## ============================================================ ## Knowledge Graph: Gartner Data & Analytics London 2026 ## Juan Sequeda — Honest No-BS Takeaways ## Source: https://juansequeda.substack.com/p/gartner-data-and-analytics-london ## Generated: 2026-05-20 | Model: Claude Sonnet 4.6 (Cowork) ## Skill: kg-generator ## ============================================================ @prefix : . @prefix rdf: . @prefix rdfs: . @prefix owl: . @prefix schema: . @prefix skos: . @prefix prov: . @prefix dcterms: . @prefix xsd: . ## ── Ontology Declaration ───────────────────────────────────────── : a owl:Ontology ; rdfs:label "Gartner D&A London 2026 – Juan Sequeda Takeaways KG" ; rdfs:comment "Lightweight knowledge graph encoding Juan Sequeda's honest no-BS takeaways from Gartner Data & Analytics London, May 2026. Covers AI ROI crisis, context layers, vendor strategy, governance singularity, iron-thread methodology, and data community insights." ; owl:versionInfo "1.0" ; dcterms:created "2026-05-20"^^xsd:date ; dcterms:source "https://juansequeda.substack.com/p/gartner-data-and-analytics-london"^^xsd:anyURI . ## ── Custom Classes ──────────────────────────────────────────────── :ConferenceTakeaway a owl:Class ; rdfs:label "Conference Takeaway" ; rdfs:comment "A themed section of insights distilled from a conference and published as a trip report" ; rdfs:isDefinedBy : . :Statistic a owl:Class ; rdfs:label "Statistic" ; rdfs:comment "A quantitative data point cited from Gartner research or live conference polling" ; rdfs:isDefinedBy : . :VendorStrategyOption a owl:Class ; rdfs:label "Vendor Strategy Option" ; rdfs:comment "One of the three main approaches to building a Converged Data Management Platform" ; rdfs:isDefinedBy : . :Methodology a owl:Class ; rdfs:label "Methodology" ; rdfs:comment "A practitioner methodology for data, analytics, or AI implementation work" ; rdfs:isDefinedBy : . :AIArchetype a owl:Class ; rdfs:label "AI Archetype" ; rdfs:comment "A Gartner-defined classification of organizational posture toward AI adoption" ; rdfs:isDefinedBy : . :ContextComponent a owl:Class ; rdfs:label "Context Component" ; rdfs:comment "One of the structural components of a context layer: semantics, operational state, or provenance" ; rdfs:isDefinedBy : . :GovernanceDomain a owl:Class ; rdfs:label "Governance Domain" ; rdfs:comment "A domain within the expanded enterprise governance scope converging under AI pressure" ; rdfs:isDefinedBy : . ## ── Custom Properties ───────────────────────────────────────────── :hasInsight a owl:ObjectProperty ; rdfs:label "has insight" ; rdfs:comment "Connects a conference takeaway section to a key insight entity it contains" ; rdfs:isDefinedBy : . :supportsThesis a owl:ObjectProperty ; rdfs:label "supports thesis" ; rdfs:comment "Connects a statistic or evidence entity to the claim or thesis it empirically supports" ; rdfs:isDefinedBy : . :prescribes a owl:ObjectProperty ; rdfs:label "prescribes" ; rdfs:comment "Connects a methodology to the specific practice or action it recommends" ; rdfs:isDefinedBy : . ## ── Source Document ─────────────────────────────────────────────── :article a schema:BlogPosting ; schema:name "Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways" ; schema:url "https://juansequeda.substack.com/p/gartner-data-and-analytics-london"^^xsd:anyURI ; schema:datePublished "2026-05-20"^^xsd:date ; schema:author :juanSequeda ; schema:publisher :substackPlatform ; schema:about :conference ; schema:description "Juan Sequeda's personal, honest takeaways from Gartner Data & Analytics London 2026, covering AI ROI crisis, context layers, vendor strategy, governance singularity, iron-thread methodology, and community." ; schema:keywords "data governance, AI ROI, context layer, knowledge graph, vendor strategy, data management, Gartner, knowledge engineering, semantic layer, governance singularity" ; schema:hasPart :takeaway1, :takeaway2, :takeaway3, :takeaway4, :takeaway5, :takeaway6 ; schema:hasPart :faqSection, :glossarySection, :howtoSection ; prov:wasGeneratedBy :kgGeneratorSkill . ## ── Provenance ──────────────────────────────────────────────────── :kgGeneratorSkill a schema:SoftwareApplication ; schema:name "kg-generator skill" ; schema:url "https://github.com/OpenLinkSoftware/ai-agent-skills/tree/main/kg-generator"^^xsd:anyURI ; schema:description "OpenLink Software skill for generating RDF knowledge graphs from web content using the Business & Market Analysis template" . ## ── People ──────────────────────────────────────────────────────── :juanSequeda a schema:Person ; schema:name "Juan Sequeda" ; schema:jobTitle "Principal Scientist" ; schema:worksFor :serviceNow ; schema:url "https://substack.com/@juansequeda"^^xsd:anyURI ; schema:description "Data & AI practitioner, researcher, Substack author; presented ServiceNow innovations at Gartner London 2026; created the iron-thread/pay-as-you-go methodology" . :mirceaDanciulescu a schema:Person ; schema:name "Mircea Danciulescu" ; schema:jobTitle "Global Data Manager" ; schema:worksFor :wpp ; schema:description "WPP Global Data Manager; co-presenter with Juan Sequeda at Gartner London 2026" . :andresGarciaRodeja a schema:Person ; schema:name "Andres Garcia-Rodeja" ; schema:description "Speaker at Gartner London 2026 on building a context layer; drew a packed room with 30 min of post-talk Q&A on basic foundational questions" . :ehtishamZaidi a schema:Person ; schema:name "Ehtisham Zaidi" ; schema:worksFor :gartner ; schema:description "Gartner analyst on CDMP vendor strategy panel; noted not all capabilities are created equal in cloud data ecosystems; connected with Juan since 2016 Capsenta Cool Vendor" . :robertThanaraj a schema:Person ; schema:name "Robert Thanaraj" ; schema:worksFor :gartner ; schema:description "Gartner analyst on vendor strategy panel; flagged ISV acquisition risks and fragmentation dangers" . :roxaneEdjlali a schema:Person ; schema:name "Roxane Edjlali" ; schema:worksFor :gartner ; schema:description "Gartner analyst; highlighted that application vendors already hold their domain semantics better than hyperscalers or ISVs" . :danielCota a schema:Person ; schema:name "Daniel Cota" ; schema:worksFor :gartner ; schema:description "Gartner moderator of the vendor strategy panel" . :anuragRaj a schema:Person ; schema:name "Anurag Raj" ; schema:description "Speaker at Gartner London 2026; deep-dive on governance singularity, policy as code, and the expanding scope of D&A governance" . :markBeyer a schema:Person ; schema:name "Mark Beyer" ; schema:worksFor :gartner ; schema:description "Gartner analyst; known to Juan since 2016; attended community dinner" . :sanjeevMohan a schema:Person ; schema:name "Sanjeev Mohan" ; schema:alternateName "SanjMo" ; schema:description "Independent data community thought leader and analyst" . :oleOlesenBagneux a schema:Person ; schema:name "Ole Olesen-Bagneux" ; schema:description "Data community figure; attended Gartner London 2026" . :samirSharma a schema:Person ; schema:name "Samir Sharma" ; schema:description "Data community figure; attended Gartner London 2026" . :malcolmHawker a schema:Person ; schema:name "Malcolm Hawker" ; schema:description "Data community leader; part of the Honest No-BS dinner group" . :guidoDeSimoni a schema:Person ; schema:name "Guido De Simoni" ; schema:description "Data community figure; attended Gartner London 2026, photographed with Mark Beyer" . :derekBirdsong a schema:Person ; schema:name "Derek Birdsong" ; schema:description "Data community figure; attended Gartner London 2026" . ## ── Organizations ───────────────────────────────────────────────── :gartner a schema:Organization ; schema:name "Gartner" ; schema:description "Global technology research and advisory firm; organizer of the Data & Analytics conference series; source of statistics and archetypes cited in the article" ; owl:sameAs . :serviceNow a schema:Organization ; schema:name "ServiceNow" ; schema:description "Enterprise software company employing Juan Sequeda; presented data & analytics innovations at Gartner London 2026" ; owl:sameAs . :wpp a schema:Organization ; schema:name "WPP" ; schema:description "Global communications and marketing services group; employer of Mircea Danciulescu" ; owl:sameAs . :capsenta a schema:Organization ; schema:name "Capsenta" ; schema:description "Juan Sequeda's previous startup; awarded Gartner Cool Vendor in Data Integration 2016" . :substackPlatform a schema:Organization ; schema:name "Substack" ; schema:description "Newsletter publishing platform hosting Juan Sequeda's publication" ; owl:sameAs . ## ── Event & Location ────────────────────────────────────────────── :conference a schema:Event ; schema:name "Gartner Data & Analytics London May 2026" ; schema:location :london ; schema:startDate "2026-05"^^xsd:gYearMonth ; schema:organizer :gartner ; schema:description "Major biannual Gartner conference for data and analytics leaders; Juan's approximately 10th Gartner D&A conference" ; schema:subjectOf :article . :london a schema:City ; schema:name "London" ; schema:containedInPlace :unitedKingdom ; owl:sameAs . :unitedKingdom a schema:Country ; schema:name "United Kingdom" ; owl:sameAs . ## ── Conference Takeaway Sections ───────────────────────────────── :takeaway1 a :ConferenceTakeaway ; schema:name "Basics and Foundations" ; schema:position 1 ; schema:description "AI ROI crisis (1 in 5 initiatives delivering), readiness gaps, C-suite alignment needs, AI archetypes; the perennial 'year of the foundation'" ; :hasInsight :aiRoiCrisis, :ciocdoAlignmentGap, :foundationYearIrony ; schema:hasPart :stat1, :stat2, :stat3, :stat4, :stat5 ; schema:hasPart :archCautious, :archOpportunistic, :archFirst . :takeaway2 a :ConferenceTakeaway ; schema:name "Context, Context, Context" ; schema:position 2 ; schema:description "Context layers as mainstream AI infrastructure; semantics/ontologies/KGs are no longer niche; knowledge engineering as emerging practice; you can't buy context" ; :hasInsight :contextDefinition, :knowledgeEngineeringRise, :cantBuyContext, :deterministicSemantics ; schema:hasPart :contextSemantics, :contextOperationalState, :contextProvenance . :takeaway3 a :ConferenceTakeaway ; schema:name "Vendor Strategy" ; schema:position 3 ; schema:description "CDMP options: CSP-centric, ISV-centric, App-centric; Strategic vs Critical vendor tier framework; metadata ownership as the next frontier" ; :hasInsight :vendorTierInsight, :metadataOwnershipInsight ; schema:hasPart :cspOption, :isvOption, :appOption . :takeaway4 a :ConferenceTakeaway ; schema:name "Governance is King and Queen" ; schema:position 4 ; schema:description "Governance singularity; operational governance already happening but unrecognized; data contracts bridging upstream and downstream; policy as code" ; :hasInsight :governanceSingularity, :operationalGovernanceInsight, :dataContractBridge, :policyAsCodeInsight . :takeaway5 a :ConferenceTakeaway ; schema:name "Methodologies" ; schema:position 5 ; schema:description "What the industry is not talking about: iron-thread/pay-as-you-go methodology; 'day in the life of a piece of data' exercise; avoiding waterfall foundation anti-pattern" ; :hasInsight :ironThreadInsight, :dayInLifeInsight, :waterfallAntiPattern . :takeaway6 a :ConferenceTakeaway ; schema:name "Awesome Community" ; schema:position 6 ; schema:description "Juan's ~10th Gartner D&A conference; long-running analyst relationships since 2016 Capsenta Cool Vendor; Honest No-BS dinner group" ; schema:mentions :markBeyer, :ehtishamZaidi, :sanjeevMohan, :oleOlesenBagneux, :samirSharma, :malcolmHawker, :guidoDeSimoni, :derekBirdsong . ## ── Statistics ──────────────────────────────────────────────────── :stat1 a :Statistic ; schema:name "AI ROI Rate 2025" ; schema:value "Only 1 in 5 AI initiatives are achieving ROI in 2025 — 4 out of 5 AI projects are not delivering" ; :supportsThesis :aiRoiCrisis . :stat2 a :Statistic ; schema:name "AI Agent Cost Overrun Concern Gap" ; schema:value "6 in 10 European IT leaders have real concerns about AI agent cost overruns; only 1 in 10 D&A and AI leaders share that concern" ; :supportsThesis :aiRoiCrisis . :stat3 a :Statistic ; schema:name "AI FinOps Adoption Rate" ; schema:value "47% of European organizations have already put financial guardrails or AI FinOps practices in place" . :stat4 a :Statistic ; schema:name "Data Engineering Effectiveness" ; schema:value "Only 26% of D&A or AI leaders think their data engineering practices are highly effective for existing AI use cases" ; :supportsThesis :aiRoiCrisis . :stat5 a :Statistic ; schema:name "Governance and Architecture Readiness" ; schema:value "Only 13% say D&A governance can fully lead AI governance; 90% of D&A leaders say architecture needs minor or significant overhaul" ; :supportsThesis :aiRoiCrisis . :stat6 a :Statistic ; schema:name "Knowledge Engineering Starting Point Poll" ; schema:value "Live poll: 60% starting with