Enterprise Architecture Glossary

EA Core Concepts

Enterprise Architecture The discipline of aligning an organization’s business strategy with its operating model, technology, data, and processes in a coherent, governed way. EA makes the invisible structure of an enterprise visible and manageable. It is both a practice and a body of artifacts. See also: Architecture Repository, Architecture Framework.

Architecture Repository The central store where all EA artifacts live: models, diagrams, decisions, standards, and reference data. In Sparx EA, the repository is a database: a local file for one modeler, or a shared database such as SQL Server, Oracle, MySQL or PostgreSQL for a team, accessed through the desktop client and Pro Cloud Server. A well-governed repository is the difference between EA as a one-off exercise and EA as an ongoing capability. See also: Pro Cloud Server (PCS), Sparx EA.

Architecture Framework A structured set of principles, methods, and models for producing, organizing, and governing architecture artifacts. Frameworks give teams a common language so they do not have to invent their own. TOGAF is the most widely adopted; others include DoDAF, FEAF, and BIAN. See also: TOGAF, Metamodel.

Metamodel The model of your modeling language: the rules that define which elements exist, what properties they carry, and how they relate to each other. In Sparx EA, the built-in metamodel can be extended or replaced using MDG Technology. AI can only read a model reliably when the metamodel is right. See also: MDG Technology.

MDG Technology Model Driven Generation Technology: Sparx EA’s mechanism for defining and distributing custom metamodels, diagram toolboxes, and stereotypes across a team or enterprise. MDG profiles are the primary governance instrument for ensuring consistent modeling. When AI tools query your EA repository, well-formed MDG profiles are what make the data intelligible. Kernaro AI Hub’s MCP server, for example, needs an MDG for its GraphQL translation layer. See also: Metamodel, Kernaro AI Hub.

Architecture Pattern A reusable, named solution to a recurring architecture problem. A pattern is guidance that each team adapts to its own situation. Documenting patterns in the EA repository with Sparx EA turns institutional knowledge into a governed, searchable asset. See also: Reference Architecture.

Capability Map A structured view of what an organization does, expressed as discrete capabilities instead of organizational units or processes. Capabilities are stable over time even as structures and processes change, making them the ideal anchor for investment decisions and gap analysis. A capability map is usually the right place to start with EA. See also: Business Architecture, Value Stream.

Value Stream An end-to-end sequence of activities that delivers value to a customer or stakeholder. Value streams cross organizational boundaries, which is precisely what makes them useful for spotting inefficiency and misalignment. In ArchiMate, a value stream is a strategy element. See also: Capability Map, Business Architecture.

Business Architecture The layer of EA concerned with strategy, business models, capabilities, value streams, organizational structures, and processes. Business architecture provides the “why” and “what” that technology architecture must serve. Without it, technology investment decisions lack grounding. See also: Application Architecture, Capability Map.

Application Architecture The layer of EA that describes the software applications an organization uses, how they are structured internally, and how they interact with each other and with the business. Application architecture is where most legacy complexity becomes visible. See also: Business Architecture, Technology Architecture.

Technology Architecture The layer of EA concerned with the infrastructure, platforms, cloud services, and technology standards that host and connect applications. Technology architecture decisions have long lead times and high switching costs, making governance here especially important. See also: Application Architecture, Data Architecture.

Data Architecture The layer of EA that defines how data is produced, stored, governed, and consumed across the enterprise. Data architecture has grown in importance with the rise of AI: models are only as good as the data they reason over. See also: Technology Architecture, Ontology.

Architecture Principles Agreed statements that guide architecture decisions consistently across the enterprise. Principles are enduring preferences with a stated rationale. A good principle names the trade-off it is making and why. Example: “We prefer cloud-native services over self-hosted equivalents, except where data sovereignty requires otherwise.” See also: Architecture Decision Record (ADR).

Architecture Decision Record (ADR) A short document that captures a significant architecture decision: its context, the options considered, the decision made, and the consequences accepted. ADRs are the institutional memory of your architecture program. Stored in Sparx EA, they become queryable artifacts rather than buried documents. See also: Architecture Principles, Architecture Repository.

Architecture Runway The extent to which the current architecture can support near-term feature and capability delivery without significant rework. A short runway forces reactive, expensive changes; a long runway enables agile delivery. Managing runway is a core responsibility of an EA practice embedded in product or program delivery. See also: Technical Debt, Architecture Roadmap.

