AI Agent Cost Overrun Concern Gap
type: Statistic
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
supports thesis: AI ROI Crisis

Choose your CDMP strategy: CSP, ISV, or App-centric
type: HowToStep
position: 3
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.

Operational State
type: Context Component
description: Entities, activities, and environmental conditions — what is actually happening in the business right now, not after the fact.

Andres Garcia-Rodeja
type: Person
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

Vendor Strategy
type: Conference Takeaway
description: CDMP options: CSP-centric, ISV-centric, App-centric; Strategic vs Critical vendor tier framework; metadata ownership as the next frontier
has part: CSP-Centric CDMP
has part: ISV-Centric CDMP
has part: App-Centric CDMP
position: 3
has insight: Strategic vs Critical Vendor Tiers
has insight: Metadata Ownership as the Next Frontier

AI Governance
type: Governance Domain

ServiceNow
type: Organization
description: Enterprise software company employing Juan Sequeda; presented data & analytics innovations at Gartner London 2026

How should CDOs and CIOs be working together on AI?
type: Question

Run the 'day in the life of a piece of data' exercise
type: HowToStep
position: 5
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.

Methodologies
type: Conference Takeaway
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
position: 5
has insight: Iron-Thread: Value and Foundations Together
has insight: Day in the Life of Data
has insight: Foundation-First Waterfall Anti-Pattern

Anurag Raj
type: Person
description: Speaker at Gartner London 2026; deep-dive on governance singularity, policy as code, and the expanding scope of D&A governance

Samir Sharma
type: Person
description: Data community figure; attended Gartner London 2026

Mircea Danciulescu
type: Person
description: WPP Global Data Manager; co-presenter with Juan Sequeda at Gartner London 2026
job title: Global Data Manager
works for: WPP

Derek Birdsong
type: Person
description: Data community figure; attended Gartner London 2026

Guido De Simoni
type: Person
description: Data community figure; attended Gartner London 2026, photographed with Mark Beyer

Capsenta
type: Organization
description: Juan Sequeda's previous startup; awarded Gartner Cool Vendor in Data Integration 2016

Converged Data Management Platform (CDMP)
type: Concept
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.

Data Journey Mapping
type: CreativeWork
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.

Iron-Thread / Pay-As-You-Go Methodology
type: Methodology
alternate name: Pay-As-You-Go Methodology
author: Juan Sequeda
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: Outcome-First Approach

a6
type: Answer
parent item: How should CDOs and CIOs be working together on AI?
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.

supports thesis
type: ObjectProperty
comment: Connects a statistic or evidence entity to the claim or thesis it empirically supports

Conference Takeaway
type: Class
comment: A themed section of insights distilled from a conference and published as a trip report

Knowledge Engineering Goes Mainstream
type: CreativeWork
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.'

Gartner
type: Organization
description: Global technology research and advisory firm; organizer of the Data & Analytics conference series; source of statistics and archetypes cited in the article

Knowledge Engineering Starting Point Poll
type: Statistic
value: Live poll: 60% starting with semantic layers for BI; 40% going straight to ontologies and knowledge graphs
supports thesis: Knowledge Engineering Goes Mainstream

Iron-Thread: Value and Foundations Together
type: CreativeWork
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.

has insight
type: ObjectProperty
comment: Connects a conference takeaway section to a key insight entity it contains

Iron-Thread Methodology
type: Concept
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.

AI ROI Rate 2025
type: Statistic
value: Only 1 in 5 AI initiatives are achieving ROI in 2025 — 4 out of 5 AI projects are not delivering
supports thesis: AI ROI Crisis

Roxane Edjlali
type: Person
description: Gartner analyst; highlighted that application vendors already hold their domain semantics better than hyperscalers or ISVs
works for: Gartner

AI FinOps Adoption Rate
type: Statistic
value: 47% of European organizations have already put financial guardrails or AI FinOps practices in place

Context Component
type: Class
comment: One of the structural components of a context layer: semantics, operational state, or provenance

a2
type: Answer
parent item: What should a context layer actually include?
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.

