Why Real-Time Became the Default — and How Data Teams Are Actually Using It

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…

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How Infrastructure Is Reshaping the U.S.–China AI Race

When you think of the global AI race, what comes to mind? Models, benchmarks, and faster training? Well, those still matter, but the race depends more on infrastructure – think data centers and power grids.  While many countries are investing heavily in this space and are slowly catching up in the AI race, the U.S….

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The Data Gravity Problem Is Back, and AI Made It Worse

Data gravity never actually disappeared, so it may not be fully accurate to say it’s back. However, it definitely stayed quiet. Traditional data analytics workloads were forgiving enough that this problem was not catastrophic. Dashboards loading slightly slower, or reports running overnight were not a disaster. The system continued to work, even if it was…

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New Study Shows How to Close the AI Readiness Gap With Trusted Data and Talent

A recent report from Precisely highlights an interesting paradox: 87% of organizations believe they are ready for AI, yet at the same time, 40% of the leaders reported that data, skills, and infrastructure remain the biggest obstacles. Precisely’s fourth annual State of Data Integrity and AI Readiness report reveals a growing disconnect in how organizations…

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