@prefix : . @prefix schema: . @prefix owl: . @prefix rdfs: . @prefix rdf: . @prefix xsd: . @prefix prov: . # ── Ontology ────────────────────────────────────────────────────────────── : a owl:Ontology ; schema:name "CDOIQ 2026 Takeaways Knowledge Graph Ontology"@en ; schema:description "Lightweight ontology and knowledge graph describing Juan Sequeda's CDOIQ 2026 conference takeaways article."@en ; schema:identifier "https://juansequeda.substack.com/p/cdoiq-2026-my-honest-no-bs-takeaways"^^xsd:anyURI . :CaseStudy a rdfs:Class ; rdfs:label "Case Study"@en ; rdfs:comment "An organizational case study of a data governance or AI initiative presented at CDOIQ 2026."@en ; rdfs:isDefinedBy : . :PanelSession a rdfs:Class ; rdfs:label "Panel Session"@en ; rdfs:comment "A multi-speaker panel discussion at CDOIQ 2026."@en ; rdfs:isDefinedBy : . :Quotation a rdfs:Class ; rdfs:label "Quotation"@en ; rdfs:comment "A direct or closely paraphrased statement attributed to a named speaker at CDOIQ 2026."@en ; rdfs:isDefinedBy : . :StrategicQuestion a rdfs:Class ; rdfs:label "Strategic Question"@en ; rdfs:comment "One of the three questions the author poses to data leaders."@en ; rdfs:isDefinedBy : . :hasOutcome a rdf:Property ; rdfs:label "has outcome"@en ; rdfs:comment "Links a case study to its measured or reported outcome."@en ; rdfs:domain :CaseStudy ; rdfs:range xsd:string ; rdfs:isDefinedBy : . :featuresOrganization a rdf:Property ; rdfs:label "features organization"@en ; rdfs:comment "Links a case study or panel session to the organization it profiles."@en ; rdfs:domain :CaseStudy ; rdfs:range schema:Organization ; rdfs:isDefinedBy : . :hasModerator a rdf:Property ; rdfs:label "has moderator"@en ; rdfs:comment "Links a panel session to the person who moderated it."@en ; rdfs:domain :PanelSession ; rdfs:range schema:Person ; rdfs:isDefinedBy : . :hasPanelist a rdf:Property ; rdfs:label "has panelist"@en ; rdfs:comment "Links a panel session to a person who spoke on it."@en ; rdfs:domain :PanelSession ; rdfs:range schema:Person ; rdfs:isDefinedBy : . # ── Core Article ────────────────────────────────────────────────────────── :article a schema:Article ; schema:name "CDOIQ 2026: My Honest, No-BS Takeaways"@en ; schema:headline "CDOIQ 2026 Conference Takeaways"@en ; schema:abstract "Juan Sequeda's grounded, no-hype recap of the CDOIQ 2026 conference: foundational governance work at Nationwide, KeyBank, Leidos, and Farm Credit Services of America; the tech-company divide exemplified by ADP and Capital One; the changing CDO role across Humana, Ford, and Lowe's; agentic AI reality checks from CVS, CSL, The Hartford, and JPMorgan Chase; and semantics as first-class infrastructure."@en ; schema:articleBody "Sequeda characterizes CDOIQ 2026 as grounded rather than hype-driven. Part One shows data leaders treating governance, data quality, and organizational alignment as the real work itself, not a prerequisite, illustrated by Nationwide's Trusted Data Score, KeyBank's multi-year adoption effort, Leidos's diamond leadership model, and Farm Credit Services of America's targeted data-element approach. Part Two contrasts ADP and Capital One's decade-long, technology-native data platforms against the harder path facing non-tech enterprises. Part Three follows the CDO panel (Humana, Ford, Lowe's) through the shifting nature of the role and the structural drivers of CDO turnover. Part Four is a reality check on agentic AI in production, centered on minimizing non-determinism, plus Farm Credit Services of America's agentic credit-narrative automation. Part Five argues semantics is the thread running through everything, closing with Sequeda's own 20 Lessons talk and three strategic questions for data executives."@en ; schema:url ; schema:datePublished "2026-07-30"^^xsd:date ; schema:author ; schema:accountablePerson ; schema:publisher ; schema:about :cdoiqConference , :dataGovernance , :agenticAI , :semanticsAsInfrastructure ; schema:mentions , , , :farmCreditServicesOfAmerica , , , , , , , , , ; schema:hasPart :partOneSection , :partTwoSection , :partThreeSection , :partFourSection , :partFiveSection , :closingSection , :faqSection , :glossarySection , : ; prov:wasGeneratedBy . :partOneSection schema:isPartOf :article . :partTwoSection schema:isPartOf :article . :partThreeSection schema:isPartOf :article . :partFourSection schema:isPartOf :article . :partFiveSection schema:isPartOf :article . :closingSection schema:isPartOf :article . :faqSection schema:isPartOf :article . :glossarySection schema:isPartOf :article . : schema:isPartOf :article . # ── Part One: Foundations ─────────────────────────────────────────────────── :partOneSection a schema:CreativeWork ; schema:name "Part One: Most Organizations Are Still in the Middle of Fundamentals"@en ; schema:about :dataGovernance ; schema:text "Across every session, one thing came up repeatedly: you cannot shortcut the foundation. Governance, data quality, ownership models, and semantics are not prerequisites to the real work — they are the real work."