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
Choose your CDMP strategy: CSP, ISV, or App-centric
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.
Entities, activities, and environmental conditions — what is actually happening in the business right now, not after the fact.
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
CDMP options: CSP-centric, ISV-centric, App-centric; Strategic vs Critical vendor tier framework; metadata ownership as the next frontier
Enterprise software company employing Juan Sequeda; presented data & analytics innovations at Gartner London 2026
How should CDOs and CIOs be working together on AI?
Run the 'day in the life of a piece of data' exercise
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.
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
Speaker at Gartner London 2026; deep-dive on governance singularity, policy as code, and the expanding scope of D&A governance
Data community figure; attended Gartner London 2026
WPP Global Data Manager; co-presenter with Juan Sequeda at Gartner London 2026
Data community figure; attended Gartner London 2026, photographed with Mark Beyer
Juan Sequeda's previous startup; awarded Gartner Cool Vendor in Data Integration 2016
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.
Converged Data Management Platform (CDMP)
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.
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.
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.
Connects a statistic or evidence entity to the claim or thesis it empirically supports
A themed section of insights distilled from a conference and published as a trip report
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.'
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
Live poll: 60% starting with semantic layers for BI; 40% going straight to ontologies and knowledge graphs
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.
Iron-Thread: Value and Foundations Together
Connects a conference takeaway section to a key insight entity it contains
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.
Only 1 in 5 AI initiatives are achieving ROI in 2025 — 4 out of 5 AI projects are not delivering
Gartner analyst; highlighted that application vendors already hold their domain semantics better than hyperscalers or ISVs
47% of European organizations have already put financial guardrails or AI FinOps practices in place
One of the structural components of a context layer: semantics, operational state, or provenance
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
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
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.
Application provider-centric: app vendors already hold their domain's semantics better than anyone else. Challenge: organizations use multiple applications across the enterprise.
Context layers as mainstream AI infrastructure; semantics/ontologies/KGs are no longer niche; knowledge engineering as emerging practice; you can't buy context
Data & AI practitioner, researcher, Substack author; presented ServiceNow innovations at Gartner London 2026; created the iron-thread/pay-as-you-go methodology
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.
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.
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?
Global communications and marketing services group; employer of Mircea Danciulescu
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.
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.
data governance, AI ROI, context layer, knowledge graph, vendor strategy, data management, Gartner, knowledge engineering, semantic layer, governance singularity
Gartner Data & Analytics London May 2026: My Honest No-BS Takeaways
Match your knowledge engineering starting point to your background
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.
Gartner analyst on vendor strategy panel; flagged ISV acquisition risks and fragmentation dangers
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 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.
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.
What should a context layer actually include?
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
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.
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.
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.
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.
Only 13% say D&A governance can fully lead AI governance; 90% of D&A leaders say architecture needs minor or significant overhaul
A practitioner methodology for data, analytics, or AI implementation work
Translating governance policies (definitions, quality, security, privacy, ethics, lifecycle) into enforceable executable code rather than documentation. Six policy types per Anurag Raj.
Tracking of data origins, the processes it underwent, decisions made, actions taken, and outcomes that resulted. Core context component alongside semantics and operational state.
One of the three main approaches to building a Converged Data Management Platform
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.
Gartner archetype: AI embedded as a core organizational operating principle across all functions.
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?
Tracking of data, the processes it went through, decisions made, actions taken, outcomes that resulted. The who/what/when/why decision traces.
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.'
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.
OpenLink Software skill for generating RDF knowledge graphs from web content using the Business & Market Analysis template
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
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.
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.
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.
A quantitative data point cited from Gartner research or live conference polling
Independent Software Vendor-centric: best-of-breed depth and cloud neutrality. Risks: acquisition instability and re-creating fragmented best-of-breed stitching problems.
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 singularity; operational governance already happening but unrecognized; data contracts bridging upstream and downstream; policy as code
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.
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.
Gartner's classification of organizational AI postures: AI-Cautious, AI-Opportunistic, AI-First. Juan challenges AI-Cautious as a legitimate strategic posture.
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.
Juan's ~10th Gartner D&A conference; long-running analyst relationships since 2016 Capsenta Cool Vendor; Honest No-BS dinner group
Audit the context that already exists in your organization
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?
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.
Major biannual Gartner conference for data and analytics leaders; Juan's approximately 10th Gartner D&A conference
Gartner Data & Analytics London May 2026
Connects a methodology to the specific practice or action it recommends
A domain within the expanded enterprise governance scope converging under AI pressure
Gartner analyst; known to Juan since 2016; attended community dinner
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)
Newsletter publishing platform hosting Juan Sequeda's publication
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.
Gartner D&A London 2026 – Juan Sequeda Takeaways KG
Context = Semantics (ontologies, glossaries, metrics) + Operational State (entities, activities, conditions) + Provenance (decisions, data traces). Juan extends to also include Users, Access, and Assets.
Only 26% of D&A or AI leaders think their data engineering practices are highly effective for existing AI use cases
Data community leader; part of the Honest No-BS dinner group
Ontologies, business glossaries, metrics definitions — the foundational semantic layer. Where data catalogs and metadata tools have traditionally focused.
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?
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?
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.
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.
AI ROI crisis (1 in 5 initiatives delivering), readiness gaps, C-suite alignment needs, AI archetypes; the perennial 'year of the foundation'
Should my organization adopt an AI-Cautious posture?
Why are 4 out of 5 AI initiatives failing to achieve ROI?
Independent data community thought leader and analyst
A formal agreement between data producers and consumers specifying schema, lineage, SLAs, and quality metrics. Bridges upstream operational governance and downstream analytical governance.
Gartner archetype: organizations pursuing AI selectively where clear opportunities arise without full commitment.
Gartner moderator of the vendor strategy panel
A Gartner-defined classification of organizational posture toward AI adoption