Data to decisions examines the enablement of data-driven decisions across organizations. Holistic, data-informed decisions require a multi-disciplinary approach that combines performance monitoring with traditional business intelligence and analytic technologies. In addition, data-driven decisions are increasingly delivered in the context of business applications rather than in separate, analytic interfaces.
CxOs are betting that the Return on Transformation Investments (RTI) with AI will come from efficiency, revenue and growth, compliance and risk and proactive monitoring.
Foundational model debates--large language models, small language models, orchestration, enterprise data and choices--are surfacing in ongoing enterprise buyer discussions. The challenge: You may need a crystal ball and architecture savvy to avoid previous mistakes such as lock-in.
Discover how storytelling and data intelligence intersect—from Apple evangelism to secure global insights—reshaping leadership in a digital world.
Anthropic CEO Dario Amodei said large language model personality is starting to matter, argued costs to train models will come down and agents that act autonomously will need more scale and reliability.
Amazon CEO Andy Jassy said AWS is underway building "primitive services," or discrete building blocks, for generative AI and that approach will ensure customers bring more workloads to the cloud service.
Google Cloud pitched an agent-oriented vision for generative AI at Google Cloud Next and highlighted a bevy of emerging use cases going from pilot to production.
MongoDB expanded integrations with Google Cloud's Vertex AI, BigQuery, Google Distributed Cloud and Google Cloud Manufacturing Data Engine.
Google Cloud outlined a series of services and enhancements across its platform in a bid to make it easier for enterprises to bring their data to generative AI models, build applications and deploy them at scale.
JPMorgan Chase CEO Jamie Dimon issued his annual shareholder letter and provided an incremental update on the company's artificial intelligence efforts as well as private cloud buildout.
Enterprises need to focus on data lakehouse strategies in 2024 to properly take advantage of generative AI; model architecture will be critical to managing large and small models; fine tuning is more difficult than you'd think; and CXOs were weary of database vendors glomming on to genAI hype.
