Semantics
type: ContextComponent
type: Intangible
description: Ontologies, business glossaries, metrics definitions, semantic layers, and knowledge graphs.

Semantic layer
type: DefinedTerm
description: A business-friendly semantic abstraction commonly used to start knowledge engineering for BI.

AI-Cautious
type: AIArchetype
type: Intangible
description: A cautious AI ambition posture that the article challenges as not being a strategy.

CommunityPerson
type: Class
description: A person mentioned in the community and analyst sections of the trip report.

European IT leader concern about AI agent cost overruns
type: MetricObservation
type: Observation
description: 6 out of 10 European IT leaders have real concerns about AI agent cost overruns.
unit text: %
value: 60

Data contract
type: DefinedTerm
description: A formal handshake between upstream operational governance and downstream analytical governance.

Privacy
type: PolicyType
type: Intangible
description: Policy type suitable for policy-as-code translation: Privacy.

has archetype
type: Property
type: ObjectProperty
description: Connects the article to AI ambition archetypes.

has policy type
type: Property
type: ObjectProperty
description: Connects governance-as-code to policy categories.

ContextLayer
type: Class
description: A managed enterprise layer that captures semantics, operational state, provenance, policies, users, access, and assets for AI-ready work.

Gartner Data & Analytics London May 2026
type: Event
description: Conference attended by Juan Sequeda and used as the source of the trip-report takeaways.
location: London
start date: 2026-05

What does context include?
type: Question
accepted answer: Answer 2
description: What does context include?

Knowledge graph
type: DefinedTerm
description: A graph of entities and relationships used to represent enterprise context and semantics.

Ethics
type: PolicyType
type: Intangible
description: Policy type suitable for policy-as-code translation: Ethics.

Executable SPARQL recipes
type: ArticleSection
description: Generated executable SPARQL recipes over the companion RDF graph.
has part: List context layer components
has part: Compare vendor strategy options
has part: List expanded governance domains
has part: Rank quantitative observations
is part of: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways

Architecture needs overhaul
type: MetricObservation
type: Observation
description: 90% of D&A leaders say their architecture needs a minor or significant overhaul to support new use cases.
unit text: %
value: 90

Application-provider-centric
type: VendorStrategyOption
type: Intangible
description: Best when data and use-case requirements are fulfilled within the application footprint.
risk: Most organizations use multiple applications, creating cross-application context challenges.

Run live query
type: Action
description: URL-encoded live query action for URIBurner SPARQL.
target: https://linkeddata.uriburner.com/sparql

CSP-centric
type: VendorStrategyOption
type: Intangible
description: Best when the enterprise is already on one cloud and values pre-integrated capabilities and speed over differentiation.
risk: Hyperscaler metadata tools typically scan inside their own ecosystems; multi-cloud buyers still need ISVs.

ISV-centric
type: VendorStrategyOption
type: Intangible
description: Best for best-of-breed depth, cloud neutrality, and high maturity in specific capabilities.
risk: Risk of recreating fragmented stitching and exposure to acquisition by a non-strategic platform vendor.

supports use case
type: Property
type: ObjectProperty
description: Connects a foundation or context layer to a business use case.

Awesome community
type: ArticleSection
description: The conference community section mentions analysts, friends, and data community participants.
is part of: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways

Users, access, and assets
type: ContextComponent
type: Intangible
description: User identity, department or cohort context, access entitlements, and business assets.

Choose an outcome
type: HowToStep
description: Start with a concrete business outcome or use case.

has metric
type: Property
type: ObjectProperty
description: Connects the article to a quantitative observation.

AI-Opportunistic
type: AIArchetype
type: Intangible
description: An AI ambition posture oriented around selective opportunities.

Basics and foundations
type: ArticleSection
description: AI and D&A leaders are still working through foundational issues: data quality, governance effectiveness, and architecture readiness.
is part of: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways

Security
type: PolicyType
type: Intangible
description: Policy type suitable for policy-as-code translation: Security.

has component
type: Property
type: ObjectProperty
description: Connects a context layer to a component.

GovernanceDomain
type: Class
description: A domain of governance that must be coordinated for AI, analytics, risk, data, security, or operational work.

