[{"id":2019,"date":"2026-04-09T09:04:08","date_gmt":"2026-04-09T09:04:08","guid":{"rendered":"https:\/\/datadecoded.com\/ldn\/?post_type=seminar&#038;p=2019"},"modified":"2026-04-14T13:20:54","modified_gmt":"2026-04-14T13:20:54","slug":"panel-stretching-the-limits-of-data-ai-and-it-how-leading-brands-are-keeping-their-balance","status":"publish","type":"seminar","link":"https:\/\/datadecoded.com\/ldn\/seminars\/panel-stretching-the-limits-of-data-ai-and-it-how-leading-brands-are-keeping-their-balance\/","title":{"rendered":"PANEL: Stretching the Limits of Data, AI and IT \u2013 How Leading Brands Are Keeping Their Balance"},"content":{"rendered":"<p style=\"font-weight: 400\">The triumvirate of IT, data and AI offices need to work together like never before, pushing out into multiple cloud-based environments while at the same time keeping core legacy business systems operating optimally. Ensuring data moves at the speed of AI while protecting critical processes can seem like an impossible balancing act at times.<\/p>\n<p style=\"font-weight: 400\">In this engaging session the challenge of orchestrating between the CIO, CDO and CAIO will be put under scrutiny.<\/p>\n<p style=\"font-weight: 400\">A vital session for any delegates wrangling with the alignment of IT, data, AI and business strategies, while also ensuring engineering keeps the critical edata co-system running.<\/p>\n<p><strong>Takeaways<\/strong><\/p>\n<ul>\n<li>Why building cross-functional relationships ensures delivery on the promise of AI<\/li>\n<li>Typical stumbling blocks and areas of conflict<\/li>\n<li>How to maintain performance while keeping control of costs<\/li>\n<li>When to say yes to new solution features and when no is the only option<\/li>\n<\/ul>\n","protected":false},"template":"","class_list":["post-2019","seminar","type-seminar","status-publish","hentry"],"acf":{"start_time":"2026-04-22 16:30:00","end_time":"2026-04-22 17:15:00","theatre":630,"speakers":[2022,2055,2057],"businesses":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.6 (Yoast SEO v28.6) - 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Most stall somewhere between demo and production. The industry has been asking which model is best? \u2013 when the answer lives elsewhere entirely.<\/div>\n<div>\nFrank Weigel, Chief Product Officer at Matillion, makes the case that 2026 is the year of context: the year it becomes undeniable that AI systems are only as intelligent as what they know about your specific business. The failure mode that kills enterprise AI is not hallucination. It is a confident, plausible answer built on the wrong data \u2014 and it&#8217;s undetectable without the right infrastructure around the model.<br \/>\nAttendees will hear why human-curated approaches like data catalogs have always failed at scale, what a properly architected context system looks like, and leave with a three-question diagnostic for evaluating any AI initiative they&#8217;re running today.<br \/>\n<b><br \/>\n<\/b><\/div>\n<div><b>Takeaways <\/b><\/p>\n<ul>\n<li>Enterprise AI failures stem from missing company-specific context for data selection, not weak models \u2014 and the most dangerous outputs are structurally correct but semantically wrong, which conventional testing can&#8217;t detect<\/li>\n<li>Human-curated context systems like data catalogs are structurally flawed; context must be AI-managed and continuously updated to work reliably<\/li>\n<li>Trustworthy AI depends on two things: sufficient context for correct data selection, and lineage-based transparency that domain experts (not just engineers) can verify<\/li>\n<li>A simple three-question diagnostic can assess the context maturity of any AI data initiative.<\/li>\n<\/ul>\n<\/div>\n","protected":false},"template":"","class_list":["post-1979","seminar","type-seminar","status-publish","hentry"],"acf":{"start_time":"2026-04-23 10:30:00","end_time":"2026-04-23 11:00:00","theatre":630,"speakers":[1977],"businesses":[1088]},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.6 (Yoast SEO v28.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>The Year of Context - Data Decoded LDN<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datadecoded.com\/ldn\/seminars\/the-year-of-context\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Year of Context\" \/>\n<meta property=\"og:description\" content=\"Every AI project begins with a demonstration that impresses. Most stall somewhere between demo and production....\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datadecoded.com\/ldn\/seminars\/the-year-of-context\/\" \/>\n<meta property=\"og:site_name\" content=\"Data Decoded LDN\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataDecoded\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datadecoded.com\/ldn\/wp-content\/uploads\/sites\/4\/2025\/11\/SS005579-DATA-DECODED-LDN-SITE-HEADER-scaled.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1280\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@DataDecoded_HQ\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/seminars\\\/the-year-of-context\\\/\",\"url\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/seminars\\\/the-year-of-context\\\/\",\"name\":\"The Year of Context - Data Decoded LDN\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/#website\"},\"datePublished\":\"2026-04-08T08:39:09+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/seminars\\\/the-year-of-context\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/seminars\\\/the-year-of-context\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/seminars\\\/the-year-of-context\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"The Year of Context\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/#website\",\"url\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/\",\"name\":\"Data Decoded LDN\",\"description\":\"14-15 Apr 2027, Olympia London\",\"publisher\":{\"@id\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/#organization\",\"name\":\"Data Decoded LDN\",\"url\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2025\\\/11\\\/SS005579-DATA-DECODED-LDN-SITE-HEADER-scaled.webp\",\"contentUrl\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/wp-content\\\/uploads\\\/sites\\\/4\\\/2025\\\/11\\\/SS005579-DATA-DECODED-LDN-SITE-HEADER-scaled.webp\",\"width\":2560,\"height\":1280,\"caption\":\"Data Decoded LDN\"},\"image\":{\"@id\":\"https:\\\/\\\/datadecoded.com\\\/ldn\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/DataDecoded\",\"https:\\\/\\\/x.com\\\/DataDecoded_HQ\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/105812864\\\/\",\"https:\\\/\\\/www.youtube.com\\\/@DataDecodedHQ\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"The Year of Context - Data Decoded LDN","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datadecoded.com\/ldn\/seminars\/the-year-of-context\/","og_locale":"en_US","og_type":"article","og_title":"The Year of Context","og_description":"Every AI project begins with a demonstration that impresses. 