Read more about author Joel Christner. Imagine a world where your data not only tells a story but also anticipates your next move – this is the promise of effective data management in the AI era. As organizations try to deal with vast amounts of information, three key components have emerged as essential for unlocking the full potential of data: metadata, graph representations, and vector embeddings. Metadata: The Foundation of…

Read more about author Roger Brulotte. In recent years, a large number of businesses have jumped on the wave and moved their applications and data to the cloud, with nine out of 10 IT professionals considering the cloud a cornerstone of their digital strategy. While the cloud can offer flexibility, the reality is managing cloud expenses and resulting security liabilities have become significant challenges. A staggering 94% of IT leaders report rising cloud storage…

Read more about author Donnchadh Casey. It is now well understood that integrating AI into an organization’s digital infrastructure will unlock real-time insights for decision-makers, streamline internal workflows by automating repetitive tasks, and enhance customer service interactions with AI-powered assistants. This technology will accelerate everything from purchases to personalized service requests. These benefits have made AI an essential component of modern digital ecosystems, accelerating growth, operational efficiency, and competitive advantage. …

Read more about author Yuval Perlov. The emergence of artificial intelligence (AI) brings data governance into sharp focus because grounding large language models (LLMs) with secure, trusted data is the only way to ensure accurate responses. So, what exactly is AI data governance? Let’s define “AI data governance” as the process of managing the data product lifecycle within AI systems. To keep it simple, we can break down AI data governance into…