Enterprise leaders have spent two years and hundreds of billions of dollars on AI. The results have been uneven. According to McKinsey’s 2024 global survey, fewer than one in three companies report that their AI investments have generated meaningful, sustained business value. The demos tend to impress, and production tends to disappoint. The diagnosis offered…
At BigDATAwire we have covered how the race to deploy agentic AI is already heavily contested. However, the real question is whether enterprise data infrastructure is ready for it. It appears it is struggling to keep pace. Fivetran’s 2026 Agentic AI Readiness Index found that while 41% of organizations are already using agentic AI in…
The last decade of data infrastructure was built around pipelines. Move the data, transform it, store it, then do something useful with it. That approach became so normal that most teams stopped questioning it. At Cloud Next 2026, Google seemed to be leaning toward a different way of working with data, one built more around…
I was in a meeting recently with a VP of Data at a mid-size enterprise when she said something that stopped me. We were talking about her team’s quarterly roadmap, and she paused and said, almost to herself: “We have faster pipelines than we’ve ever had, and somehow decisions still take a week.” She wasn’t…
According to the State of Analytics Engineering 2026, the modern data stack is scaling fast, but unevenly. It is also growing faster than the trust and governance mechanisms designed to support it. AI is no longer experimental. It is embedded. 72% of teams now prioritize AI-assisted coding, and more than 77% of leaders are already…
Moving large-scale data across platforms, clouds, and global regions is no longer a special project for a few highly technical teams. It has become a routine operational requirement for modern enterprises. Companies now run analytics in one environment, store long-term archives in another, and build applications that must pull data from multiple locations with accuracy…
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…
Until recently, most enterprises treated real-time data as something you reached for only when absolutely necessary. It sat at the edges of enterprise architecture. However, that has changed. If you have been following our coverage at BigDATAwire, you would have seen that real-time data and analytics has been a core theme for enterprise modernization efforts…