Governance function fully able to lead AI governance
type: MetricObservation
type: Observation
description: Only 13% say their D&A governance function can fully lead AI governance.
unit text: %
value: 13

Pay-as-you-go methodology
type: DefinedTerm
type: Methodology
description: Juan Sequeda's methodology for progressing by use case rather than waterfall foundation programs.

Run live query
type: Action
description: URL-encoded live query action for URIBurner SPARQL.
target: https://linkeddata.uriburner.com/sparql

Sanjeev Mohan
type: Person
type: CommunityPerson
description: Data community participant mentioned as SanjMo.

risk
type: Property
type: ObjectProperty
description: Records a risk called out in the trip report.

ContextComponent
type: Class
description: A component of enterprise context such as semantics, rules, ownership, operational state, provenance, users, access, or assets.

Malcolm Hawker
type: Person
type: CommunityPerson
description: Data community participant mentioned in the community section.

Provenance
type: ContextComponent
type: Intangible
description: Decision traces, process lineage, actions taken, and outcomes produced.

Gartner Data & Analytics London May 2026 takeaways RDF
type: Dataset
description: Dataset containing the generated RDF and representative data for the trip report.
name: Gartner London takeaways RDF dataset
distribution: Turtle distribution
was generated by: RDF and companion document generation

Deliver and govern
type: HowToStep
description: Deliver the use case while building the reusable context and governance foundation.

VendorStrategyOption
type: Class
description: A strategic option for building a converged data management platform.

Daniel Cota
type: Person
type: CommunityPerson
description: Moderator of the strategic data management platform panel.

Analytics governance
type: GovernanceDomain
type: Intangible
description: Governance domain named in the article: Analytics governance.

Lifecycle
type: PolicyType
type: Intangible
description: Policy type suitable for policy-as-code translation: Lifecycle.

Knowledge engineering starts with ontologies and knowledge graphs
type: MetricObservation
type: Observation
description: A live poll found 40% going straight to ontologies and knowledge graphs.
unit text: %
value: 40

ServiceNow
type: Organization
description: Company whose data and analytics innovations were presented and demonstrated at the conference.

What methodology does the article advocate?
type: Question
accepted answer: Answer 4
description: What methodology does the article advocate?

Policy as code frame
type: Intangible
description: A frame for translating definitions, models, quality, security, privacy, ethics, and lifecycle policies into enforceable code.
has policy type: Definitions and models
has policy type: Quality
has policy type: Security
has policy type: Privacy
has policy type: Ethics
has policy type: Lifecycle

URIBurner SPARQL endpoint
type: WebAPI
description: Default live endpoint for generated executable query links.

concerns capability
type: Property
type: ObjectProperty
description: Connects a strategy option or governance item to a capability.

Data governance
type: GovernanceDomain
type: Intangible
description: Governance domain named in the article: Data governance.

Vendor strategy
type: ArticleSection
description: Organizations need explicit strategy across CSP-centric, ISV-centric, and application-provider-centric data management platforms.
is part of: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways
mentions: CSP-centric
mentions: ISV-centric
mentions: Application-provider-centric
mentions: Tiered strategic-critical vendor strategy

Context, context, context
type: ArticleSection
about: Enterprise Context Layer
description: Context layers, semantics, ontologies, knowledge graphs, operational state, provenance, users, access, and assets are core to AI-ready data infrastructure.
is part of: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways

AI-First
type: AIArchetype
type: Intangible
description: An AI ambition posture oriented around systematic AI transformation.

RDF and companion document generation
type: Activity
description: Generation of RDF, HTML, and Markdown artifacts from the article.
used: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways

Decision governance
type: GovernanceDomain
type: Intangible
description: Governance domain named in the article: Decision governance.

Answer 1
type: Answer
description: Enterprise AI value depends on context, governance, data foundations, and use-case-driven methodology.

Governance singularity
type: DefinedTerm
description: The convergence of data, AI, analytics, cybersecurity, MDM, decision, risk, and IT governance.

Apply the iron-thread method
type: HowTo
description: Use outcome-first delivery to build foundations and deliver value together.
step: Choose an outcome
step: Trace the work
step: Identify minimum context
step: Deliver and govern

Identify minimum context
type: HowToStep
description: Model the semantics, provenance, users, access, and policies needed for the outcome.

has vendor strategy
type: Property
type: ObjectProperty
description: Connects the article or organization to a vendor strategy option.