Reference Architecture A proven, reusable architecture template for a recurring class of solutions: patterns, standards, and structure captured once so teams don’t start from a blank page. In Sparx EA, reference architectures live in the repository as governed, extendable models. See also: Architecture Pattern, MDG Technology.

Architecture Modeling The work of capturing the organization, its systems, and their relationships as a formal model: elements, connectors, and diagrams in the repository rather than pictures in a document. One of the four use cases of AI-Augmented Architecture, and the foundation the others build on. See also: Architecture Analysis, Diagram vs Model.

Architecture Analysis Explaining how the parts of an organization, system, or ecosystem relate, and assessing the impact of change. Worked by hand, it is bounded by how much one person can hold at once. With AI, the analysis can span the whole repository, and the architect judges what the results mean. One of the four use cases of AI-Augmented Architecture. See also: Architecture Modeling, Architecture Governance.

Architecture Disciplines

Application Portfolio Management (APM) The discipline of cataloging, rationalizing, and governing the organization’s applications by judging redundancy, consolidation, lifecycle, and business value across the estate. Done the modern way in Sparx EA, the model connects to the PPM, ITSM, eGRC and ITFM systems that already hold the portfolio data instead of storing static values, so the portfolio stays current. See also: Capability Map, Technology Architecture.

Process Modeling The discipline of capturing how work gets done (activities, decisions, roles, and flows), usually in BPMN and DMN, and connecting it to the capabilities and applications that support it. See also: BPMN, DMN.

Model-Based Systems Engineering (MBSE) An engineering discipline that replaces document-centric systems engineering with a connected model of requirements, behavior, and structure, typically in SysML, so the system is specified, analyzed, and verified as one coherent model. See also: SysML, Requirements Management.

Solution Architecture The discipline of designing a specific solution to a business need: choosing the approach, defining components and integrations, and tracing the design back to the requirements and capabilities it serves. See also: Application Architecture, Reference Architecture.

Software Architecture The discipline of defining the structure of a software system (its components, interfaces, dependencies, and patterns) and keeping that model honest as the codebase evolves. See also: UML, Technical Debt.

IT Architecture The discipline concerned with the technology and infrastructure estate: platforms, networks, cloud services, and the standards and dependencies that connect them. Closely related to Technology Architecture, with an operational, estate-wide emphasis. See also: Technology Architecture, Reference Architecture.

Requirements Management The discipline of capturing, organizing, tracing, and validating requirements against the architecture and design, so coverage gaps and change impacts are visible in the model rather than discovered late. See also: Model-Based Systems Engineering (MBSE), Architecture Decision Record (ADR).

Enterprise Architecture & Data Architecture Two further domain specialties, defined in full under EA Core Concepts above. See also: Enterprise Architecture, Data Architecture.

Modeling Languages

ArchiMate An open, vendor-neutral modeling language for enterprise architecture, maintained by The Open Group. ArchiMate provides a layered structure (motivation, strategy, business, application, technology, physical, implementation) with well-defined element types and relationship semantics. It is the preferred language for cross-layer EA modeling. Sparx EA ships with full ArchiMate support. See also: TOGAF, UML.

BPMN Business Process Model and Notation: the ISO standard for modeling business processes. BPMN diagrams are readable by both business and technical stakeholders, making them a valuable communication tool. BPMN models well in Sparx EA and is often combined with ArchiMate: ArchiMate for structure, BPMN for process detail. See also: ArchiMate, UML.

UML Unified Modeling Language: the standard for object-oriented and software design modeling. UML covers structural diagrams (class, component, deployment) and behavioral diagrams (sequence, activity, state machine). Sparx EA was built on UML and remains one of the strongest UML tools available. See also: BPMN, SysML.

SysML Systems Modeling Language: a UML profile adapted for systems engineering. SysML adds requirements diagrams and parametric diagrams to UML’s repertoire, making it suitable for cyber-physical systems and defense or aerospace programs. Sparx EA has strong SysML support. See also: UML, DoDAF.

DMN Decision Model and Notation: the OMG standard for modeling business decisions and decision logic. DMN separates decision structure (decision requirements diagrams) from decision logic (decision tables). It is particularly useful alongside BPMN for modeling automated decision processes. See also: BPMN.

