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Reading Time: 3 minutesA customer opens their banking app and asks about a personal loan. The bank’s propensity model ranks them as a strong candidate. There’s just one problem: their financial position has changed since the model was scored. For Indonesian banks building…
Reading Time: 5 minutesMany believe they have addressed AI sovereignty because they know where their model is running. Perhaps they use a private model, deployed in a sovereign cloud, or keep inferences within a particular region. That is a start. But it does…
Reading Time: 4 minutesAs AI systems become increasingly embedded within enterprise decision-making and operational processes, organizations require more than accurate outputs. They require intelligent systems whose behavior can be understood, inspected, governed, and contextualized within coherent semantic frameworks. In this context, explainability evolves…
Reading Time: 2 minutesThe recent Iran-Israel conflict is beginning to reshape the economics of enterprise technology. A sustained energy shock is raising fuel, transport, and input costs, hardening inflation expectations and making capital more selective, just as investors grow less tolerant of long…
Reading Time: 3 minutesDenodo did it again: For the sixth year in a row, Denodo was recognized as a Leader in the Gartner® Magic Quadrant™ for Data Integration Tools report, including the most recent one released on 12/8 of last year. “Data integration…
Reading Time: 2 minutesThis is the third post in my four-part series about AI transformation. The second covered the role of the data team, and the first covered trust. AI innovation is happening at an extraordinary pace. New models, frameworks, and tools are…
Reading Time: 3 minutesThis is the second post in my four-part series about AI transformation. The first covered trust, the most important ingredient in this transformation. As enterprises push toward AI-driven operations, data teams are facing a new mandate. In the past, their…
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: 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…
