About This Assessment
This 15-question assessment measures how ready your Sparx EA practice is to connect to AI tools, BI dashboards, and executive-facing analytics platforms. It scores across five dimensions: repository quality, MDG governance, team capability, stakeholder engagement, and AI infrastructure: and gives you a score from 0 to 45. Your score appears immediately. To receive a detailed diagnostic report with prioritized recommendations, enter your email below the results.
The assessment takes approximately 8 minutes to complete.
Dimension 1: Repository Quality
A high-quality repository is the foundation of useful AI output. AI tools query what exists: if elements are incomplete, inconsistently named, or unconnected, AI responses reflect that.
Question 1: How consistently are elements named and typed in your EA repository?
Select the statement that best describes your current situation.
- (0) We have no naming conventions. Element names are set by individual architects. The same concept appears in multiple forms across the repository.
- (1) We have informal conventions. Some teams follow them. There is noticeable inconsistency across domains or packages.
- (2) We have documented naming standards. Most architects follow them most of the time. There is occasional drift that gets corrected in reviews.
- (3) We have enforced naming standards. Conventions are embedded in MDG stereotypes or validation rules and are checked systematically.
Question 2: How complete is your element property coverage: tagged values, descriptions, and assigned owners?
Select the statement that best describes your current situation.
- (0) Most elements have no descriptions or tagged values. Properties are rarely filled in beyond the element name.
- (1) Some elements are well-described. Coverage is patchy: certain architects or domains are thorough; others are not.
- (2) The majority of elements have core properties completed. Gaps exist but are the exception rather than the rule in most domains.
- (3) Property completion is governed and measured. We track completeness, have mandatory fields enforced through MDG or process, and review gaps regularly.
Question 3: How well does your repository avoid orphan elements and broken relationships?
Select the statement that best describes your current situation.
- (0) We have significant orphan element debt. There are large numbers of elements with no relationships that connect them to anything meaningful.
- (1) We are aware of the problem but haven’t addressed it systematically. Periodic cleanup happens but orphans accumulate between reviews.
- (2) We manage orphan elements as part of regular housekeeping. Reviews happen quarterly or more frequently. The problem is contained.
- (3) We have structural or process controls that prevent orphan accumulation. Validation rules or review gates flag unconnected elements before they compound.
Dimension 2: MDG Governance
MDG Technology: Sparx EA’s metamodel definition framework: is the primary governance mechanism for repository quality. Its configuration directly determines what AI tools see and can query.
Question 4: How mature is your MDG Technology configuration?
Select the statement that best describes your current situation.
- (0) We use Sparx EA with default or minimal MDG configuration. Elements are created using standard built-in types without stereotype governance.
- (1) We have some MDG profiles in place. Stereotypes exist for some domains but coverage is partial and governance is inconsistent.
- (2) We have MDG profiles covering most of our architecture domains. Stereotypes are defined, published, and used by the team. Some gaps remain.
- (3) We have a comprehensive, actively maintained MDG configuration. Profiles cover all domains, stereotypes are versioned, and the team has a change process for MDG updates.
Question 5: How consistently do architects follow MDG stereotypes when creating elements?
Select the statement that best describes your current situation.
- (0) MDG stereotypes are rarely used. Architects create elements without applying stereotypes, or use them inconsistently.
- (1) Some architects use stereotypes consistently; others do not. Compliance depends on individual discipline rather than a shared standard.
- (2) Most architects use the correct stereotypes most of the time. Peer review and onboarding cover stereotype discipline. Occasional drift is corrected.
- (3) Stereotype use is effectively mandatory. Either validation rules enforce it, or the team culture makes non-conformance visible and corrected immediately.
Question 6: How regularly is your MDG validated and updated as architecture standards evolve?
Select the statement that best describes your current situation.
- (0) Our MDG configuration has not been reviewed since it was set up. Changes to architecture standards are not reflected in MDG.
- (1) MDG is updated occasionally. Typically when a specific problem is noticed. There is no scheduled review cycle.
- (2) MDG is reviewed periodically. There is a loose cycle: perhaps annually: and significant standard changes are reflected relatively quickly.