@en ; schema:hasPart :nationwideCaseStudy , :keyBankCaseStudy , :leidosCaseStudy , :farmCreditCaseStudy . :nationwideCaseStudy a :CaseStudy ; schema:name "Nationwide: Making Trust Measurable"@en ; schema:isPartOf :partOneSection ; schema:creator ; schema:description "Bill Snider presented Nationwide's Trusted Data Score, a five-category star-rating framework (ownership, curation, quality, protection, observability) applied to roughly 100 priority 'lake houses' out of 12,500 schemas under management, making data trust measurable and visible to executives."@en ; :featuresOrganization ; :hasOutcome "Governance focused on ~100 of 12,500 schemas via a weighted, five-category Trusted Data Score"@en . :keyBankCaseStudy a :CaseStudy ; schema:name "KeyBank: Sustained and Disciplined Execution"@en ; schema:isPartOf :partOneSection ; schema:creator ; schema:description "Caroline Serio and her team (Douglas Kanouff, Kevin Eichas, Brendan Sullivan, Steve Kimmett) rebuilt data catalog adoption at KeyBank over four years through gamification, community building, an annual internal Data Summit, and a shift to a data product ownership model — not a new tool purchase."@en ; :featuresOrganization ; :hasOutcome "Four-year adoption turnaround; Data Summit grew to 400+ attendees and 20 booths; legacy data-quality debt resolved once business units owned data as a product"@en . :leidosCaseStudy a :CaseStudy ; schema:name "Leidos: The Diamond Model"@en ; schema:isPartOf :partOneSection ; schema:creator ; schema:description "Kris Mork, CDO of Leidos, presented a diamond model data strategy (Attention first, Direction second, Energy third, Congruence throughout) using a horse-herd leadership metaphor, arguing most data leaders start with energy before establishing direction grounded in real stakeholder needs."@en ; :featuresOrganization . :farmCreditCaseStudy a :CaseStudy ; schema:name "Farm Credit Services of America: Don't Boil the Ocean"@en ; schema:isPartOf :partOneSection ; schema:creator ; schema:description "Narayanan Nair, CDO of Farm Credit Services of America, identified 15-20 critical data elements across 15-20 critical systems (~400 total elements) most likely to feed AI and agentic systems, appointed a dedicated knowledge manager, and consolidated unstructured documents onto a managed platform before applying AI."@en ; :featuresOrganization :farmCreditServicesOfAmerica . :farmCreditServicesOfAmerica a schema:Organization ; schema:name "Farm Credit Services of America"@en ; schema:description "A regional association within the U.S. Farm Credit System providing agricultural lending and financial services."@en . # ── Part Two: The Tech Company Divide ─────────────────────────────────────── :partTwoSection a schema:CreativeWork ; schema:name "Part Two: The Tech Company Divide"@en ; schema:text "ADP and Capital One built genuinely impressive data platforms over a decade because technology is native to their organizational DNA. The honest no-bs question: if the ADPs and Capital Ones of the world can do this, can everyone else?"@en ; schema:hasPart :adpCaseStudy , :capitalOneCaseStudy , :institutionalDelusionQuote . :adpCaseStudy a :CaseStudy ; schema:name "ADP: The Muruntau Gold Mine Analogy"@en ; schema:isPartOf :partTwoSection ; schema:creator ; schema:description "Amin Venjara, CDO of ADP, compared enterprise data to the Muruntau gold mine in Uzbekistan: gold discovered in 1958 wasn't realized until mining infrastructure was built. ADP designed its data platform around three personas (builders, analyzers, operators) and invested heavily in entity resolution and graph infrastructure, defining an enterprise knowledge graph linking 1M clients."@en ; :featuresOrganization . :capitalOneCaseStudy a :CaseStudy ; schema:name "Capital One: Federated Hub-and-Spoke Data Products"@en ; schema:isPartOf :partTwoSection ; schema:creator ; schema:description "Amy Lenander (CDO) and Christina Egea (SVP of Product Management) presented Capital One's federated hub-and-spoke model with one dedicated owner across both operational and analytical environments, and its rejection of boiling-the-ocean standardization in favor of high-reuse data first. Capital One formalized the ontologist job title in 2022; Bethany Sehon presented a related knowledge graph pilot at the 2019 Knowledge Graph Conference."