Read more about author Udaya Veeramreddygari. In our fast-paced, interconnected digital world, data is truly the heartbeat of how organizations make decisions. However, the rapid explosion of data in terms of volume, speed, and diversity has brought about significant challenges in keeping that data reliable and high-quality. Relying on traditional manual methods for data governance just doesn’t cut it anymore; in fact, it can even hinder progress in today's dynamic…

Read more about author Ken Ammon. In today’s hyper-competitive economy, data is a critical asset that drives innovation, strategic decision-making, and competitive advantage. However, for many mid-sized organizations, turning raw data into actionable business intelligence (BI) is challenging. The rapid pace of technological advancements, coupled with increasingly complex data environments, presents significant hurdles, particularly for those with limited resources to build and maintain their own data management and AI infrastructure.…

Read more about author Eric Herzog. The data that is stored in vector databases is key to the success of generative AI (GenAI) for enterprises in all industries. Up-to-date, private data in company data sources, including unstructured data and structured data, is what is required during AI inferencing to make GenAI models more accurate and relevant.  To make the data systematically useful for GenAI after the initial training of an…

Read more about author Tristan Stevens. Enterprise technology leaders face mounting pressure to balance competing priorities. Cloud platforms offer unprecedented innovation, potential, and scale, but regulatory compliance requirements and data control mandates create friction when trying to consolidate everything in a public cloud. And shifting to a full SaaS environment just gives up too much control for these large companies, creating conflict between performance and compliance. This tension has created…