Start with a business outcome, not a technology
type: HowToStep
position: 1
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.

Use deterministic semantics for high-reliability AI use cases
type: HowToStep
position: 7
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.

App-Centric CDMP
type: Vendor Strategy Option
description: Application provider-centric: app vendors already hold their domain's semantics better than anyone else. Challenge: organizations use multiple applications across the enterprise.

Context, Context, Context
type: Conference Takeaway
description: Context layers as mainstream AI infrastructure; semantics/ontologies/KGs are no longer niche; knowledge engineering as emerging practice; you can't buy context
has part: Semantics
has part: Operational State
has part: Provenance
position: 2
has insight: What Is Context?
has insight: Knowledge Engineering Goes Mainstream
has insight: You Cannot Buy Context
has insight: Deterministic Semantics for Trust

Juan Sequeda
type: Person
description: Data & AI practitioner, researcher, Substack author; presented ServiceNow innovations at Gartner London 2026; created the iron-thread/pay-as-you-go methodology
job title: Principal Scientist
works for: ServiceNow

London
type: City
contained in place: United Kingdom

Cybersecurity Governance
type: Governance Domain

You Cannot Buy Context
type: CreativeWork
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.

Data Governance
type: Governance Domain

Foundation-First Waterfall Anti-Pattern
type: CreativeWork
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.

Strategic vs Critical Vendor Tiers
type: CreativeWork
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?

WPP
type: Organization
description: Global communications and marketing services group; employer of Mircea Danciulescu

Corporate Risk Governance
type: Governance Domain

Knowledge Engineering
type: Concept
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.

Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways
type: BlogPosting
about: Gartner Data & Analytics London May 2026
author: Juan Sequeda
date published: 2026-05-20
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.
has part: Basics and Foundations
has part: Context, Context, Context
has part: Vendor Strategy
has part: Governance is King and Queen
has part: Methodologies
has part: Awesome Community
has part: Frequently Asked Questions
has part: Glossary
has part: How to Build AI-Ready Data Foundations (Per Juan Sequeda's Gartner London 2026 Takeaways)
keywords: data governance, AI ROI, context layer, knowledge graph, vendor strategy, data management, Gartner, knowledge engineering, semantic layer, governance singularity
publisher: Substack
was generated by: kg-generator skill

Match your knowledge engineering starting point to your background
type: HowToStep
position: 6
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.

Robert Thanaraj
type: Person
description: Gartner analyst on vendor strategy panel; flagged ISV acquisition risks and fragmentation dangers
works for: Gartner

Knowledge Graph
type: Concept
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.

Data Contracts as Bridge
type: CreativeWork
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.

Day in the Life of a Piece of Data
type: Methodology
author: Juan Sequeda
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: Data Journey Mapping

What should a context layer actually include?
type: Question

a3
type: Answer
parent item: What is the governance singularity?
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.

Bridge operational and analytical governance with data contracts
type: HowToStep
position: 4
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.

Governance Singularity
type: CreativeWork
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.

CSP-Centric CDMP
type: Vendor Strategy Option
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.

AI FinOps
type: Concept
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.

Governance and Architecture Readiness
type: Statistic
value: Only 13% say D&A governance can fully lead AI governance; 90% of D&A leaders say architecture needs minor or significant overhaul
supports thesis: AI ROI Crisis

Methodology
type: Class
comment: A practitioner methodology for data, analytics, or AI implementation work

What is the governance singularity?
type: Question

Policy as Code
type: Concept
definition: Translating governance policies (definitions, quality, security, privacy, ethics, lifecycle) into enforceable executable code rather than documentation. Six policy types per Anurag Raj.

Provenance
type: Concept
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.

Decision Governance
type: Governance Domain

Vendor Strategy Option
type: Class
comment: One of the three main approaches to building a Converged Data Management Platform

a1
type: Answer
parent item: Why are 4 out of 5 AI initiatives failing to achieve ROI?
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.