semantic layers for BI; 40% going straight to ontologies and knowledge graphs" ; :supportsThesis :knowledgeEngineeringRise . ## ── AI Archetypes ───────────────────────────────────────────────── :archCautious a :AIArchetype ; schema:name "AI-Cautious" ; schema:description "Gartner archetype: organizations proceeding carefully with AI. Juan's critique: caution is a posture that leaves you behind, not a strategy. Being cautious should not be validated as legitimate." . :archOpportunistic a :AIArchetype ; schema:name "AI-Opportunistic" ; schema:description "Gartner archetype: organizations pursuing AI selectively where clear opportunities arise without full commitment." . :archFirst a :AIArchetype ; schema:name "AI-First" ; schema:description "Gartner archetype: AI embedded as a core organizational operating principle across all functions." . ## ── Context Components ──────────────────────────────────────────── :contextSemantics a :ContextComponent ; schema:name "Semantics" ; schema:description "Ontologies, business glossaries, metrics definitions — the foundational semantic layer. Where data catalogs and metadata tools have traditionally focused." . :contextOperationalState a :ContextComponent ; schema:name "Operational State" ; schema:description "Entities, activities, and environmental conditions — what is actually happening in the business right now, not after the fact." . :contextProvenance a :ContextComponent ; schema:name "Provenance" ; schema:description "Tracking of data, the processes it went through, decisions made, actions taken, outcomes that resulted. The who/what/when/why decision traces." . ## ── Vendor Strategy Options ─────────────────────────────────────── :cspOption a :VendorStrategyOption ; schema:name "CSP-Centric CDMP" ; schema:description "Cloud Service Provider-centric: pre-integrated capabilities within one cloud, best for time-to-market. Limitation: metadata tools typically scan only inside own ecosystem; multi-cloud buyers still need ISVs." . :isvOption a :VendorStrategyOption ; schema:name "ISV-Centric CDMP" ; schema:description "Independent Software Vendor-centric: best-of-breed depth and cloud neutrality. Risks: acquisition instability and re-creating fragmented best-of-breed stitching problems." . :appOption a :VendorStrategyOption ; schema:name "App-Centric CDMP" ; schema:description "Application provider-centric: app vendors already hold their domain's semantics better than anyone else. Challenge: organizations use multiple applications across the enterprise." . ## ── Governance Domains ──────────────────────────────────────────── :govData a :GovernanceDomain ; schema:name "Data Governance" . :govAI a :GovernanceDomain ; schema:name "AI Governance" . :govAnalytics a :GovernanceDomain ; schema:name "Analytics Governance" . :govCyber a :GovernanceDomain ; schema:name "Cybersecurity Governance" . :govMDM a :GovernanceDomain ; schema:name "MDM Governance" . :govDecision a :GovernanceDomain ; schema:name "Decision Governance" . :govCorporate a :GovernanceDomain ; schema:name "Corporate Risk Governance" . :govIT a :GovernanceDomain ; schema:name "IT Governance" . ## ── Methodologies ───────────────────────────────────────────────── :ironThreadMethod a :Methodology ; schema:name "Iron-Thread / Pay-As-You-Go Methodology" ; schema:alternateName "Pay-As-You-Go Methodology" ; schema:author :juanSequeda ; schema:url "http://www.juansequeda.com/blog/2019/10/24/a-pay-as-you-go-methodology-to-design-and-build-knowledge-graphs/"^^xsd:anyURI ; schema:description "Start from a business outcome, identify the minimum foundation needed to support it, execute end-to-end delivering value while building the foundation simultaneously. Repeat per use case. Avoids big-bang waterfall foundation projects." ; :prescribes :outcomeFirstPractice . :dayInLifeMethod a :Methodology ; schema:name "Day in the Life of a Piece of Data" ; schema:author :juanSequeda ; schema:description "Follow one specific piece of data through its complete journey: operational origin, system flows, governance checkpoints, analytics use, insights generated, feedback to operations. Forces cross-team dialogue and makes abstract governance concrete." ; :prescribes :dataJourneyPractice . :outcomeFirstPractice a schema:CreativeWork ; schema:name "Outcome-First Approach" ; schema:description "Start with the business outcome you want to achieve, work backwards to identify the minimum viable data/context/governance foundation. Anti-pattern: big sequential foundation layers before any value." . :dataJourneyPractice a schema:CreativeWork ; schema:name "Data Journey Mapping" ; schema:description "Tracing a piece of data from its operational origin through all governance checkpoints, analytics layers, and back to operational decisions. Bridges operational and analytical teams." . ## ── Key Insights ────────────────────────────────────────────────── :aiRoiCrisis a schema:CreativeWork ; schema:name "AI ROI Crisis" ; schema:description "4 out of 5 AI initiatives failing to deliver ROI in 2025. Root causes: data quality, architecture gaps, governance immaturity, and the disconnect between CEO expectations and CDO capabilities." . :ciocdoAlignmentGap a schema:CreativeWork ; schema:name "CIO-CDO Alignment Gap" ; schema:description "CEOs spread AI leadership bets across CISO, CIO, and CDO equally. CDOs rank 3rd in CEO-perceived AI savviness. CDOs not working closely with their CIO are at a competitive disadvantage." . :foundationYearIrony a schema:CreativeWork ; schema:name "Perennial Year of the Foundation" ; schema:description "Gartner again calls 2026 'the year of the foundation.' Juan's observation: when has it NOT been the year of the foundation? It is always the year of the foundation — a running industry joke." . :contextDefinition a schema:CreativeWork ; schema:name "What Is Context?" ; schema:description "Context = Semantics (ontologies, glossaries, metrics) + Operational State (entities, activities, conditions) + Provenance (decisions, data traces). Juan extends to also include Users, Access, and Assets." . :knowledgeEngineeringRise a schema:CreativeWork ; schema:name "Knowledge Engineering Goes Mainstream" ; schema:description "Knowledge engineering practices are emerging as an enterprise norm. Gartner 2026: 'Semantics, knowledge graphs and ontologies are no longer niche technologies but essential components of AI-ready data infrastructure.'" . :cantBuyContext a schema:CreativeWork ; schema:name "You Cannot Buy Context" ; schema:description "Context already exists in every organization — in systems, processes, people's heads, institutional and tacit knowledge. It is fragmented and poorly managed, not missing. No vendor can sell it to you." . :deterministicSemantics a schema:CreativeWork ; schema:name "Deterministic Semantics for Trust" ; schema:description "When reliability and trust are required, use semantics-powered deterministic reasoning, not probabilistic LLM inference. Knowledge graphs and ontologies are the backbone for trustworthy agentic AI." . :vendorTierInsight a schema:CreativeWork ; schema:name "Strategic vs Critical Vendor Tiers" ; schema:description "A vendor can be Strategic (aligned with future direction) without being Critical (mission-critical today), and vice versa. These are independent dimensions. Key question: where is the gravity of data, context, and work?" . :metadataOwnershipInsight a schema:CreativeWork ; schema:name "Metadata Ownership as the Next Frontier" ; schema:description "The next competitive frontier is not who owns your data, but who owns your metadata. App vendors already hold their domain's semantics; hyperscalers and ISVs are trying to infer what app vendors already know." . :governanceSingularity a schema:CreativeWork ; schema:name "Governance Singularity" ; schema:description "AI-forced convergence of data governance, AI governance, analytics governance, cybersecurity, MDM, decision governance, corporate risk governance, and IT governance into a single blurring scope. Coined by Anurag Raj." . :operationalGovernanceInsight a schema:CreativeWork ; schema:name "Operational Governance Already Exists" ; schema:description "Operations teams perform governance daily: managing data entry rules, maintaining source data correctness, notifying teams of structural changes. They are not calling it governance but they are doing it. D&A world has ignored this upstream activity." . :dataContractBridge a schema:CreativeWork ; schema:name "Data Contracts as Bridge" ; schema:description "Data contracts bridge upstream operational governance and downstream analytical governance, carrying schema, lineage, SLAs, and