@en ; :featuresOrganization . :institutionalDelusionQuote a schema:CreativeWork ; schema:name "Institutional Delusion"@en ; schema:isPartOf :partTwoSection ; schema:text "Institutional delusion, thinking you are what you're not."@en ; schema:description "A framing discussed at the #HonestNoBS Dinner for why non-tech enterprises may not be able to transplant the organizational DNA behind ADP's and Capital One's decade of compounding data investment."@en . # ── Part Three: The CDO Role ───────────────────────────────────────────────── :partThreeSection a schema:CreativeWork ; schema:name "Part Three: The CDO Role, What's Actually Changing"@en ; schema:text "The role has moved from defensive, compliance-oriented work to offensive value creation and business transformation, with meaningful nuance across industries."@en ; schema:hasPart :cdoPanelSession , :cdoTurnoverDiscussion . :cdoPanelSession a :PanelSession ; schema:name "The CDO Panel"@en ; schema:isPartOf :partThreeSection ; schema:description "Bhagyesh Phanse (CDAO, Humana), Caroline Buckley (CDAIO, Ford), and Chandhu Nair (SVP Stores, Data, AI, and Innovation, Lowe's) discussed how the CDO role has shifted toward business transformation, moderated by Randy Bean."@en ; :hasModerator ; :hasPanelist , , ; :featuresOrganization , , . :cdoTurnoverDiscussion a schema:CreativeWork ; schema:name "The CDO Turnover Question"@en ; schema:isPartOf :partThreeSection ; schema:about :cdoTurnover ; schema:description "Caroline Buckley framed high CDO turnover as a positive signal of demand for great talent. Bhagyesh Phanse framed it as a structural problem: expectations are up, patience is dramatically down, and the gap between what leaders are asked to deliver and the timeline it actually takes drives replacement. Chandhu Nair saw it as a natural technology-adoption cycle of centralization, democratization, and recentralization."@en ; schema:hasPart :phanseQuote , :nairSkillsQuote . :phanseQuote a schema:CreativeWork ; schema:name "Expectations Up, Patience Down"@en ; schema:isPartOf :cdoTurnoverDiscussion ; schema:creator ; schema:text "Expectations are up, patience is dramatically down, and the gap between what leaders are asked to deliver and the timeline it actually takes is the real driver."@en . :nairSkillsQuote a schema:CreativeWork ; schema:name "Value Realization Storytelling"@en ; schema:isPartOf :cdoTurnoverDiscussion ; schema:creator ; schema:text "Data storytelling has evolved to value realization storytelling — telling the story of what changed in the business because of the data, not the data story itself."@en . # ── Part Four: Agents, AI, and the Reality Check ──────────────────────────── :partFourSection a schema:CreativeWork ; schema:name "Part Four: Agents, AI, and the Reality Check"@en ; schema:about :agenticAI ; schema:text "Agentic AI is non-deterministic: the next action depends on prior output and can always change. Practitioners running this in production argue for keeping the agentic surface area as small as possible."@en ; schema:hasPart :aiAgentsPanelSession , :financialServicesPanelSession , :moralesQuote , :farmCreditProductivityCaseStudy , :shadowAgentDiscussion . :aiAgentsPanelSession a :PanelSession ; schema:name "AI Agents Panel"@en ; schema:isPartOf :partFourSection ; schema:description "Radha Kuchibhotla (Lead Director, AI Solutions Design, CVS) and Art Morales (VP, Technology Enabled Science, CSL) discussed what counts as an 'agent' and the production risk of non-deterministic agentic AI."@en ; :hasPanelist , ; :featuresOrganization , . :financialServicesPanelSession a :PanelSession ; schema:name "Financial Services Panel"@en ; schema:isPartOf :partFourSection ; schema:description "Nachiket Mehta (VP of AI and Data Engineering, The Hartford), Andy Ghosal (VP Product Management, JPMorgan Chase), and Narayanan Nair (CDO, Farm Credit Services of America) discussed AI and data engineering in financial services."@en ; :hasPanelist , , ; :featuresOrganization , , :farmCreditServicesOfAmerica . :moralesQuote a schema:CreativeWork ; schema:name "Minimize Non-Determinism"@en ; schema:isPartOf :partFourSection ; schema:creator ; schema:about :agenticAI ; schema:text "The goal in production should be to minimize the non-determinism of agentic AI and convert as many steps as possible to deterministic processes."