- (3) MDG governance is an active practice. Updates are version-controlled, there is a formal change process, and architecture standard changes trigger MDG review automatically.
Dimension 3: Team Capability
The capability of your EA team determines the ceiling on repository quality: and therefore on what AI tools can do with your data.
Question 7: How familiar is your EA team with ArchiMate or another formal architecture notation?
Select the statement that best describes your current situation.
- (0) Our team uses free-form modeling without a formal notation. Diagrams are created intuitively. There is no shared language across architects.
- (1) Some team members are familiar with ArchiMate or a formal notation. It is used selectively. Most work is not notation-governed.
- (2) The majority of the team models in a consistent notation. Not every architect is certified, but there is a shared modeling language that is consistently applied.
- (3) The team has strong formal notation capability. ArchiMate (or an equivalent) is the de facto standard. New architects are onboarded to it. Model reviews enforce it.
Question 8: How comfortable is the team with Sparx EA scripting or automation: JScript, LUA, or the Sparx automation API?
Select the statement that best describes your current situation.
- (0) No one on the team scripts or automates Sparx EA tasks. All work is done manually through the UI.
- (1) One or two individuals have scripted for specific tasks. Scripts exist but are not maintained or shared systematically.
- (2) The team uses scripts and automation for some repeatable tasks. There is a script library and some automation of bulk operations or reporting.
- (3) Scripting and automation are a normal part of how the team works. The team maintains an automation library, uses the Sparx API confidently, and automates governance checks.
Question 9: How strong is the team’s repository discipline: version control, package management, baseline use, and access control?
Select the statement that best describes your current situation.
- (0) Repository discipline is minimal. There are no baselines, version control is not used, and access is uncontrolled.
- (1) Some disciplines are in place. Access control exists but version control and baselines are rarely used.
- (2) Core disciplines are established. Baselines are taken at significant milestones, access control is configured, and the team understands package ownership.
- (3) Repository discipline is mature. Baselines, access controls, package ownership, and version management are all actively practiced and auditable.
Dimension 4: Stakeholder Engagement
AI augmentation delivers most value when it puts architecture insight in front of decision-makers. This dimension measures how well-positioned your practice is to benefit from that.
Question 10: How accessible are your architecture outputs to non-architect stakeholders today?
Select the statement that best describes your current situation.
- (0) Stakeholders have no direct access to architecture outputs. All content is mediated by the architecture team through presentations or documents.
- (1) Some outputs are shared externally. Exported diagrams or documents are shared on demand. Stakeholders do not have live access.
- (2) Stakeholders can access architecture views through a portal or published output. There is some self-service, but it is limited and not current in real time.
- (3) Stakeholders have meaningful live access. A dashboard, portal, or platform gives decision-makers current architecture insight without requiring architect intervention.
Question 11: How regularly do business leaders actually consume architecture insights?
Select the statement that best describes your current situation.
- (0) Business leaders rarely see architecture outputs. EA is largely invisible to the business.
- (1) Architecture is presented to leadership occasionally. Typically at project gates or specific events. There is no regular cadence.
- (2) There is a regular cadence of architecture insight to leadership. Quarterly reports, portfolio reviews, or ARB outputs reach senior stakeholders.
- (3) Business leaders actively consume architecture insight. Leadership regularly asks architecture questions and uses EA outputs to inform decisions.
Question 12: How well does architecture influence investment and project decisions?
Select the statement that best describes your current situation.
- (0) Architecture has no formal role in investment or project decisions. Projects proceed without architecture input.
- (1) Architecture provides input occasionally. The team is consulted on some significant decisions but not systematically.
- (2) Architecture is a standard input to investment governance. ARB or similar process is in place. Architecture review is part of project approval.
- (3) Architecture is a core input to strategic investment decisions. Portfolio rationalisation, cloud migration, and technology strategy are actively architecture-informed.
Dimension 5: AI Infrastructure
This dimension measures how far along you are in deploying the specific technology layer that connects Sparx EA to AI tools, BI platforms, and executive dashboards.
Question 13: Is EA GraphLink deployed in your environment, or actively being planned?
Select the statement that best describes your current situation.
- (0) We have not heard of EA GraphLink, or have not considered it. There is no current plan to deploy it.
