Reading Time: 3 minutesThis is the last post in my four-part series about AI transformation. The third covered the role of the AI team, the second covered the role of the data team, and the first covered the core ingredient of trust. For…
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: 3 minutesAcross nearly every industry, organizations are racing to transform how they operate with AI. But despite billions of dollars in investment, real-world results have often been disappointing. Studies consistently show that a significant number of AI initiatives fail to reach…
Reading Time: 3 minutesIn the first part of this series, I provided an introduction to semantic modeling, covering the power of the maieutic approach. In this post, I’ll look into one of the fundamental difficulties of semantic modeling: Its ongoing, iterative nature. “My…
Reading Time: 4 minutesBusiness users want simple answers to apparently simple questions, such as “What counts as a customer, or revenue?” or “Which KPIs can I trust?” and it is up to the data professional, the data architect, or analytics leads, to create…
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…