@en . :farmCreditProductivityCaseStudy a :CaseStudy ; schema:name "Farm Credit Services of America: Agentic Credit Narratives"@en ; schema:isPartOf :partFourSection ; schema:description "Farm Credit Services of America writes approximately 12,000 credit narratives annually. An agentic solution with five to six specialized agents, pulling from a consolidated enterprise data warehouse, cut writing time roughly in half, validated by regenerating hundreds of past narratives and comparing them against analyst-written originals."@en ; :featuresOrganization :farmCreditServicesOfAmerica ; :hasOutcome "Writing time cut roughly in half; estimated 20,000-24,000 hours saved per year; human review time reduced to about five minutes per narrative"@en . :shadowAgentDiscussion a schema:CreativeWork ; schema:name "The Shadow Agent Problem"@en ; schema:isPartOf :partFourSection ; schema:about :termShadowAgent ; schema:text "Agent governance requires traceability, human-in-the-loop oversight, and proper access controls. The shadow agent problem is real: employees build their own agents, then leave, with no documentation of what those agents do."@en . # ── Part Five: Semantics as Infrastructure ─────────────────────────────────── :partFiveSection a schema:CreativeWork ; schema:name "Part Five: Semantics, The Thread Running Through Everything"@en ; schema:about :semanticsAsInfrastructure ; schema:text "Semantics and ontologies came up in more sessions, in more ways, than anything else at CDOIQ 2026 — and still isn't treated with the seriousness it deserves. Semantics are a must for AI and for the interoperability of systems."@en ; schema:hasPart :twentyLessonsTalk , :sequedaQuestionsSection . :twentyLessonsTalk a schema:CreativeWork ; schema:name "Scar Tissue and 20 Lessons from 20 Years of Building the Foundation AI Actually Needs"@en ; schema:isPartOf :partFiveSection ; schema:creator ; schema:description "Sequeda's own CDOIQ talk, first delivered at Data Day Texas in January 2026, covering 20 lessons in one minute each: ontologies and knowledge graphs meaningfully improve LLM accuracy (a 3x improvement in query accuracy from grounding LLMs in a knowledge graph); automated ontology generation is overselling assisted automation with human governance; structured semantic integration is a harder problem than unstructured text POCs; governance is what gets AI POCs out of pilot and into production; and knowledge should be a first-class citizen, not a byproduct of data."@en . :sequedaQuestionsSection a schema:CreativeWork ; schema:name "Three Questions Every Data Executive Should Be Asking"@en ; schema:isPartOf :partFiveSection ; schema:hasPart :q_optionality , :q_governance , :q_incentives . :q_optionality a :StrategicQuestion ; schema:name "Do my vendors support true data and semantics optionality, or are they creating lock-in behind the language of openness?"@en ; schema:isPartOf :sequedaQuestionsSection . :q_governance a :StrategicQuestion ; schema:name "Do I have the governance infrastructure to take my AI POCs to production?"@en ; schema:isPartOf :sequedaQuestionsSection . :q_incentives a :StrategicQuestion ; schema:name "Are my incentive structures rewarding durable knowledge infrastructure or just fast wins that create tomorrow's technical debt?"@en ; schema:isPartOf :sequedaQuestionsSection . # ── Closing ─────────────────────────────────────────────────────────────── :closingSection a schema:CreativeWork ; schema:name "Closing Thoughts"@en ; schema:text "The leap from solid foundations to actual business transformation hasn't happened yet for most of these organizations. Semantics is the differentiator, and the organizational challenge is harder than the technical one. The CDOs who understand this haven't been chasing hype — they've been busy building real foundations."@en ; schema:hasPart :assessmentQuote . :assessmentQuote a schema:CreativeWork ; schema:name "The Transformation Gap"@en ; schema:isPartOf :closingSection ; schema:creator ; schema:text "The leap from solid foundations to actual business transformation hasn't happened yet for most of these organizations, and nobody quite knows how to close it."@en . # ── Event & Concepts ───────────────────────────────────────────────────── :cdoiqConference a schema:Event ; schema:name "CDOIQ 2026"@en ; schema:description "The 20th Annual Chief Data Officer and Information Quality (CDOIQ) Symposium, held at the Hyatt Regency Boston-Cambridge, where data executives discussed governance, data quality, and agentic AI in production."