Glossary
type: DefinedTermSet
description: Defined terms for the generated article knowledge graph.
has defined term: Enterprise Context Layer
has defined term: Knowledge graph
has defined term: Ontology
has defined term: Semantic layer
has defined term: Data contract
has defined term: Policy as code
has defined term: Iron-thread approach
has defined term: Governance singularity

Cybersecurity governance
type: GovernanceDomain
type: Intangible
description: Governance domain named in the article: Cybersecurity governance.

Operational state
type: ContextComponent
type: Intangible
description: Entities, activities, and environmental conditions that describe what is happening in the business now.

Rules, policies, and ownership
type: ContextComponent
type: Intangible
description: Business rules, governance policies, and accountability assignments.

Operational governance
type: GovernanceDomain
type: Intangible
description: Governance domain named in the article: Operational governance.

MetricObservation
type: Class
description: A quantitative observation or statistic from the trip report.

FAQ
type: FAQPage
description: Questions and answers derived from the trip report.
main entity: What is the main conference takeaway?
main entity: What does context include?
main entity: Why does governance expand?
main entity: What methodology does the article advocate?

List expanded governance domains
type: SoftwareSourceCode
type: CreativeWork
description: Shows governance domains participating in the governance singularity frame.
is part of: Executable SPARQL recipes
potential action: Run live query
programming language: SPARQL
text: PREFIX sm: <https://juansequeda.substack.com/p/gartner-data-and-analytics-london#> PREFIX schema: <http://schema.org/> SELECT ?domain ?name WHERE { GRAPH <https://linkeddata.uriburner.com/DAV/demos/daas/gartner-data-analytics-london-gpt5-chat-1.ttl> { sm:governanceFrame sm:hasGovernanceDomain ?domain . ?domain schema:name ?name . } } ORDER BY ?name

IT governance
type: GovernanceDomain
type: Intangible
description: Governance domain named in the article: IT governance.

Methodologies
type: ArticleSection
description: The article argues for use-case-driven, pay-as-you-go, iron-thread methodology rather than foundation-first waterfall.
is part of: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways

Rank quantitative observations
type: SoftwareSourceCode
type: CreativeWork
description: Returns article metrics and values as executable representative data.
is part of: Executable SPARQL recipes
potential action: Run live query
programming language: SPARQL
text: PREFIX sm: <https://juansequeda.substack.com/p/gartner-data-and-analytics-london#> PREFIX schema: <http://schema.org/> SELECT ?metric ?name ?value ?unit WHERE { GRAPH <https://linkeddata.uriburner.com/DAV/demos/daas/gartner-data-analytics-london-gpt5-chat-1.ttl> { ?metric a sm:MetricObservation ; schema:name ?name ; schema:value ?value ; schema:unitText ?unit . } } ORDER BY DESC(?value)

Answer 4
type: Answer
description: Start from business outcomes, build minimum foundations for the use case, deliver value, and repeat.

Robert Thanaraj
type: Person
type: CommunityPerson
description: Gartner analyst on the strategic data management platform panel.

Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways
type: TechArticle
type: Article
about: Gartner Data & Analytics London May 2026
about: Enterprise Context Layer
article section: Data and analytics conference takeaways
author: Juan Sequeda
date published: 2026-05-20
description: Juan Sequeda's trip report on Gartner Data & Analytics London covering foundations, context, vendor strategy, governance, methodologies, and community.
has part: Basics and foundations
has part: Context, context, context
has part: Vendor strategy
has part: Governance is also king and queen
has part: Methodologies
has part: Awesome community
has part: Executable SPARQL recipes
has part: Gartner London takeaways catalog
has part: List context layer components
has part: Compare vendor strategy options
has part: List expanded governance domains
has part: Rank quantitative observations
has part: FAQ
has part: Glossary
has part: Apply the iron-thread method
mentions: ServiceNow
mentions: Semantics
mentions: Rules, policies, and ownership
mentions: Operational state
mentions: Provenance
mentions: Users, access, and assets
mentions: Knowledge graph
mentions: Ontology
mentions: Semantic layer
mentions: Data contract
mentions: Policy as code
mentions: Iron-thread approach
mentions: Pay-as-you-go methodology
mentions: Governance singularity
mentions: Policy as code frame
mentions: Mircea Danciulescu
mentions: Andres Garcia-Rodeja
mentions: Ehtisham Zaidi
mentions: Robert Thanaraj
mentions: Roxane Edjlali
mentions: Daniel Cota
mentions: Anurag Raj
mentions: Sanjeev Mohan
mentions: Malcolm Hawker
has archetype: AI-Cautious
has archetype: AI-Opportunistic
has archetype: AI-First
has governance domain: Expanded governance frame
has metric: AI ROI achievement in 2025
has metric: European IT leader concern about AI agent cost overruns
has metric: D&A and AI leader concern about AI agent cost overruns
has metric: Organizations with financial guardrails or AI FinOps
has metric: Highly effective data engineering for AI use cases
has metric: Governance function fully able to lead AI governance
has metric: Architecture needs overhaul
has metric: Knowledge engineering starts with semantic BI layers
has metric: Knowledge engineering starts with ontologies and knowledge graphs
has vendor strategy: CSP-centric
has vendor strategy: ISV-centric
has vendor strategy: Application-provider-centric
has vendor strategy: Tiered strategic-critical vendor strategy

Methodology
type: Class
description: A delivery methodology for building foundations while producing outcome-oriented value.

Anurag Raj
type: Person
type: CommunityPerson
description: Gartner analyst presenting on the future of D&A governance.

PolicyType
type: Class
description: A type of governance policy that may be expressed as code.

Iron-thread approach
type: DefinedTerm
type: Methodology
description: A use-case-first methodology that builds minimum foundations while delivering value end-to-end.

MDM governance
type: GovernanceDomain
type: Intangible
description: Governance domain named in the article: MDM governance.

Gartner London 2026 context and governance ontology
type: Ontology
description: A lightweight ontology and representative data model for Juan Sequeda's Gartner Data & Analytics London May 2026 takeaways.

Organizations with financial guardrails or AI FinOps
type: MetricObservation
type: Observation
description: 47% of European organizations have financial guardrails or AI FinOps practices in place.
unit text: %
value: 47

Governance is also king and queen
type: ArticleSection
description: Governance expands beyond data governance into AI, analytics, cyber, risk, MDM, decision, IT, and operational governance.
is part of: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways

Knowledge engineering starts with semantic BI layers
type: MetricObservation
type: Observation
description: A live poll found 60% starting knowledge engineering with semantic layers for BI.
unit text: %
value: 60

What is the main conference takeaway?
type: Question
accepted answer: Answer 1
description: What is the main conference takeaway?

Corporate risk governance
type: GovernanceDomain
type: Intangible
description: Governance domain named in the article: Corporate risk governance.

Enterprise Context Layer
type: ContextLayer
type: Intangible
description: A platform-independent layer of semantics, rules, ownership, operations, provenance, users, access, and assets that supports AI-ready work.
has component: Semantics
has component: Rules, policies, and ownership
has component: Operational state
has component: Provenance
has component: Users, access, and assets

D&A and AI leader concern about AI agent cost overruns
type: MetricObservation
type: Observation
description: Only 1 out of 10 D&A and AI leaders share AI agent cost-overrun concerns.
unit text: %
value: 10

Why does governance expand?
type: Question
accepted answer: Answer 3
description: Why does governance expand?

Definitions and models
type: PolicyType
type: Intangible
description: Policy type suitable for policy-as-code translation: Definitions and models.

Expanded governance frame
type: Intangible
description: Governance expands beyond data governance into coordinated governance over AI, analytics, cybersecurity, MDM, decision, risk, IT, and operations.
has governance domain: Data governance
has governance domain: AI governance
has governance domain: Analytics governance
has governance domain: Cybersecurity governance
has governance domain: MDM governance
has governance domain: Decision governance
has governance domain: Corporate risk governance
has governance domain: IT governance
has governance domain: Operational governance

Highly effective data engineering for AI use cases
type: MetricObservation
type: Observation
description: Only 26% of D&A or AI leaders think their data engineering practices are highly effective for existing AI use cases.
unit text: %
value: 26

Roxane Edjlali
type: Person
type: CommunityPerson
description: Gartner analyst on the strategic data management platform panel.