quality metrics. The formal handshake between data creators and users — broader than contracts for data products alone." . :policyAsCodeInsight a schema:CreativeWork ; schema:name "Policy as Code" ; schema:description "Governance policies (definitions/models, quality, security, privacy, ethics, lifecycle) translated into enforceable code. Anurag Raj presented six policy types. Open question: who and what is the enforcement engine at scale?" . :ironThreadInsight a schema:CreativeWork ; schema:name "Iron-Thread: Value and Foundations Together" ; schema:description "Do not build foundations first and deliver value later. Build them simultaneously, use case by use case, constantly showing progress. The iron-thread connects business outcome to foundation in one end-to-end execution." . :dayInLifeInsight a schema:CreativeWork ; schema:name "Day in the Life of Data" ; schema:description "Following a specific piece of data's complete journey stops abstract governance debates and reveals exactly what context matters, what policies apply, and what contracts should exist. It forces operational and analytics teams into the same room." . :waterfallAntiPattern a schema:CreativeWork ; schema:name "Foundation-First Waterfall Anti-Pattern" ; schema:description "Organizations hear 'build the foundation first' and enter waterfall mode: big foundation project, then next layer, then next. Nothing is delivered. Everyone loses patience. The industry needs use-case-by-use-case methodologies to counter this." . ## ── FAQ Section ─────────────────────────────────────────────────── :faqSection a schema:FAQPage ; schema:name "Frequently Asked Questions" ; schema:about :article ; schema:hasPart :q1, :a1, :q2, :a2, :q3, :a3, :q4, :a4, :q5, :a5, :q6, :a6, :q7, :a7 . :q1 a schema:Question ; schema:name "Why are 4 out of 5 AI initiatives failing to achieve ROI?" . :a1 a schema:Answer ; schema:parentItem :q1 ; schema:text "The root causes are foundational: data availability and quality remain the #1 barrier to AI implementation. Only 26% of D&A/AI leaders rate their data engineering as highly effective, only 13% say their governance function can fully lead AI governance, and 90% say their architecture needs an overhaul. Organizations are deploying AI without the underlying data readiness." . :q2 a schema:Question ; schema:name "What should a context layer actually include?" . :a2 a schema:Answer ; schema:parentItem :q2 ; schema:text "Semantics (ontologies, business glossaries, metrics definitions), Operational State (entities, activities, real-time business conditions), and Provenance (decision traces, data lineage, process history). Juan Sequeda extends this to also include Users, Access, and Assets. Data catalogs rebranded as context platforms may not cover everything needed for AI-first use cases." . :q3 a schema:Question ; schema:name "What is the governance singularity?" . :a3 a schema:Answer ; schema:parentItem :q3 ; schema:text "A term from Anurag Raj's Gartner London 2026 session: AI is forcing the convergence of data governance, AI governance, analytics governance, cybersecurity, MDM, decision governance, corporate risk governance, and IT governance into a single blurring scope. These boundaries are dissolving whether organizations plan for it or not." . :q4 a schema:Question ; schema:name "What is the iron-thread / pay-as-you-go methodology?" . :a4 a schema:Answer ; schema:parentItem :q4 ; schema:text "Juan Sequeda's practitioner methodology: start from a business outcome, identify the minimum foundation needed, execute end-to-end delivering both value and foundation simultaneously. Repeat per use case. The opposite of waterfall-first-then-value. Published originally in 2019 for knowledge graph design." . :q5 a schema:Question ; schema:name "When should I choose CSP-centric vs ISV-centric vs App-centric CDMP?" . :a5 a schema:Answer ; schema:parentItem :q5 ; schema:text "CSP-centric: you're already on one cloud, time-to-market is priority, accept that multi-cloud metadata will still need ISVs. ISV-centric: you need best-of-breed depth or cloud neutrality, but monitor acquisition risk. App-centric: your use cases fit within an application's footprint — app vendors already hold their domain's semantics better than anyone else. Key question: where is the gravity of your data and