The rise of enterprise AI has exposed a glaring weakness in traditional data governance strategies: how to measure the success of data governance. Most organizations struggle in this space. But wait, haven’t enterprises already spent years building governance dashboards and compliance frameworks to measure success? Yes, and that can help. However, such tools often emphasize…
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…
Is life sciences research still a biology challenge? With the recent advancements in AI, the real bottleneck is data. Vast amounts of biological data exist across literature, experiments, and proprietary datasets, but turning that into actionable hypotheses remains slow, manual, and error-prone. What’s missing is not more data – we know there is no shortage…
SAP is moving to fix a problem that has quietly held back enterprise AI. The company is acquiring, targeting two weak points that most organizations still struggle with: fragmented data and poor AI performance on structured datasets. At its core, the problem is simple. Enterprise AI struggles because data is often not accessible in real…
The recent move by Anaconda to acquire Outerbounds is aimed directly at a gap between experimentation and production, where workflows often fail to run consistently across environments. Instead of replacing existing tools, it introduces a layer that coordinates how workflows are defined, executed, and tracked. The goal is not just governance, but consistency. A way…
At its recent Cloud Next event, Google unveiled advanced geospatial AI capabilities that extend how imagery is analyzed and integrated into enterprise workflows. However, that was only a starting point. The tech giant has announced that in the coming weeks it will keep expanding its Imagery Insights portfolio with the launch of new Aerial and…
Have you ever wondered why the most critical step in AI, retrieving the right data, is still tied to centralized cloud systems? Even as models move closer to users, the systems that supply them with context remain largely cloud dependent. Data management and analytics software provider Actian is challenging that assumption with VectorAI DB. It…
For years, artificial intelligence has been sold as something close to magic. Feed it enough data, train a sufficiently complex model, and intelligence will emerge. Predictions improve. Decisions accelerate. The system “learns.” That story is convenient. It’s also increasingly misleading. The dominant architecture of AI today assumes a clean separation: data is raw material, models…
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…