Juan Sequeda
type: Person
description: Author of the trip report and data/knowledge graph practitioner.

AI ROI achievement in 2025
type: MetricObservation
type: Observation
description: Only 1 in 5 AI initiatives are achieving ROI in 2025.
unit text: %
value: 20

Gartner London takeaways catalog
type: Catalog
description: DCAT catalog for the generated context, governance, vendor strategy, and methodology knowledge graph.
dataset: Gartner Data & Analytics London May 2026 takeaways RDF

Andres Garcia-Rodeja
type: Person
type: CommunityPerson
description: Presenter of a talk about how to build a context layer.

Compare vendor strategy options
type: SoftwareSourceCode
type: CreativeWork
description: Returns CSP, ISV, application-provider, and tiered vendor strategy options with risks.
is part of: Executable SPARQL recipes
potential action: Run live query
programming language: SPARQL
text: PREFIX sm: <https://juansequeda.substack.com/p/gartner-data-and-analytics-london#> PREFIX schema: <http://schema.org/> SELECT ?strategy ?name ?risk WHERE { GRAPH <https://linkeddata.uriburner.com/DAV/demos/daas/gartner-data-analytics-london-gpt5-chat-1.ttl> { ?strategy a sm:VendorStrategyOption ; schema:name ?name ; sm:risk ?risk . } } ORDER BY ?name

Trace the work
type: HowToStep
description: Follow the data, people, policies, and operational steps involved.

has governance domain
type: Property
type: ObjectProperty
description: Connects a governance frame to a governance domain.

Ehtisham Zaidi
type: Person
type: CommunityPerson
description: Gartner analyst on the strategic data management platform panel.

Run live query
type: Action
description: URL-encoded live query action for URIBurner SPARQL.
target: https://linkeddata.uriburner.com/sparql

Tiered strategic-critical vendor strategy
type: VendorStrategyOption
type: Intangible
description: A medallion-like approach that distinguishes strategic vendors from critical vendors.
risk: Critical vendors may not be strategic, and strategic vendors may not yet be critical.

Policy as code
type: DefinedTerm
description: Governance policies translated into enforceable code.

Run live query
type: Action
description: URL-encoded live query action for URIBurner SPARQL.
target: https://linkeddata.uriburner.com/sparql

Quality
type: PolicyType
type: Intangible
description: Policy type suitable for policy-as-code translation: Quality.

Answer 2
type: Answer
description: Context includes semantics, rules, policies, ownership, operational state, provenance, users, access, and assets.

AI governance
type: GovernanceDomain
type: Intangible
description: Governance domain named in the article: AI governance.

AIArchetype
type: Class
description: A Gartner-style AI ambition archetype.

Turtle distribution
type: Distribution
access URL: https://linkeddata.uriburner.com/DAV/demos/daas/gartner-data-analytics-london-gpt5-chat-1.ttl
media type: text/turtle

List context layer components
type: SoftwareSourceCode
type: CreativeWork
description: Shows the components modeled for the enterprise context layer.
is part of: Executable SPARQL recipes
potential action: Run live query
programming language: SPARQL
text: PREFIX sm: <https://juansequeda.substack.com/p/gartner-data-and-analytics-london#> PREFIX schema: <http://schema.org/> SELECT ?component ?name ?description WHERE { GRAPH <https://linkeddata.uriburner.com/DAV/demos/daas/gartner-data-analytics-london-gpt5-chat-1.ttl> { sm:enterpriseContextLayer sm:hasComponent ?component . ?component schema:name ?name ; schema:description ?description . } } ORDER BY ?name

Ontology
type: DefinedTerm
description: A formal model of classes and relationships used to make context explicit.

recommendation
type: Property
type: ObjectProperty
description: Records a recommendation or takeaway.

Mircea Danciulescu
type: Person
type: CommunityPerson
description: WPP Global Data Manager who joined the ServiceNow conversation.

Answer 3
type: Answer
description: AI blurs data, analytics, cyber, decision, risk, IT, and operational governance boundaries.