As enterprises scale AI, they are inadvertently worsening an existing data crisis. The resulting surge in telemetry is overwhelming infrastructure already strained by the shift to multi-cloud environments, applications, and IoT, which is driving inflated costs. Organizations are paying for the collection, storage, and processing of massive data logs that often provide little business value….
The conversation around AI agents has focused heavily on reasoning. Less attention has gone to coordination. Enterprises that have been experimenting with agents are struggling to manage agents that share context and operate across live business systems without stepping on each other. Running a single agent on streaming data is manageable. Running several agents across…
Pure Storage made a two-part announcement this week: it is rebranding as Everpure and acquiring data management startup 1touch. This is a signal that storage vendors are racing into data management. One reason for this is that AI has shifted value from raw capacity to control over how data is activated. Owning the data layers…
wuiAI companies want licensed video training data because their models are evolving beyond text and images. Media companies have millions of hours of video data. However, what has been missing is the infrastructure to turn raw footage into datasets that can be used at scale. Versos AI says it has built that missing layer. The…
When an enterprise agent gives an incorrect or illogical output, most people assume it’s a problem with the model, or the prompt wasn’t clear enough. In some cases, they might just blame the data platform vendor. Too technical. Too complicated. However, what if the model did exactly what it was told and there was nothing…
Agentic analytics platform ThoughtSpot launched its next generation of Analyst Studio, which it claims offers a “new suite of capabilities to revolutionize how data teams deliver AI-ready data with speed, flexibility and control.” The analytics market has spent the last decade obsessing over usability. They wanted faster dashboards and queries, while keeping the interface easy…
Artificial intelligence dominated discussions at Davos this year, but inside enterprises the conversation has shifted from excitement to execution. Organizations are moving quickly to deploy agents and automation. However, many are discovering that technology alone does not guarantee operational value. Against that backdrop, Neal Ramasamy, Chief Information Officer at Cognizant, shared his perspective with BigDATAwire…
At BigDATAwire we outlined the key data challenges that will define the Genesis Mission. There is a growing acknowledgment that scientific AI often breaks down at the data layer. Fragmented datasets and uneven metadata introduce friction that no model alone can overcome. Federated access rules and mismatched computing environments add to the challenge. While the…
The hidden costs of traditional databases often force developers to create custom infrastructure for managing workflows. This is a critical resource conflict. Instead of focusing on building new products or advancing AI initiatives, the developers spend more time solving operational problems. As enterprises move deeper into AI, this issue is pushing data teams outside their…
Agentic AI has dominated industry conversations since 2025. Adoption across enterprises is on the rise, and investment plans remain aggressive. Use cases are expanding rapidly, from simple employee assistants to advanced agentic workflows for insurance renewals or support ticket triage, to name just a few. The shift has been undeniably fast. The momentum notwithstanding, a…