Notation The visual vocabulary of a modeling language: the shapes, lines, colors, and labels that represent model elements on a diagram. Notation is the surface; the metamodel is the meaning beneath it. Consistent notation is a governance concern, enforced in Sparx EA through MDG Technology. See also: Metamodel, MDG Technology.

Viewpoint A specification of the conventions for constructing a view: which concerns it addresses, which element types it includes, and which stakeholders it is intended for. The TOGAF Architecture Content Framework and ArchiMate 3 both define standard viewpoints. See also: Stakeholder View, Concern.

Concern A specific interest that a stakeholder has in the architecture: performance, security, cost, compliance, scalability. Architecture views are constructed to address specific concerns. Tracing concerns to viewpoints to views is the formal discipline of architecture description. See also: Viewpoint, Stakeholder View.

Stakeholder View A representation of the architecture that addresses the concerns of one or more stakeholders. A view is always produced from a viewpoint. Effective EA produces views that stakeholders use, which requires understanding what each stakeholder cares about. See also: Viewpoint, Concern.

Diagram vs Model A diagram is a graphical representation. A model is the structured, semantically rich data underneath. In Sparx EA, diagrams are windows into the model, so the same element can appear on many diagrams without being duplicated in the model. AI tools query the model, not the diagram. This distinction matters whenever an AI tool connects to the repository, whether through Kernaro AI Hub’s MCP server or AI Power Tools for Sparx EA. See also: MCP Server, Architecture Repository.

Frameworks

TOGAF The Open Group Architecture Framework: the world’s most widely adopted enterprise architecture framework. TOGAF provides the Architecture Development Method (ADM), a cyclic process for developing and governing architecture, along with content frameworks, capability frameworks, and reference models. Use TOGAF as a starting point and adapt it. See also: ArchiMate, Architecture Framework.

ArchiMate See Modeling Languages section. As a framework entry: ArchiMate is maintained by The Open Group and is designed to work alongside TOGAF: TOGAF provides the method and ArchiMate provides the language. See also: TOGAF.

DoDAF The US Department of Defense Architecture Framework: a framework designed for defense acquisition and systems engineering. DoDAF organizes views into viewpoints (All, Capability, Data and Information, Operational, Project, Services, Standards, Systems). Widely used in US defense and by allied defense organizations. See also: MODAF, NAF, SysML.

MODAF The UK Ministry of Defense Architecture Framework: a framework closely aligned with DoDAF but adapted for UK defense procurement and operations. MODAF has largely been superseded by NAF in UK and NATO contexts but remains in use in legacy programs. See also: DoDAF, NAF.

NAF The NATO Architecture Framework: developed to support NATO interoperability and coalition operations. NAF v4 was designed to align with TOGAF and ArchiMate, making it more accessible to commercial EA architects entering defense programs. See also: DoDAF, MODAF, ArchiMate.

FEAF The US Federal Enterprise Architecture Framework: a framework for managing EA across US federal government agencies. FEAF organizes architecture across performance, business, data, application, infrastructure, and security sub-architectures. See also: TOGAF, Architecture Framework.

BIAN The Banking Industry Architecture Network framework: an industry reference model for banking business architecture. BIAN provides a Service Landscape of banking capabilities and service domains, making it valuable for financial services EA programs as a starting point for capability mapping. See also: Capability Map, TM Forum ODA.

TM Forum ODA The TM Forum Open Digital Architecture: a reference architecture framework for telecommunications companies. ODA provides a component-based architecture model with well-defined business capability components and API standards. Analogous to BIAN but for telco. See also: BIAN, Architecture Framework.

Sparx EA Platform

Stereotype A named extension of a base modeling element that gives it a specialized type, icon, and properties. Stereotypes are the building blocks of a custom metamodel, defined and distributed through MDG Technology. See also: MDG Technology, Tagged Value.

Tagged Value A custom name-value property attached to a model element, capturing attributes the base type doesn’t carry: lifecycle, owner, criticality, cost. Tagged values are what turn elements into queryable, AI-readable data. See also: Stereotype, Metamodel.

Prolaborate Sparx Systems’ web-based collaboration and publishing layer over the EA repository, giving non-modeling stakeholders dashboards, reviews, and read/feedback access without an Enterprise Architect license. See also: Kernaro AI Hub, Pro Cloud Server (PCS).