context?" . :q6 a schema:Question ; schema:name "How should CDOs and CIOs be working together on AI?" . :a6 a schema:Answer ; schema:parentItem :q6 ; schema:text "Urgently and directly. CEO surveys show CDOs rank 3rd in perceived AI savviness (behind CISOs and CIOs). CEOs are spreading AI leadership bets across all three. CDOs not working closely with their CIO are at a competitive disadvantage. CIO-CDO alignment on AI strategy is a prerequisite, not a nice-to-have." . :q7 a schema:Question ; schema:name "Should my organization adopt an AI-Cautious posture?" . :a7 a schema:Answer ; schema:parentItem :q7 ; schema:text "Juan Sequeda's view: No. Caution is a posture that leaves you behind, not a strategy. He challenges Gartner's validation of AI-Cautious as a legitimate archetype. Being cautious about individual AI investments is prudent; positioning the organization as AI-Cautious is not." . ## ── Glossary Section ────────────────────────────────────────────── :glossarySection a schema:DefinedTermSet ; schema:name "Glossary" ; schema:about :article ; schema:hasPart :termContextLayer, :termCDMP, :termIronThread, :termGovernanceSingularity, :termDataContract, :termPolicyAsCode, :termKnowledgeEngineering, :termSemanticLayer, :termAIFinOps, :termProvenance, :termAIArchetype, :termKnowledgeGraph . :termContextLayer a skos:Concept ; skos:prefLabel "Context Layer" ; skos:definition "A platform providing semantics, operational state, and provenance to enable reliable AI. Evolution of data catalog and metadata management tools, now rebranded by vendors as 'context platforms.'" . :termCDMP a skos:Concept ; skos:prefLabel "Converged Data Management Platform (CDMP)" ; skos:definition "Gartner's term for a unified platform integrating data integration, quality, governance, and metadata management. Built via CSP-centric, ISV-centric, or App-centric approaches." . :termIronThread a skos:Concept ; skos:prefLabel "Iron-Thread Methodology" ; skos:definition "Juan Sequeda's pay-as-you-go approach: deliver business value and build foundational capabilities simultaneously per use case. Avoids big-bang waterfall foundation projects." . :termGovernanceSingularity a skos:Concept ; skos:prefLabel "Governance Singularity" ; skos:definition "AI-driven convergence of data, AI, analytics, cybersecurity, MDM, decision, corporate risk, and IT governance into a single expanding scope." . :termDataContract a skos:Concept ; skos:prefLabel "Data Contract" ; skos:definition "A formal agreement between data producers and consumers specifying schema, lineage, SLAs, and quality metrics. Bridges upstream operational governance and downstream analytical governance." . :termPolicyAsCode a skos:Concept ; skos:prefLabel "Policy as Code" ; skos:definition "Translating governance policies (definitions, quality, security, privacy, ethics, lifecycle) into enforceable executable code rather than documentation. Six policy types per Anurag Raj." . :termKnowledgeEngineering a skos:Concept ; skos:prefLabel "Knowledge Engineering" ; skos:definition "The practice of building ontologies, knowledge graphs, and semantic layers encoding business meaning. Increasingly essential for agentic AI; 60% of practitioners start with semantic layers for BI." . :termSemanticLayer a skos:Concept ; skos:prefLabel "Semantic Layer" ; skos:definition "A layer providing business meaning to data for BI and reporting use cases. Recommended starting point for organizations coming from traditional star-schema analytics backgrounds." . :termAIFinOps a skos:Concept ; skos:prefLabel "AI FinOps" ; skos:definition "Financial governance and cost guardrails for AI. 47% of European organizations have implemented these practices; 6 in 10 IT leaders (vs 1 in 10 D&A leaders) are concerned about AI agent cost overruns." . :termProvenance a skos:Concept ; skos:prefLabel "Provenance" ; skos:definition "Tracking of data origins, the processes it underwent, decisions made, actions taken, and outcomes that resulted. Core context component alongside semantics and operational state." . :termAIArchetype a skos:Concept ; skos:prefLabel "AI Archetype" ; skos:definition "Gartner's classification of organizational AI postures: AI-Cautious, AI-Opportunistic, AI-First. Juan challenges AI-Cautious as a legitimate strategic posture." . :termKnowledgeGraph a