Gartner predicts 60% of orgs will fail to realize AI value due to data governance gaps. EDB's AI Factory can help.

Gartner warns that by 2027, 60% of organizations will fail to realize AI value due to data governance gaps. In The Register, EDB’s VP of Product Marketing Doug Flora explains how the EDB Postgres AI Factory helps enterprises cross the agentic AI chasm with a sovereign platform that unifies data, secures it end-to-end, and accelerates production by as much as 3x faster with 6x better cost efficiency. Read Doug’s full piece here: https://lnkd.in/eqXCTwTG #EDBPostgresAI #SovereignAI #DataSovereignty #AgenticAI #PostgreSQL #DataSecurity #AIFactory

Gartner warns that by 2027, 60% of organizations will fail to realize AI value due to data governance gaps. In The Register, EDB’s VP of Product Marketing Doug Flora explains how the EDBPostgres AI Factory helps enterprises cross the agentic AI chasm with a sovereign platform that unifies data, secures it end-to-end, and accelerates production by as much as 3x faster with 6x better cost efficiency. Read Doug’s full piece here: https://bit.ly/3HWX7iz #EDBPostgresAI #SovereignAI #DataSovereignty #AgenticAI #PostgreSQL #DataSecurity #AIFactory

Gartner warns that by 2027, 60% of organizations will fail to realize AI value due to data governance gaps. In The Register, EDB’s VP of Product Marketing Doug Flora explains how the EDBPostgres AI Factory helps enterprises cross the agentic AI chasm with a sovereign platform that unifies data, secures it end-to-end, and accelerates production by as much as 3x faster with 6x better cost efficiency. Read Doug’s full piece here: https://bit.ly/45NQ1pS #EDBPostgresAI #SovereignAI #DataSovereignty #AgenticAI #PostgreSQL #DataSecurity #AIFactory

Gartner warns that by 2027, 60% of organizations will fail to realize AI value due to data governance gaps. In The Register, EDB’s VP of Product Marketing Doug Flora explains how the EDBPostgres AI Factory helps enterprises cross the agentic AI chasm with a sovereign platform that unifies data, secures it end-to-end, and accelerates production by as much as 3x faster with 6x better cost efficiency. Read Doug’s full piece here: https://bit.ly/45ZjbT2 #EDBPostgresAI #SovereignAI #DataSovereignty #AgenticAI #PostgreSQL #DataSecurity #AIFactory

Gartner warns that by 2027, 60% of organizations will fail to realize AI value due to data governance gaps. In The Register, EDB’s VP of Product Marketing Doug Flora explains how the EDBPostgres AI Factory helps enterprises cross the agentic AI chasm with a sovereign platform that unifies data, secures it end-to-end, and accelerates production by as much as 3x faster with 6x better cost efficiency. Read Doug’s full piece here: https://bit.ly/4oZDirk #EDBPostgresAI #SovereignAI #DataSovereignty #AgenticAI #PostgreSQL #DataSecurity #AIFactory