@en ; schema:startDate "2026"^^xsd:gYear ; schema:url . :dataGovernance a schema:DefinedTerm ; schema:name "Data Governance"@en ; schema:description "The foundational discipline of managing data quality, ownership, and organizational alignment that Sequeda frames as the real work, not a prerequisite to it."@en . :agenticAI a schema:DefinedTerm ; schema:name "Agentic AI"@en ; schema:description "Non-deterministic AI systems whose next action depends on prior output and can always change, discussed at CDOIQ 2026 in terms of production risk and the need to minimize non-determinism."@en . :semanticsAsInfrastructure a schema:DefinedTerm ; schema:name "Semantics as Infrastructure"@en ; schema:description "The practice of treating ontologies and knowledge graphs as first-class infrastructure for AI and system interoperability rather than optional tooling."@en . :diamondModel a schema:DefinedTerm ; schema:name "Diamond Model"@en ; schema:description "Kris Mork's data leadership model: Attention first, Direction second, Energy third, Congruence throughout — skipping a step produces fragmentation or cultural collapse."@en . :dataProductOwnership a schema:DefinedTerm ; schema:name "Data Product Ownership"@en ; schema:description "A model in which business units own their data as a product with real accountability, credited at KeyBank as the turning point that resolved years of deferred data-quality debt."@en . # ── People ────────────────────────────────────────────────────────────── a schema:Person ; schema:name "Juan Sequeda"@en ; schema:url ; schema:identifier "https://www.linkedin.com/in/juansequeda/" ; owl:sameAs , . a schema:Person ; schema:name "Juan Sequeda"@en ; schema:url . a schema:Person ; schema:name "Juan Sequeda"@en ; schema:url . a schema:Person ; schema:name "Bill Snider"@en ; schema:url ; schema:worksFor . a schema:Person ; schema:name "Caroline Serio"@en ; schema:url ; schema:jobTitle "Head of Data and Analytics Governance"@en ; schema:worksFor . a schema:Person ; schema:name "Douglas Kanouff"@en ; schema:url ; schema:worksFor . a schema:Person ; schema:name "Kevin Eichas"@en ; schema:url ; schema:worksFor . a schema:Person ; schema:name "Brendan Sullivan"@en ; schema:url ; schema:worksFor . :steveKimmett a schema:Person ; schema:name "Steve Kimmett"@en ; schema:worksFor ; rdfs:comment "Named as part of the KeyBank team; no platform profile URL given in source."@en . a schema:Person ; schema:name "Kris Mork"@en ; schema:url ; schema:jobTitle "Chief Data Officer"@en ; schema:worksFor . a schema:Person ; schema:name "Narayanan Nair"@en ; schema:url ; schema:jobTitle "Chief Data Officer"@en ; schema:worksFor :farmCreditServicesOfAmerica . a schema:Person ; schema:name "Amin Venjara"@en ; schema:url ; schema:jobTitle "Chief Data Officer"@en ; schema:worksFor . a schema:Person ; schema:name "Amy Lenander"@en ; schema:url ; schema:jobTitle "Chief Data Officer"@en ; schema:worksFor . a schema:Person ; schema:name "Christina Egea"@en ; schema:url ; schema:jobTitle "SVP of Product Management"@en ; schema:worksFor . a schema:Person ; schema:name "Bethany Sehon"@en ; schema:url ; schema:worksFor . a schema:Person ; schema:name "Bhagyesh Phanse"@en ; schema:url ; schema:jobTitle "Chief Data and Analytics Officer"@en ; schema:worksFor . a schema:Person ; schema:name "Caroline Buckley"@en ; schema:url ; schema:jobTitle "Chief Data and Analytics/Insights Officer"@en ; schema:worksFor . a schema:Person ; schema:name "Chandhu Nair"@en ; schema:url ; schema:jobTitle "SVP Stores, Data, AI, and Innovation"@en ; schema:worksFor . a schema:Person ; schema:name "Randy Bean"@en ; schema:url ; rdfs:comment "Moderator of the CDOIQ 2026 CDO panel."@en . a schema:Person ; schema:name "Radha Kuchibhotla"@en ; schema:url ; schema:jobTitle "Lead Director, AI Solutions Design"@en ; schema:worksFor . a schema:Person ; schema:name "Art Morales"@en ; schema:url ; schema:jobTitle "VP, Technology Enabled Science"@en ; schema:worksFor . a schema:Person ; schema:name "Nachiket Mehta"@en ; schema:url ; schema:jobTitle "VP of AI and Data Engineering"@en ; schema:worksFor . a schema:Person ; schema:name "Andy Ghosal"@en ; schema:url ; schema:jobTitle "VP Product Management"@en ; schema:worksFor . :kimberleyHerrington a schema:Person ; schema:name "Kimberley Herrington"@en ; rdfs:comment "Credited by Sequeda for capturing a photo moment; no platform profile URL given in source."@en . # ── Organizations ────────────────────────────────────────────────────────── a schema:Organization ; rdfs:label "Nationwide"@en ; schema:name "Nationwide"@en ; schema:description "A U.S. insurance and financial services group managing 12,500 schemas across 2,500 critical business applications, presented at CDOIQ 2026 for its Trusted Data Score governance framework."