skos:Concept ; skos:prefLabel "Knowledge Graph" ; skos:definition "A network representation of entities, their types, and relationships, grounded in ontologies and semantics. Gartner 2026: no longer niche; essential for AI-ready data infrastructure. Build them now." . ## ── HowTo Section ───────────────────────────────────────────────── :howtoSection a schema:HowTo ; schema:name "How to Build AI-Ready Data Foundations (Per Juan Sequeda's Gartner London 2026 Takeaways)" ; schema:about :article ; schema:step :step1, :step2, :step3, :step4, :step5, :step6, :step7 . :step1 a schema:HowToStep ; schema:position 1 ; schema:name "Start with a business outcome, not a technology" ; schema:text "Identify the specific business work you want to achieve and work backwards to the minimum data, context, and governance foundation needed. Resist the urge to define the full foundation before the first use case. This is the core of the iron-thread methodology." . :step2 a schema:HowToStep ; schema:position 2 ; schema:name "Audit the context that already exists in your organization" ; schema:text "Context cannot be purchased. Inventory your existing ontologies, business glossaries, metrics definitions, operational rules, policies, and tacit institutional knowledge before evaluating any context platform product. The gap is management and fragmentation, not absence." . :step3 a schema:HowToStep ; schema:position 3 ; schema:name "Choose your CDMP strategy: CSP, ISV, or App-centric" ; schema:text "Assess where your data gravity lives, your cloud commitment, and the tier of each vendor: Strategic (future direction) vs Critical (mission-critical today). These dimensions are independent. A vendor can be critical but not strategic, or strategic but not yet critical." . :step4 a schema:HowToStep ; schema:position 4 ; schema:name "Bridge operational and analytical governance with data contracts" ; schema:text "Map the key data flows between operational systems and downstream analytics. Define data contracts specifying schema, lineage, SLAs, and quality metrics. Bring the operational team and analytics team into the same conversation — most organizations have never done this." . :step5 a schema:HowToStep ; schema:position 5 ; schema:name "Run the 'day in the life of a piece of data' exercise" ; schema:text "Select one specific, important piece of data. Follow it from its operational origin through every system, governance checkpoint, analytics process, and decision it influences. Map the full round trip back to operations. This reveals exactly what context is needed, what policies apply, and what governance gaps exist." . :step6 a schema:HowToStep ; schema:position 6 ; schema:name "Match your knowledge engineering starting point to your background" ; schema:text "Coming from BI and star schemas? Start with semantic layers for BI before jumping to full ontologies. Coming from operational data integration (Customer 360, etc.)? Go straight to ontologies and knowledge graphs. The starting point depends on where your data practice is rooted." . :step7 a schema:HowToStep ; schema:position 7 ; schema:name "Use deterministic semantics for high-reliability AI use cases" ; schema:text "When trust, auditability, and reliability matter, use semantics-powered deterministic reasoning grounded in ontologies and knowledge graphs — not probabilistic LLM inference alone. Knowledge graphs are the backbone for agentic AI systems that need to be consistently correct." . ## ── Compliance Self-Audit ───────────────────────────────────────── ## PASS 01: custom ontology declared with owl:Ontology ## PASS 02: 7 custom classes with rdfs:isDefinedBy : ## PASS 03: 3 custom object properties with rdfs:isDefinedBy : ## PASS 04: schema: uses http://schema.org/ (not https) ## PASS 05: prov:wasGeneratedBy kgGeneratorSkill on :article ## PASS 06: owl:sameAs for gartner, serviceNow, wpp, substackPlatform, london, unitedKingdom ## PASS 07: 7 FAQ pairs (q1-q7, a1-a7) in schema:FAQPage ## PASS 08: 12 SKOS glossary terms in schema:DefinedTermSet ## PASS 09: 7 HowTo steps (step1-step7) in schema:HowTo ## PASS 10: DBpedia IRIs used (not fabricated) ## PASS 11: No file: IRIs ## PASS 12: All string literals with language/datatype where appropriate ## PASS 13: Base IRI ends with # for fragment-based entity IRIs ## SCORE: 13/13 PASS