ISG Research: How AI-ready data foundation drives enterprise success

Organizations are modernizing data management with IBM watsonx.data, a hybrid and governed lakehouse platform that unifies data across domains. The Data Product Hub serves as a centralized marketplace where users can discover, subscribe to, and receive data products directly into their lakehouses, regardless of where the data originates. This simplifies delivery, accelerates insights, and supports better decision-making. Governed access and analysis are enabled through the watsonx.data console, making data sharing seamless for both providers and consumers. #IBMwatsonx #DataLakehouse #DataProducts #AI #DataManagement #HybridCloud Read more below: https://lnkd.in/gMdVUwpT

🎓 Back from VLDB 2025 in London (Sep 1–5). 1,100+ attendees from 40+ countries, 400+ papers. I joined an invited panel on Neural Relational Data to discuss how foundation models for relational data should look like: Are LLMs enough? 🔁 The big picture: AI-for-DM (LLMs and agents doing discovery, schema matching, evaluation) meets DM-for-AI (databases evolving to run AI better with agent-aware operators, vector support, and portable data products). 📈 Why 2025 is a turning point: moving from single tables to relational data with multiple tables, and ultimately Semantically Linked Tables; treating knowledge as (graph) structure of business objects & rules and more, providng provenance for grounded, auditable decisions; using synthetic corpora to accelerate training and evaluation without exposing sensitive data not only for text but also enterprise data and tasks. 🚀 What this enables: stronger demand forecasting, risk and fraud detection, next-best-action, and scenario planning; retrieval-augmented analytics over structured data that “pull” the right tables, rows, and rules on demand; explanations tied to canoncial business knowledge - not just text. 🧭 What leaders should do now: ✅ build a linked semantic layer connecting business objects, rules, and procedural knowledge; 🔎 apply retriever → LLM reranker pipelines to manage integration and schema drift with explainability; 🧪 use synthetic data strategically and keep real-world validation in the loop; 🛡️ tighten governance to provide business data for fine-tuning of foundation models on structured data. 🌐 Adjacent signals: portable, self-describing data products via open formats and protocols; readiness for agentic, speculative access patterns with caching, shaping, and accuracy/latency controls; “Lakebase” patterns unifying OLTP and OLAP on shared object stores with copy-on-write; smarter data source discovery via learned/differentiable indexes. 💡 Bottom line: the next enterprise AI wave isn’t just better LLMs and agents: It’s tabular/relational foundation models over semantically linked data, trained and served on agent-aware, portable, and governed data systems. Get the details: https://lnkd.in/eszCTisf #VLDB2025 #BusinessAI #DataManagement #FoundationModels #TabularData #KnowledgeGraphs #DataEngineering #MLOps #RAG #Lakehouse #Agents

🚀 Bridging SQL and Vector Databases: The Future of Hybrid AI Stacks As enterprises embrace AI, one challenge is clear: how do we unify structured data (SQL) with unstructured embeddings (vector DBs) to power real-time, intelligent applications? DreamFactory’s recent article highlights Data AI Gateways as the solution delivering a single interface that: ✅ Boosts AI accuracy (up to 90%) ✅ Cuts costs by reducing integration complexity ✅ Simplifies developer workflows ✅ Ensures compliance with privacy and security standards By combining SQL and vector databases in hybrid AI stacks, organizations unlock the potential for real-time analytics, scalable performance, and AI-driven insights. This aligns closely with my own work on a multi-engine data virtualization framework, which extends these principles to enterprise scale, allowing data infrastructures to orchestrate caching, MPP, and vector engines under one federated layer. Together, these innovations represent the next wave of cloud-native modernization and AI-ready data systems. https://lnkd.in/eMvnEkQ4

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

So many enterprises rely on predictive analytics to carve out their competitive edge. This is a helpful breakdown of how AI-powered data management tackles top business intelligence challenges. #BI #AI #DataManagement

🤔 What’s more valuable than data itself? The metadata that makes it usable. We’ve all heard the saying that data is the new oil. But just like oil, raw data isn’t worth much on its own. It needs context to be useful. That’s what metadata provides. It helps you understand: ✨ What the data is 📍 Where it came from and where it’s going 📊 How it’s being used 👤 Who owns it ✅ Whether it can be trusted This context is becoming critical as data teams grow, dashboards multiply, and AI tools begin generating SQL and insights on their own. Metadata is quickly becoming the most important (and most underinvested) layer of the modern data stack. Without it, teams are flying blind. With it, you’re building an AI-ready environment. Shinji Kim joins Sean Falconer on the Software Engineering Daily podcast to dive deeper into why metadata matters now more than ever: https://lnkd.in/de_5qEwy