@en . a schema:Organization ; rdfs:label "KeyBank"@en ; schema:name "KeyBank"@en ; schema:description "An American regional bank whose data and analytics governance team rebuilt catalog adoption over four years through community building and a shift to data product ownership."@en . a schema:Organization ; rdfs:label "Leidos"@en ; schema:name "Leidos"@en ; schema:description "An American defense, aviation, and IT company whose CDO Kris Mork presented a diamond model of data leadership at CDOIQ 2026."@en . a schema:Organization ; rdfs:label "ADP"@en ; schema:name "ADP"@en ; schema:description "An American human resources management and payroll processing company cited at CDOIQ 2026 as a benchmark for decade-long, technology-native data platform investment."@en . a schema:Organization ; rdfs:label "Capital One"@en ; schema:name "Capital One"@en ; schema:description "An American bank holding company that formalized the ontologist job title in 2022 and runs a federated hub-and-spoke data product model, cited alongside ADP as a tech-company benchmark."@en . a schema:Organization ; rdfs:label "Humana"@en ; schema:name "Humana"@en ; schema:description "An American health insurance company whose Chief Data and Analytics Officer Bhagyesh Phanse discussed the dual mandate of offensive AI value and defensive risk management on the CDOIQ 2026 CDO panel."@en . a schema:Organization ; rdfs:label "Ford"@en ; schema:name "Ford Motor Company"@en ; schema:description "An American automaker whose Chief Data and Analytics/Insights Officer Caroline Buckley described running parallel tracks of fixing data for current AI initiatives while building an enterprise semantic layer."@en . a schema:Organization ; rdfs:label "Lowe's"@en ; schema:name "Lowe's"@en ; schema:description "An American home improvement retailer where Chandhu Nair leads Stores, Data, AI, and Innovation without a formal CDO title, and where AI is used at scale to parse unstructured documents into taxonomies."@en . a schema:Organization ; rdfs:label "CVS"@en ; schema:name "CVS Health"@en ; schema:description "An American healthcare company whose Lead Director of AI Solutions Design, Radha Kuchibhotla, discussed agentic AI on the CDOIQ 2026 AI panel."@en . a schema:Organization ; rdfs:label "CSL"@en ; schema:name "CSL Limited"@en ; schema:description "An Australian multinational biotechnology company whose VP of Technology Enabled Science, Art Morales, argued production agentic AI should minimize non-determinism."@en . a schema:Organization ; rdfs:label "The Hartford"@en ; schema:name "The Hartford"@en ; schema:description "An American insurance company whose VP of AI and Data Engineering, Nachiket Mehta, discussed AI and data engineering on the CDOIQ 2026 financial services panel."@en . a schema:Organization ; rdfs:label "JPMorgan Chase"@en ; schema:name "JPMorgan Chase"@en ; schema:description "An American multinational banking institution whose VP of Product Management, Andy Ghosal, discussed AI and data engineering on the CDOIQ 2026 financial services panel."@en . # ── FAQ ──────────────────────────────────────────────────────────────── :faqSection a schema:FAQPage ; schema:name "Frequently Asked Questions"@en ; schema:mainEntity :q1 , :q2 , :q3 , :q4 , :q5 , :q6 , :q7 , :q8 , :q9 , :q10 , :q11 , :q12 , :q13 , :q14 . :q1 a schema:Question ; schema:name "What was the overall tone of the CDOIQ 2026 conference according to Juan Sequeda?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a1 . :a1 a schema:Answer ; schema:text "Sequeda characterizes the conference as grounded rather than hype-driven, with data executives emphasizing foundational work over transformation theater."@en . :q2 a schema:Question ; schema:name "What is Nationwide's Trusted Data Score?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a2 . :a2 a schema:Answer ; schema:text "A five-category star-rating framework (ownership, curation, quality, protection, observability) that Bill Snider's team applies to roughly 100 priority lake houses out of 12,500 schemas, making data trust measurable and visible to executives."@en . :q3 a schema:Question ; schema:name "How did KeyBank turn around data catalog adoption?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a3 . :a3 a schema:Answer ; schema:text "Caroline Serio's team drove adoption over four years through gamification, community building, an internal Data Summit, and a shift to a data product ownership model that resolved years of deferred data-quality debt."@en . :q4 a schema:Question ; schema:name "What is Kris Mork's Diamond Model?