Systems | Development | Analytics | API | Testing

New Google Earth AI models and datasets on BigQuery and Gemini Enterprise Agent Platform help to understand our planet and its communities.

A working starter pack for finance and functional leaders — plus what to ask next once the conversation opens up.

If you’ve spent any time working with Oracle ERP data, you know this tale: your dashboards look polished, but the numbers inside them are hours or days old. The promise of modern cloud ERP was real-time business intelligence, yet most finance and operations teams are still clicking through static reports, waiting on IT for extracts, and making decisions based on business data that no longer reflects what’s actually happening.

Enterprises are facing one of the most significant infrastructure pivots in a decade. Between rising AI adoption, escalating data‑sovereignty requirements, and the industry‑wide shift away from legacy virtualization stacks, organizations are under pressure to move faster—without compromising resilience, control, or budget. Recent industry data underscores this urgency.

High-growth companies move fast. Headcount doubles. New markets open. Acquisitions close. And somewhere in the middle of all that momentum, your finance team is still manually stitching together spreadsheets, waiting on IT to refresh data, and running planning cycles that take longer than they should. The problem often isn’t your people, it’s your Enterprise Performance Management (EPM) system. The platform that served you well at $50M in revenue can become a serious liability at $200M.

The right question returns a deal name, an owner, and a dollar value. The wrong one returns a framework about pipeline health. The difference is not the model, it’s how you ask. It’s 7:47am Monday. Your pipeline review starts at 8. You have thirteen minutes to find out which deals need attention, which reps are behind pace, and whether you’re actually going to hit the number this quarter.

An open, unified data foundation is critical for reliably and cost-effectively supporting future data use cases, especially AI.