Reading Time: 2 minutes“…greater than 80% of IT leaders believe their enterprise data is not ready for agentic AI…” – Gartner, from “Innovation Guide for AI Agents” Agentic AI holds great promise, but as this quote from Gartner makes clear, success has been…
Reading Time: 5 minutesFor years, enterprise data architecture has relied on collecting, transforming, and replicating data across multiple systems to serve different analytical and operational needs. This approach has delivered value, but it has also introduced unwanted side effects, over time: unnecessarily duplicated…
Reading Time: 3 minutesSemantic models, logical data architectures, and why Denodo is the ideal companion for traditional AI, generative AI, and RAG Artificial intelligence has made extraordinary progress in recent years. From predictive models to generative systems capable of producing text, images, and…
Reading Time: 2 minutesAI has fueled a massive hunger for data. Many data science teams operate under the assumption that “more data equals better models,” leading to a culture of collecting and storing everything possible. This pervasive mindset directly clashes with the final…
Reading Time: 3 minutesIn the age of boundless data and powerful AI models, companies are facing a stark challenge: how do you train the smartest possible AI while also respecting an individual’s right to control their data, as protected by the General Data…
Reading Time: 2 minutesThe rapid acceleration of artificial intelligence (AI) into core business functions is setting the stage for the most significant data privacy challenge of 2026: Complying with the General Data Protection Regulation’s basic privacy principles. The first post in this series…
Reading Time: 2 minutesArtificial intelligence (AI) has exited the lab and entered the boardroom, promising unprecedented efficiency and insight. Yet its transformative power is often at odds with the General Data Protection Regulation (GDPR). The core challenge is this: AI’s boundless data hunger…
Reading Time: 5 minutesThe insurance industry has spent decades refining its ability to predict risk, building sophisticated actuarial models, investing in analytics, and advancing underwriting science. But 2026 marks a fundamental turning point. Competitive advantage will no longer come from predicting outcomes more…
Reading Time: 4 minutesIn 2026, the gap between AI leaders and AI laggards in banking won’t be measured by model accuracy. It will be measured by how fast a bank can activate trusted data at the moment of decision. Banks have reached a…
Reading Time: 5 minutesWhen we talk about data architecture — whether it’s in support of analytics, AI, data science, or combinations of these and other initiatives — we usually start with technology: what’s new, what’s modern, what’s changing. But in this post, I…