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a4 . :a4 a schema:Answer ; schema:text "Leidos CDO Kris Mork's data leadership model: Attention first, Direction second, Energy third, Congruence throughout — skipping a step produces fragmentation or cultural collapse."@en . :q5 a schema:Question ; schema:name "What outcome did Farm Credit Services of America achieve with agentic AI?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a5 . :a5 a schema:Answer ; schema:text "An agentic solution automating credit narrative writing cut analyst time roughly in half, saving an estimated 20,000-24,000 hours per year, validated against hundreds of analyst-written originals."@en . :q6 a schema:Question ; schema:name "What is the 'tech company divide' Sequeda identifies?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a6 . :a6 a schema:Answer ; schema:text "ADP and Capital One built mature data platforms over a decade because technology is native to their organizational DNA, raising the honest question of whether non-tech enterprises can replicate that path or need a different one."@en . :q7 a schema:Question ; schema:name "What did Art Morales say about agentic AI in production?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a7 . :a7 a schema:Answer ; schema:text "That the goal in production should be to minimize the non-determinism of agentic AI and convert as many steps as possible to deterministic processes."@en . :q8 a schema:Question ; schema:name "How does the article treat semantics and ontologies?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a8 . :a8 a schema:Answer ; schema:text "As the thread running through everything and a must for AI, citing Capital One's ontologist titles since 2022, ADP's enterprise knowledge graph, and Lowe's use of AI to parse unstructured documents into taxonomies."@en . :q9 a schema:Question ; schema:name "What three questions does Sequeda pose to data leaders?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a9 . :a9 a schema:Answer ; schema:text "Whether vendors support true data and semantics optionality or create lock-in behind the language of openness, whether governance infrastructure supports taking AI POCs to production, and whether incentive structures reward durable knowledge infrastructure over fast wins."@en . :q10 a schema:Question ; schema:name "What is Sequeda's overall assessment of the industry's progress?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a10 . :a10 a schema:Answer ; schema:text "Foundational work is proceeding steadily, but the leap from solid foundations to actual business transformation hasn't happened yet for most of these organizations, and nobody quite knows how to close it."@en . :q11 a schema:Question ; schema:name "Who spoke on the CDOIQ 2026 CDO panel?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a11 . :a11 a schema:Answer ; schema:text "Bhagyesh Phanse (Humana), Caroline Buckley (Ford), and Chandhu Nair (Lowe's), moderated by Randy Bean, discussing how the CDO role has shifted from defensive compliance to offensive value creation."@en . :q12 a schema:Question ; schema:name "What are the three explanations offered for high CDO turnover?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a12 . :a12 a schema:Answer ; schema:text "Caroline Buckley sees it as a positive signal of demand for great talent; Bhagyesh Phanse sees a structural mismatch between rising expectations and shrinking patience; Chandhu Nair sees a natural technology-adoption cycle of centralization, democratization, and recentralization."@en . :q13 a schema:Question ; schema:name "What is the 'shadow agent' problem?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a13 . :a13 a schema:Answer ; schema:text "Employees building their own AI agents and then leaving, with no documentation of what those agents do — a real and growing governance risk without traceability and human-in-the-loop oversight."@en . :q14 a schema:Question ; schema:name "What was Juan Sequeda's own CDOIQ 2026 talk about?"@en ; schema:isPartOf :faqSection ; schema:acceptedAnswer :a14 . :a14 a schema:Answer ; schema:text "Scar Tissue and 20 Lessons from 20 Years of Building the Foundation AI Actually Needs, covering topics from knowledge graphs improving LLM accuracy 3x to governance being what moves AI POCs into production."