Sparx EA Enterprise Architect by Sparx Systems: a desktop-based modeling tool used for enterprise architecture. Sparx EA supports UML, ArchiMate, BPMN, SysML, TOGAF, and custom metamodels via MDG Technology. Its repository model (shared database) enables team collaboration at enterprise scale. See also: EA Repository, Pro Cloud Server (PCS).

EA Repository The shared database that stores all model content in a Sparx EA deployment. The repository is the single source of truth for all architecture artifacts: elements, relationships, diagrams, documents, baselines, and tagged values. Repository integrity is the foundation of everything else. See also: Pro Cloud Server (PCS), Sparx EA, Baseline.

Pro Cloud Server (PCS) Sparx Systems’ server component for Sparx EA repositories. It lets the desktop client connect to a shared repository over HTTP or HTTPS, serves WebEA so people can view the model and make limited edits in a browser, provides the repository connection Prolaborate uses, and runs integrations with other enterprise tools. See also: Sparx EA, EA Repository.

Kernaro AI Hub Sparx Systems’ web platform over the EA repository. Architects and stakeholders ask questions of the model in plain language, run impact analysis, build reports and diagrams, and connect the model to delivery and collaboration tools. It includes a read-only MCP server and a read-only GraphQL API for enterprise-wide access. Bring-your-own LLM: the customer supplies the keys and pays for usage. See also: Kernaro Assist, Kernaro Connector for Power BI.

Kernaro Assist Sparx Systems’ AI assistant inside the Enterprise Architect client. Architects analyze, create, change and validate model content in plain language without leaving the tool. Bring-your-own LLM: the customer supplies the keys and pays for usage. See also: Kernaro AI Hub, Model Context Protocol (MCP).

Kernaro Connector for Power BI An extension to Kernaro AI Hub that brings Enterprise Architect data into Power BI. When an organization has Kernaro AI Hub, it is the preferred route to Power BI. Once the Power BI semantic model is published, Microsoft 365 Copilot can answer questions from it. See also: Kernaro AI Hub.

Reporting Database A set of business-readable tables built on top of a Sparx EA repository, so Power BI, AI tools and a data warehouse can report across the whole model. If you host the repository yourself, the reporting views are built in the same database and you read from them live. If you don’t have access to the database, or it’s hosted elsewhere, and you need the reporting database in something like Snowflake or Microsoft Fabric, it is set up so you refresh it from your EA model on the schedule you define. See also: Kernaro Connector for Power BI, Semantic Layer, EA Repository.

MDG Technology See EA Core Concepts section. As a platform entry: in Sparx EA, MDG Technologies are the primary mechanism for distributing governance: custom element types, stereotypes, tagged values, toolboxes, and validation rules across the modeling team. Organizations without well-maintained MDG profiles find their models drift into inconsistency over time. See also: Metamodel.

MCP Server Model Context Protocol Server: a server that exposes structured data and tools to AI agents and large language models through the Model Context Protocol standard. Sparx EA core has no native MCP server. Kernaro AI Hub from Sparx Systems includes a read-only MCP server, deployed on a server for enterprise-wide access, which needs an MDG for its GraphQL translation layer. AI Power Tools for Sparx EA, free from NovoCircle, runs a local MCP server on the architect’s machine that can read and change the model. See also: Model Context Protocol (MCP), Kernaro AI Hub, AI Power Tools for Sparx EA.

GraphQL A query language and runtime for APIs that allows clients to request exactly the data they need. In the Sparx ecosystem, Kernaro AI Hub offers a read-only GraphQL API, and GraphQL is the translation layer behind Hub’s MCP server. That layer needs an MDG to describe the model it serves. See also: MCP Server, Semantic Layer.

Package The primary organizational unit within a Sparx EA repository. Packages are hierarchical containers for model elements and diagrams, analogous to folders. Package structure reflects governance decisions: who owns what, what belongs where, and how artifacts are versioned. See also: Baseline, EA Repository.

Baseline A snapshot of a Sparx EA package at a point in time, used for change tracking and version comparison. Baselines enable “before and after” analysis of model changes and support architecture governance by providing an audit trail. See also: Package, Architecture Decision Record (ADR).

Model Search Sparx EA’s built-in capability for querying the repository using SQL-based searches, pre-built search templates, or custom scripts. Model Search is how architects interrogate the repository programmatically: finding all elements of a type, all relationships of a kind, or all elements with specific tagged values. AI tools reach the same repository content through an MCP server, such as the one in AI Power Tools for Sparx EA. See also: MCP Server, AI Power Tools for Sparx EA.

