In the Media

Alexey Sidorov, Data Science Guru and Evangelist at Denodo, explores how well-governed, reusable data products are becoming the backbone of trustworthy AI, why many initiatives stalled due to weak foundations, and how expectations are resetting around value and accountability.

Per molti istituti finanziari, offrire servizi di online banking è stato a lungo considerato un traguardo. Oggi, però, non basta più e competere con operatori nativi digitali richiede basi molto più solide, a partire da un’infrastruttura capace di fornire dati affidabili, integrati e disponibili in tempo reale. Solo così una banca può davvero guardare avanti.

Data management company Denodo has been named a Databricks Validated Partner, marking another step in the companies' collaboration to help enterprises accelerate AI and analytics with quality data.

For financial institutions, AI has moved from being an experimental technology to an operational necessity, driven by the need for real-time decision-making, automation and cost efficiency pressures. A recent 2025 MIT Technology Review Survey found that around 70% of banking institutions are using agentic AI to some degree, with 56% of executives using it for fraud detection and 51% for security.

Artificial intelligence deployments will shift from experimental pilots to tightly governed, outcome-focused systems in 2026, according to Denodo executive Errol Rodericks, who expects "Outcome-First AI" to dominate corporate strategies.

In part one of this series, Sun Life’s VP of Enterprise Data and Analytics Enablement, Chris Goodale, outlined how the company strengthens data foundations, governance, and culture to support generative and agentic AI. In this second installment of his conversation with Jason Sturgess, Regional VP and General Manager at Denodo, Goodale explores Sun Life’s early GenAI wins, customer-facing innovations, and the company’s balanced approach to modern data architecture.

Now that AI has broken out of the research lab and entered the mainstream, companies naturally expect to be able to deploy it in the cloud, where it can scale and enjoy unrestrained adoption. Unfortunately, for every company that has been able to successfully deploy AI in the cloud, there are many many more that fail to do so. In this article, I’ll explore why I think that is, considering AI and cloud as discrete technologies with their own challenges, and then I’ll propose logical data management as a strategy for unlocking this needed agility across both of them.

Denodo has been named a Leader in Gartner's 2025 Magic Quadrant for Data Integration Tools, marking the sixth year in a row that the data management firm has secured the position.

The latest Gartner Magic Quadrant (MQ) for Data Integration Tools report explains how growth in the market is set to decline as data management platforms converge.

C-suite leaders agree: scaling AI to create business value is a top priority. But despite widespread experimentation, a growing “GenAI Divide” is emerging. Research shows that 95% of custom enterprise AI tools never make it to production — not because of the model, but because of brittle data workflows and a lack of contextual learning.