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The real challenge lies in what comes next.<\/p>\n<p style=\"font-weight: 400\">In this talk, we explore how data platforms must evolve beyond storage and analytics into true intelligence platforms. Drawing on experience building enterprise-scale data infrastructure at Morgan Stanley, we examine the six pillars of AI readiness: semantics, metadata, data quality, governance, discoverability, and performance.<\/p>\n<p style=\"font-weight: 400\">We&#8217;ll walk through a practical layered architecture \u2014 from raw source systems through semantic and metrics layers to model execution \u2014 that enables AI agents and analytics applications to operate on meaning, not raw tables. Attendees will leave with a clear framework for decoupling calculations from datasets, establishing reusable metrics, and building the shared semantic foundation that modern AI applications demand.<\/p>\n<p><strong>Takeaways\u00a0<\/strong><\/p>\n<ul>\n<li>Cloud migration is just the starting point &#8211; AI readiness requires semantics, governance, and data quality on top of your cloud foundation<\/li>\n<li>AI works on meaning, not tables &#8211; A semantic and metrics layer abstracts raw complexity, enabling consistent, reusable business logic across all consumers<\/li>\n<li>Unified platforms beat siloed pipelines &#8211; A shared layered architecture reduces duplication, accelerates AI adoption, and ensures every application draws from a single trusted source of truth<\/li>\n<li style=\"list-style-type: none\"><\/li>\n<\/ul>\n","protected":false},"template":"","class_list":["post-1933","seminar","type-seminar","status-publish","hentry"],"acf":{"start_time":"2026-04-23 12:00:00","end_time":"2026-04-23 12:30:00","theatre":636,"speakers":[1929],"businesses":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.6 (Yoast SEO v28.6) - 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How to ensure trustworthy data use by AI with a data-access control plane. Break down what holds enterprises back from trusting their AI use cases, and how to quantify and communicate key signals of trustworthiness.Introduce three architectural models to measure, inform, and control how AI uses enterprise data to improve data and AI trust. Learn how Bigeye\u2019s AI Trust Platform helps AI teams earn trust in AI applications with their business stakeholders, leadership, and customers.<\/p>\n<p><strong>Takeaways:<\/strong><\/p>\n<ul>\n<li>Data quality is the #1 blocker to enterprise AI adoption. 44% of organizations cite it as their biggest challenge, and Gartner predicts 40%+ of agentic AI projects will be canceled by 2027.<\/li>\n<li>AI turns small data problems into enterprise-scale crises. When agents access and act on flawed data across an organization simultaneously, the impact is exponential.<\/li>\n<li>Governed AI lets enterprises have speed and safety. Real-world examples from oil &amp; gas, manufacturing, and insurance show that the right approach delivers visibility, control, and competitive advantage.<\/li>\n<li>Bigeye&#8217;s AI Guardian gives teams three ways to operationalize trust. Monitoring observes how AI accesses data, Advising surfaces data quality signals in real time, and Steering controls what data AI can reach via a Data Access Gateway.<\/li>\n<\/ul>\n","protected":false},"template":"","class_list":["post-1908","seminar","type-seminar","status-publish","hentry"],"acf":{"start_time":"2026-04-23 10:30:00","end_time":"2026-04-23 11:00:00","theatre":634,"speakers":[1934],"businesses":[1624]},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.6 (Yoast SEO v28.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Accelerating AI Adoption - Data Decoded LDN<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datadecoded.com\/ldn\/seminars\/accelerating-ai-adoption\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Accelerating AI Adoption\" \/>\n<meta property=\"og:description\" content=\"Hear real world examples on how Fortune 500 companies are deploying AI in production. 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A new data warehouse, governance committee, and data catalogue followed. But a critical question emerged: what is data governance actually for? The answer changed everything. Data quality only materialises through analytics &#8211; meaning governance had to extend beyond the data layer to deliver real business value. This session traces that journey: from data chaos to a four-pillar analytics governance framework, and ultimately to an analytics environment that business users can access easily and trust fully.<\/p>\n<p><strong>Takeaways<\/strong><\/p>\n<ul>\n<li>Why data governance alone is insufficient \u2014 and what analytics governance adds to close the gap<\/li>\n<li>The four components of a practical analytics governance framework you can apply in your organisation<\/li>\n<li>How to connect data quality initiatives to measurable business impact<\/li>\n<li>What it takes to build an analytics environment that business users actually trust and adopt<\/li>\n<\/ul>\n","protected":false},"template":"","class_list":["post-1865","seminar","type-seminar","status-publish","hentry"],"acf":{"start_time":"2026-04-22 15:45:00","end_time":"2026-04-22 16:15:00","theatre":634,"speakers":[1861,1863],"businesses":[1821]},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.6 (Yoast SEO v28.6) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>A Case for Analytics Governance: One CDO\u2019s Journey From Governed Data to Trusted Insights - Data Decoded LDN<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datadecoded.com\/ldn\/seminars\/a-case-for-analytics-governance-one-cdos-journey-from-governed-data-to-trusted-insights\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Case for Analytics Governance: One CDO\u2019s Journey From Governed Data to Trusted Insights\" \/>\n<meta property=\"og:description\" content=\"When a CDO inherited a fragmented analytics environment &#8211; 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