In enterprise data, the word “fabric” gets thrown around like loose change. Most of the time it’s marketing glitter. But when DATAVERSITY published Open Data Fabric: Rethinking Data Architecture for AI at Scale on August 2025, it hit with substance. This wasn’t vendor jargon or a speculative sketch. It was a CEO with lived experience exposing cracks in #enterpriseAI and offering a structural cure. The author: Prat Moghe, CEO of Promethium, with a résumé spanning IBM Netezza, Cazena, and Cloudera. A veteran who knows exactly where legacy architectures buckle when AI scales. Moghe’s message was sharp. AI isn’t broken on algorithms, it’s failing on context. Enterprises sit on oceans of data, yet AI agents are stranded without business meaning. Real-time expectations collide with batch pipelines. Hallucinations spread when context is missing. And self-service remains a myth when teams can’t move without IT. His prescription? Unified access across silos, embed contextual intelligence into workflows, and create collaborative self-service models where #dataproducts evolve continuously. Then comes the fork: closed, all-in-one stacks that promise convenience versus open, platform-agnostic fabrics that deliver flexibility. Moghe’s stance is clear. Promethium, based in Cambridge and Menlo Park, has been shaping this debate long before the article dropped. Recognized as a Gartner Cool Vendor and CODiE Awards winner, and backed by Insight Partners, .406 Ventures, and Zetta Venture Partners, the company isn’t theorizing, it’s operationalizing. Leadership passed from founder Kaycee Lai, now CSO, to Moghe in April 2024, signaling continuity with acceleration. Promethium is building an AI-native #datafabric engineered for production scale. But rivals are stitching their own versions. Vancouver-based Kamu Data, founded in 2020 by Sergii Mikhtoniuk with researcher Özge Nilay Yalçın, is advancing a decentralized protocol also called Open Data Fabric. Kamu is building a Web3-native lakehouse with #blockchain-like immutability, verifiable processing, and real-time federation. Different philosophies, same battleground: how to make data infra not just AI-ready but indispensable. This is where Moghe’s article turns into signal. He’s not filling column inches; he’s shaping the terms of a $13B market growing 21.2% CAGR. Closed vs. open. Enterprise-hardened vs. decentralized-native. Integration vs. federation. Enterprises don’t have time for theory, they’ll pick fabrics like traders pick venues: on speed, trust, and survivability. The real thread to watch isn’t whether data fabric matters. It’s which vision, Promethium’s enterprise intelligence or Kamu’s decentralized fabric, becomes the weave of AI at scale, and which frays at the edges. #AI #AIAgents #Data #DataDriven #Enterprise #EnterpriseTech #EnterpriseAI #SaaS #Web3 #Technology #Innovation #TechEcosystem #StartupEcosystem #TechNews If software engineering peace of mind is what you crave, Vention is your zen.

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𝗜𝗻 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝟯–𝟱 𝘆𝗲𝗮𝗿𝘀, 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝘁𝗵𝗿𝗲𝗮𝘁 𝘁𝗼 𝗱𝗮𝘁𝗮–𝗱𝗿𝗶𝘃𝗲𝗻 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝘄𝗼𝘂𝗹𝗱 𝗯𝗲... → → → → → → → → → → → → → → → → → → → → → → → → → → → → → → 𝗣𝗢𝗢𝗥 𝗗𝗔𝗧𝗔 𝗤𝗨𝗔𝗟𝗜𝗧𝗬 Data engineering is rapidly changing, and not just in terms of technology. It’s becoming more strategic, more cross-functional, and more foundational than ever. Here’s what’s already shifting and what business leaders should prepare for: ❗ Security & Governance Come First. We’re seeing a major shift toward strict access control, metadata management, and pipeline-level governance. For public and private sectors alike, RBAC, auditability, and cost-efficient secure environments are becoming table stakes. Data teams are being pulled into these conversations alongside other IT roles. ❗Data Quality Over Dashboard Quantity No more “dump and visualize.” Teams are finally prioritizing validation, semantic layers, and lineage tracking. Data engineers are now advocates for quality. ❗Rise of Real-Time & Streaming Batch is still alive, but streaming is growing — not just for operational systems, but for powering real-time insights and AI agents. Teams are learning to design smarter streaming architectures with observability and scale in mind. ❗Supporting AI, RAG & LLM Pipelines AI systems are only as good as the data behind them. Data engineers are now working directly with AI teams to build better ingestion, implement retrieval-augmented generation (RAG), and maintain clean, tagged, documented sources that models can trust. ❗Communication & Documentation Hard truth: even the best pipeline fails without adoption. That’s why data engineers are documenting more, evangelizing quality, and explaining to stakeholders why their “quick fix” is a long-term liability. ❗The New Tools = Efficiency First It’s not about having more tools — it’s about doing more with less. Open-source orchestration, smarter APIs, and tools like Claude or Copilot are helping teams scale without burning out. We’re entering a phase where the value of data isn’t just in having it, but in how well your engineers can deliver the trust. Follow the trends or risk making decisions based on noise rather than knowledge. 🔍̲📊̲ ̲𝚃̲𝚞̲𝚛̲𝚗̲𝚒̲𝚗̲𝚐̲ ̲𝙳̲𝚊̲𝚝̲𝚊̲ ̲𝚒̲𝚗̲𝚝̲𝚘̲ ̲𝙳̲𝚎̲𝚌̲𝚒̲𝚜̲𝚒̲𝚘̲𝚗̲𝚜̲📈̲ If you’re curious about how to make your data work better for you, write to me! hashtag #Reenbit #TalkAboutData #TalkAboutPowerBI #DataEngineering #powerbi