AI & Integration

AI-Augmented Architecture AI-Augmented Architecture is the way architects should be doing the job on Sparx EA: the architect decides what should be done, and AI does the mechanics in the tool and the model. See also: Architecture Modeling, Large Language Model (LLM).

AI Power Tools for Sparx EA Free tools for every Sparx EA user, published by NovoCircle, that let an AI assistant work in the model and follow an experienced architect’s method while it does. For Claude, the Claude Desktop extension is the MCP server (Claude Desktop chat and the Code tab of the Claude desktop app) and the ai-power-tools plugin from the NovoCircle marketplace carries the skills; you need both. For GitHub Copilot, the VS Code extension gives Copilot the same server, and VS Code loads the same skills. Runs on Windows, with Enterprise Architect on the same machine. See also: Skills library, Model Context Protocol (MCP).

Model Context Protocol (MCP) An open standard, introduced by Anthropic in 2024, that defines how AI agents and large language models connect to external data sources and tools. MCP lets AI tools work with current data, including EA repositories through an MCP server such as Kernaro AI Hub’s or the one in AI Power Tools for Sparx EA, rather than relying only on training data. MCP is becoming the standard integration layer for enterprise AI. See also: MCP Server, AI Power Tools for Sparx EA.

Prolaborate 6 with AI AI in Prolaborate 6, Sparx Systems’ web-based stakeholder portal. It generates descriptions, summarizes discussions and diagrams, rephrases text and answers questions about the model. It is inactive by default and needs a Kernaro AI Hub license and the customer’s own LLM. See also: Prolaborate, Kernaro AI Hub.

GraphQL See Sparx EA Platform section. As an AI & Integration entry: GraphQL’s precise query capability makes it well-suited to BI and analytics use cases where you want to retrieve specific model slices, such as all applications in a domain or all capabilities mapped to a strategic objective, without pulling the entire repository. See also: GraphQL, Semantic Layer.

Large Language Model (LLM) A machine learning model trained on large text corpora that can generate, summarize, classify, and reason over natural language. LLMs are the engine behind tools like Microsoft Copilot, Claude, and ChatGPT. LLMs can reason over EA model content when connected via MCP or similar protocols, but their output quality is directly tied to the quality and structure of the model content. See also: Model Context Protocol (MCP), AI Agent.

AI Agent A software system that uses an LLM to plan and execute multi-step tasks, often calling external tools or data sources as part of its reasoning process. EA-connected AI agents can answer questions like “which capabilities have no supporting applications?” or “what is the impact of decommissioning this system?” by querying the EA model through an MCP server. See also: Large Language Model (LLM), MCP Server.

Retrieval-Augmented Generation (RAG) A technique for improving LLM accuracy by retrieving relevant documents or data at query time and including them in the model’s context window. An MCP server over the EA repository, such as Kernaro AI Hub’s or the one in AI Power Tools for Sparx EA, enables a form of RAG for EA content: the AI retrieves relevant model elements before generating a response. Well-structured, MDG-governed models improve RAG quality. See also: Large Language Model (LLM), MDG Technology.

Semantic Layer An abstraction layer that translates raw data into business-meaningful concepts, metrics, and relationships. In EA, the semantic layer is the metamodel: the definitions that give meaning to model elements. A strong semantic layer makes EA data interpretable by both BI tools and AI tools without additional transformation. See also: Metamodel, GraphQL.

Skills library Written procedures an AI agent follows to do an architecture task the way an experienced architect would. Each skill encodes a method and the human judgment it depends on. The core value of AI Power Tools for Sparx EA is its skills library covering modeling, analysis, governance and stakeholder engagement. See also: AI Power Tools for Sparx EA, AI Agent.

Architecture Intelligence The application of AI capabilities to EA practice: using LLMs and AI agents to query, analyze, synthesize, and extend architecture models. Architecture intelligence gives architects faster access to their own repository content and gives stakeholders self-service access to architecture knowledge. See also: AI Power Tools for Sparx EA, AI Agent.

Digital Thread A connected data trail that links artifacts across the full lifecycle of a system or capability: from requirements through design, implementation, operation, and decommission. The EA repository, when properly integrated, can serve as the backbone of a digital thread. An MCP server over the repository, such as Kernaro AI Hub’s or the one in AI Power Tools for Sparx EA, lets AI tools follow that thread. See also: Architecture Repository, MCP Server.