@en . # ── Glossary ────────────────────────────────────────────────────────────── :glossarySection a schema:DefinedTermSet ; schema:name "Glossary"@en ; schema:hasDefinedTerm :termCDOIQ , :termTrustedDataScore , :termLakeHouse , :termShadowAgent , :termNonDeterminism , :termOntologist , :termKnowledgeGraph , :termDataQuality , :termGovernance , :termCDOTurnover , :termDiamondModel , :termDataProductOwnership , :termInstitutionalDelusion . :termCDOIQ a schema:DefinedTerm ; schema:name "CDOIQ"@en ; schema:description "The Chief Data Officer and Information Quality Symposium, a recurring gathering of enterprise data leaders, now in its 20th year."@en ; schema:inDefinedTermSet :glossarySection . :termTrustedDataScore a schema:DefinedTerm ; schema:name "Trusted Data Score"@en ; schema:description "Nationwide's five-category star-rating framework (ownership, curation, quality, protection, observability) applied to its ~100 priority lake houses."@en ; schema:inDefinedTermSet :glossarySection . :termLakeHouse a schema:DefinedTerm ; schema:name "Lake House"@en ; schema:description "A data architecture combining data lake and data warehouse characteristics; Nationwide governs roughly 100 of these, out of 12,500 schemas, as its priority set."@en ; schema:inDefinedTermSet :glossarySection . :termShadowAgent a schema:DefinedTerm ; schema:name "Shadow Agent"@en ; schema:description "An unsanctioned or ungoverned AI agent built by an employee who later leaves the organization with no documentation of what it does — an emerging governance risk."@en ; schema:inDefinedTermSet :glossarySection . :termNonDeterminism a schema:DefinedTerm ; schema:name "Non-Determinism (Agentic AI)"@en ; schema:description "Variability in agentic AI outputs across runs; Art Morales argues production systems should minimize it by converting agentic processes to deterministic workflows."@en ; schema:inDefinedTermSet :glossarySection . :termOntologist a schema:DefinedTerm ; schema:name "Ontologist"@en ; schema:description "A formal job title, adopted by Capital One in 2022 with defined talent profiles and career paths, for practitioners who design and maintain organizational ontologies."@en ; schema:inDefinedTermSet :glossarySection . :termKnowledgeGraph a schema:DefinedTerm ; schema:name "Knowledge Graph"@en ; schema:description "A semantic data structure of entities and relationships; Sequeda cites 2023 research showing a 3x improvement in query accuracy when grounding LLMs in a knowledge graph."@en ; schema:inDefinedTermSet :glossarySection . :termDataQuality a schema:DefinedTerm ; schema:name "Data Quality"@en ; schema:description "The accuracy, completeness, and reliability of enterprise data; one of Nationwide's five Trusted Data Score categories, weighted roughly 40% in its second version."@en ; schema:inDefinedTermSet :glossarySection . :termGovernance a schema:DefinedTerm ; schema:name "Data Governance"@en ; schema:description "Organizational policy and practice for managing data as an asset; framed by Sequeda as the real work rather than a preliminary step, and by KeyBank as what gets AI POCs into production."@en ; schema:inDefinedTermSet :glossarySection . :termCDOTurnover a schema:DefinedTerm ; schema:name "CDO Turnover"@en ; schema:description "The high rate of Chief Data Officer departures; Bhagyesh Phanse attributes it to a structural mismatch between rising expectations and shrinking patience for foundational timelines."@en ; schema:inDefinedTermSet :glossarySection . :termDiamondModel a schema:DefinedTerm ; schema:name "Diamond Model"@en ; schema:description "Kris Mork's data leadership model: Attention, Direction, Energy, and Congruence, illustrated with a horse-herd leadership metaphor."@en ; schema:inDefinedTermSet :glossarySection . :termDataProductOwnership a schema:DefinedTerm ; schema:name "Data Product Ownership"@en ; schema:description "A model where business units own their data as a product with real accountability; credited at KeyBank as the turning point that resolved long-deferred data-quality issues."@en ; schema:inDefinedTermSet :glossarySection . :termInstitutionalDelusion a schema:DefinedTerm ; schema:name "Institutional Delusion"@en ; schema:description "Thinking your organization is what it's not — a framing from the #HonestNoBS Dinner for why non-tech enterprises may struggle to transplant tech-company data DNA."@en ; schema:inDefinedTermSet :glossarySection . # ── Provenance / Skill Attribution ───────────────────────────────────────── a schema:SoftwareApplication , prov:SoftwareAgent ; schema:name "kg-generator skill"@en ; schema:url ; schema:description "AI agent skill that generates comprehensive Knowledge Graphs (RDF-Turtle or JSON-LD) from file: or http(s): URLs."@en ; prov:actedOnBehalfOf . a schema:Person ; schema:name "Kingsley Uyi Idehen"@en .