Analytics Governance
type: Governance Domain

MDM Governance
type: Governance Domain

AI-First
type: AI Archetype
description: Gartner archetype: AI embedded as a core organizational operating principle across all functions.

Policy as Code
type: CreativeWork
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?

Provenance
type: Context Component
description: Tracking of data, the processes it went through, decisions made, actions taken, outcomes that resulted. The who/what/when/why decision traces.

Context Layer
type: Concept
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.'

Deterministic Semantics for Trust
type: CreativeWork
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.

IT Governance
type: Governance Domain

kg-generator skill
type: SoftwareApplication
description: OpenLink Software skill for generating RDF knowledge graphs from web content using the Business & Market Analysis template

Ehtisham Zaidi
type: Person
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
works for: Gartner

CIO-CDO Alignment Gap
type: CreativeWork
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.

a4
type: Answer
parent item: What is the iron-thread / pay-as-you-go methodology?
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.

Glossary
type: DefinedTermSet
about: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways
has part: Context Layer
has part: Converged Data Management Platform (CDMP)
has part: Iron-Thread Methodology
has part: Governance Singularity
has part: Data Contract
has part: Policy as Code
has part: Knowledge Engineering
has part: Semantic Layer
has part: AI FinOps
has part: Provenance
has part: AI Archetype
has part: Knowledge Graph

Semantic Layer
type: Concept
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.

United Kingdom
type: Country

Statistic
type: Class
comment: A quantitative data point cited from Gartner research or live conference polling

ISV-Centric CDMP
type: Vendor Strategy Option
description: Independent Software Vendor-centric: best-of-breed depth and cloud neutrality. Risks: acquisition instability and re-creating fragmented best-of-breed stitching problems.

Metadata Ownership as the Next Frontier
type: CreativeWork
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.

Governance is King and Queen
type: Conference Takeaway
description: Governance singularity; operational governance already happening but unrecognized; data contracts bridging upstream and downstream; policy as code
position: 4
has insight: Governance Singularity
has insight: Operational Governance Already Exists
has insight: Data Contracts as Bridge
has insight: Policy as Code

Perennial Year of the Foundation
type: CreativeWork
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.

Day in the Life of Data
type: CreativeWork
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.

AI Archetype
type: Concept
definition: Gartner's classification of organizational AI postures: AI-Cautious, AI-Opportunistic, AI-First. Juan challenges AI-Cautious as a legitimate strategic posture.

Outcome-First Approach
type: CreativeWork
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.

Awesome Community
type: Conference Takeaway
description: Juan's ~10th Gartner D&A conference; long-running analyst relationships since 2016 Capsenta Cool Vendor; Honest No-BS dinner group
mentions: Mark Beyer
mentions: Ehtisham Zaidi
mentions: Sanjeev Mohan
mentions: Ole Olesen-Bagneux
mentions: Samir Sharma
mentions: Malcolm Hawker
mentions: Guido De Simoni
mentions: Derek Birdsong
position: 6

Audit the context that already exists in your organization
type: HowToStep
position: 2
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.

What is the iron-thread / pay-as-you-go methodology?
type: Question

Operational Governance Already Exists
type: CreativeWork
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.

Gartner Data & Analytics London May 2026
type: Event
description: Major biannual Gartner conference for data and analytics leaders; Juan's approximately 10th Gartner D&A conference
location: London
organizer: Gartner
start date: 2026-05
subject of: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways

prescribes
type: ObjectProperty
comment: Connects a methodology to the specific practice or action it recommends

Governance Domain
type: Class
comment: A domain within the expanded enterprise governance scope converging under AI pressure

Mark Beyer
type: Person
description: Gartner analyst; known to Juan since 2016; attended community dinner
works for: Gartner

Governance Singularity
type: Concept
definition: AI-driven convergence of data, AI, analytics, cybersecurity, MDM, decision, corporate risk, and IT governance into a single expanding scope.

