flexible graph database. This allowed for better short-term and long-term strategic decision making around data, security, integration, new design requirements, sustainability, and future support.  Further, the agency now has clear visibility of applications across the organization that need to be updated or retired. This visibility, combined with the current state architecture and the maturity assessment, allows the agency to see not only when an application needs to be updated or retired, but also how it is addressing business problems and who in the organization is impacted by the retirement of applications. 

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