Ontology A formal representation of knowledge as a set of concepts, definitions, and relationships within a domain. In EA, the metamodel is a form of ontology. Knowledge graph technologies build on ontologies to enable richer reasoning over connected data. MDG Technology is the practical mechanism for defining and governing EA ontologies in Sparx EA. See also: Knowledge Graph, Metamodel, MDG Technology.

Knowledge Graph A structured representation of entities and the relationships between them, designed to support complex querying and reasoning. EA repositories are natural knowledge graphs: elements are entities, relationships are edges, and diagrams are views. An MCP server over the repository gives AI tools access to this graph structure. See also: Ontology, MCP Server.

Governance & Operations

Stakeholder Engagement Connecting the architecture repository to the people who need its answers by surfacing the model in plain language so business and IT partners can use it without modeling expertise. One of the four use cases of AI-Augmented Architecture. See also: Stakeholder Register, Prolaborate.

Outcome-Driven Architecture An operating model for an AI-augmented practice in which governance is encoded in the model and the practice is measured by the outcomes it produces (review cycle time, repository health, reuse) rather than the hours it logs. See also: Architecture Governance, Architecture Maturity.

Architecture Review Board (ARB) The governance body responsible for reviewing, approving, and maintaining architecture decisions within an organization. An effective ARB is lightweight enough to move at program pace but rigorous enough to prevent architectural drift. It should be chaired by a senior architect with executive sponsorship. See also: Architecture Governance, EA Center of Excellence (CoE).

Architecture Governance The framework of processes, roles, and standards by which architecture decisions are made, communicated, and enforced. Governance is what prevents EA from becoming a library of ignored diagrams. It includes the ARB, architecture principles, decision records, and compliance checkpoints in delivery processes. See also: Architecture Review Board (ARB), Architecture Principles.

EA Center of Excellence (CoE) A dedicated team or function responsible for setting EA standards, developing capability, managing the architecture repository, and supporting delivery teams. A CoE is the organizational home of the EA practice. Its size ranges from two people in a mid-sized organization to dozens in a large enterprise. See also: Architecture Governance, EA Program.

Architecture Maturity A measure of how systematically and effectively an organization practices enterprise architecture. Maturity models (such as TOGAF’s Architecture Capability Framework or Gartner’s EA Maturity Model) assess dimensions including governance, tooling, stakeholder engagement, and business alignment. Maturity assessment is a useful starting point for an EA program. See also: EA Program, EA Center of Excellence (CoE).

Technical Debt The accumulated cost of shortcuts, legacy decisions, and deferred refactoring in an organization’s technology estate. Technical debt is sometimes a deliberate trade-off, but it must be made visible and managed. EA is the discipline that makes technical debt visible at enterprise scale. See also: Architecture Runway, Decommission.

EA Program The organizational program that establishes, runs, and matures the EA practice. An EA program is distinct from individual architecture projects: it is the sustained investment in the capability of doing architecture well. Without program-level commitment, EA initiatives tend to stall. See also: EA Center of Excellence (CoE), Architecture Maturity.

Stakeholder Register A structured list of all stakeholders relevant to an architecture program: their roles, interests, concerns, and influence. Managing a stakeholder register is basic program management, but EA stakeholder registers should also capture which architecture views and artifacts each stakeholder needs. See also: Stakeholder View, RACI.

RACI A responsibility assignment matrix: Responsible, Accountable, Consulted, Informed. RACI charts clarify who does what in governance processes: who approves a decision, who must be consulted, who is kept informed. In EA governance, RACI applies to architecture reviews, model updates, standard changes, and program decisions. See also: Architecture Governance, Stakeholder Register.

Architecture Roadmap A time-phased plan that shows how the architecture will evolve from its current state to a target state. An architecture roadmap sequences and prioritizes architecture changes, with dependencies and enabling conditions made explicit. It is one of the most important outputs of an EA program. See also: Architecture Runway, Technical Debt.

Decommission The planned retirement of a system, application, or technology component from the estate. Decommissioning is often neglected: organizations add new capabilities but rarely remove old ones. EA provides the dependency analysis and impact assessment that makes safe decommissioning possible. Tracked in Sparx EA as a lifecycle state on application elements. See also: Technical Debt, Architecture Roadmap.