How to Build AI-Ready Data Foundations (Per Juan Sequeda's Gartner London 2026 Takeaways)
type: HowTo
about: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways
step: Start with a business outcome, not a technology
step: Audit the context that already exists in your organization
step: Choose your CDMP strategy: CSP, ISV, or App-centric
step: Bridge operational and analytical governance with data contracts
step: Run the 'day in the life of a piece of data' exercise
step: Match your knowledge engineering starting point to your background
step: Use deterministic semantics for high-reliability AI use cases

Substack
type: Organization
description: Newsletter publishing platform hosting Juan Sequeda's publication

Gartner D&A London 2026 – Juan Sequeda Takeaways KG
type: Ontology
created: 2026-05-20
source: https://juansequeda.substack.com/p/gartner-data-and-analytics-london
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.
version info: 1.0

What Is Context?
type: CreativeWork
description: Context = Semantics (ontologies, glossaries, metrics) + Operational State (entities, activities, conditions) + Provenance (decisions, data traces). Juan extends to also include Users, Access, and Assets.

Data Engineering Effectiveness
type: Statistic
value: Only 26% of D&A or AI leaders think their data engineering practices are highly effective for existing AI use cases
supports thesis: AI ROI Crisis

Frequently Asked Questions
type: FAQPage
about: Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways
has part: Why are 4 out of 5 AI initiatives failing to achieve ROI?
has part: a1
has part: What should a context layer actually include?
has part: a2
has part: What is the governance singularity?
has part: a3
has part: What is the iron-thread / pay-as-you-go methodology?
has part: a4
has part: When should I choose CSP-centric vs ISV-centric vs App-centric CDMP?
has part: a5
has part: How should CDOs and CIOs be working together on AI?
has part: a6
has part: Should my organization adopt an AI-Cautious posture?
has part: a7

Malcolm Hawker
type: Person
description: Data community leader; part of the Honest No-BS dinner group

Semantics
type: Context Component
description: Ontologies, business glossaries, metrics definitions — the foundational semantic layer. Where data catalogs and metadata tools have traditionally focused.

a5
type: Answer
parent item: When should I choose CSP-centric vs ISV-centric vs App-centric CDMP?
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?

AI-Cautious
type: AI Archetype
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.

When should I choose CSP-centric vs ISV-centric vs App-centric CDMP?
type: Question

AI ROI Crisis
type: CreativeWork
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.

a7
type: Answer
parent item: Should my organization adopt an AI-Cautious posture?
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.

Basics and Foundations
type: Conference Takeaway
description: AI ROI crisis (1 in 5 initiatives delivering), readiness gaps, C-suite alignment needs, AI archetypes; the perennial 'year of the foundation'
has part: AI ROI Rate 2025
has part: AI Agent Cost Overrun Concern Gap
has part: AI FinOps Adoption Rate
has part: Data Engineering Effectiveness
has part: Governance and Architecture Readiness
has part: AI-Cautious
has part: AI-Opportunistic
has part: AI-First
position: 1
has insight: AI ROI Crisis
has insight: CIO-CDO Alignment Gap
has insight: Perennial Year of the Foundation

Should my organization adopt an AI-Cautious posture?
type: Question

Why are 4 out of 5 AI initiatives failing to achieve ROI?
type: Question

Sanjeev Mohan
type: Person
alternate name: SanjMo
description: Independent data community thought leader and analyst

Ole Olesen-Bagneux
type: Person
description: Data community figure; attended Gartner London 2026

Data Contract
type: Concept
definition: A formal agreement between data producers and consumers specifying schema, lineage, SLAs, and quality metrics. Bridges upstream operational governance and downstream analytical governance.

AI-Opportunistic
type: AI Archetype
description: Gartner archetype: organizations pursuing AI selectively where clear opportunities arise without full commitment.

Daniel Cota
type: Person
description: Gartner moderator of the vendor strategy panel
works for: Gartner

AI Archetype
type: Class
comment: A Gartner-defined classification of organizational posture toward AI adoption