🧬 𝐖𝐡𝐲 𝐀𝐫𝐞 𝐖𝐞 𝐒𝐭𝐢𝐥𝐥 𝐑𝐞𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐢𝐧 2025? My colleague Jesse Paquette hits the nail on the head: "𝘐𝘯 𝘵𝘸𝘦𝘯𝘵𝘺-𝘧𝘪𝘷𝘦 𝘺𝘦𝘢𝘳𝘴 𝘪𝘯 𝘵𝘩𝘦 𝘓𝘪𝘧𝘦 𝘚𝘤𝘪𝘦𝘯𝘤𝘦𝘴 𝘪𝘯𝘥𝘶𝘴𝘵𝘳𝘺, 𝘐'𝘷𝘦 𝘰𝘣𝘴𝘦𝘳𝘷𝘦𝘥 𝘤𝘰𝘶𝘯𝘵𝘭𝘦𝘴𝘴 𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘢𝘵𝘪𝘰𝘯𝘴 𝘢𝘥𝘰𝘱𝘵, 𝘳𝘦𝘷𝘪𝘴𝘦, 𝘥𝘪𝘴𝘮𝘢𝘯𝘵𝘭𝘦, 𝘢𝘯𝘥 𝘳𝘦𝘣𝘶𝘪𝘭𝘥 𝘵𝘩𝘦𝘪𝘳 𝘥𝘢𝘵𝘢 𝘪𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦" – and we're still doing it. I completely agree: "Data systems designed for storage, not usage, remain the single biggest obstacle to progress." As we enter the era of Agentic AI, this becomes even more critical. We're at a crossroads where AI agents need seamless, intelligent access to data – not another monolithic system wrapped in clever system prompts. 🔑 The Path Forward: ✅ Modular Data Products – Each data source becomes a self-contained, API-accessible product ✅ Protocol-First Design – Standards like MCP (Model Context Protocol), A2A, and Agentic API Gateways ✅ Federated Intelligence – AI agents that can discover, access, and combine data sources dynamically ✅ Usage-Driven Architecture – Design for how data will be consumed, not just stored We believe the future isn't another centralized AI brain – it's an intelligent mesh of specialized agents working with purpose-built data products. What are your thoughts on moving from data warehouses to agentic data meshes? Please feel free to let us know (https://lnkd.in/ei8s4-tT) #DataMesh #AgenticAI #LifeSciences #DataArchitecture #AI #MCP https://lnkd.in/eyKCAyw3

Every Enterprise knows the pain: data scattered across warehouses, lakes, and apps. Insights delayed. Fraud undetected. Customers misunderstood. AI Agents limited. => Data fragmentation doesn’t just cost efficiency—it costs opportunities. We are thrilled to announce that 🌐 Neo4j Infinigraph Has Launched! 🚀 Run both transactional & analytical workloads at 100TB+ scale — no ETL, no duplicates, no compromise. 🔑 One connected graph. Infinite context. AI doesn't just need data, it needs knowledge. Neo4j Infinigraph gives your agents the context to perform at scale. More details here: https://lnkd.in/eUH_2TiT