- (1) We are aware of EA GraphLink and have investigated it. There is interest but no active deployment plan.
- (2) EA GraphLink is in our roadmap with a defined plan. Procurement or scoping discussions are underway.
- (3) EA GraphLink is deployed. We have the connectivity layer in place (whether or not downstream integrations are complete).
Question 14: Do you have a working BI integration: Power BI or Tableau: connected to your EA repository?
Select the statement that best describes your current situation.
- (0) No BI integration exists. Architecture data is not available in any BI tool.
- (1) We have explored this but not implemented it. There may be a proof of concept but nothing in production.
- (2) We have a partial BI integration. Some architecture data surfaces in BI dashboards, but coverage is limited and not automated.
- (3) We have a working, maintained BI integration. EA data flows into Power BI or Tableau on a regular refresh cycle and is used for portfolio reporting.
Question 15: Have you piloted any MCP-connected AI tools: such as Microsoft Copilot or Kernaro AI Hub: with your EA data?
Select the statement that best describes your current situation.
- (0) No. We have not piloted any MCP-connected AI tools with EA data.
- (1) We have explored this at a conceptual level. No technical pilot has been run.
- (2) We have run a limited technical pilot. An MCP connection has been tested, but it is not in regular use.
- (3) We have an active MCP-connected AI tool using EA data. Architects or stakeholders are using an AI assistant that queries live EA repository data.
Your Score and What It Means
Add up the points from each of your 15 answers. Your total falls between 0 and 45.
0 – 15: Early Stage
Your EA practice has the foundations in place but needs structural investment before AI augmentation will deliver meaningful value. At this stage, connecting AI tools to your repository would surface incomplete, inconsistently governed data: producing results that undermine confidence rather than build it.
What to do next: The priority is foundational work: establishing repository discipline, deploying the platform correctly, and putting initial MDG governance in place. The recommended path is Discover (to assess exactly where the gaps are and prioritize them) followed by Deploy and/or Amplify to close them. AI integration becomes the right conversation once the foundation is solid.
Start here: sparxservices.com/discover
16 – 25: Developing
Your practice has real capability but uneven governance. Some domains are well-managed; others are not. You have begun thinking about AI augmentation but the repository quality and MDG discipline are not yet consistently strong enough to support it end-to-end.
What to do next: The highest-value investment at this stage is governance depth: building the MDG configuration, naming standards, and completeness disciplines that make AI queries reliable. The recommended path is Amplify (MDG governance and repository standards) followed by Connect once the quality baseline is established. A targeted Discover engagement can help you confirm which governance gaps are most material.
Start here: sparxservices.com/amplify
26 – 35: Capable
Your practice is well-run. You have governance discipline, reasonable repository quality, and at least some stakeholder engagement. You are ready: or close to ready: to integrate AI tools, BI dashboards, and real-time analytics platforms.
What to do next: The recommended path is Connect: deploying EA GraphLink, configuring the GraphQL and MCP interfaces, and connecting your architecture data to Power BI, Copilot, or Kernaro AI Hub. If there are specific governance gaps flagged by your score, an Amplify workstream can run alongside Connect to address them in parallel.
Start here: sparxservices.com/connect
36 – 45: Advanced
Your EA practice is genuinely advanced. Repository quality is high, MDG governance is mature, your team is capable, and you likely have some AI or BI integration already in place. You are well past the foundational questions and into the territory of extracting maximum value from AI augmentation.
What to do next: At this level, the conversation shifts to depth and scale. The recommended path is Connect at the advanced tier: full EA GraphLink deployment across all integration surfaces, Kernaro AI Hub configuration for stakeholder self-service, and Copilot/MCP integration. Pair this with Amplify for AI-automated validation and advanced MDG pattern development. This is where architecture becomes a live data asset for the whole organization.
Start here: sparxservices.com/connect
Get Your Detailed Report
Your score gives you the band. The detailed report gives you the specifics: a dimension-by-dimension breakdown, the three highest-priority actions for your score band, and a recommended engagement path with indicative timelines and investment.
Enter your email to receive the full diagnostic report.
Sparx Services: Enterprise Architecture Platform Specialists sparxservices.com/contact