From this article : For China to lead in AI, it must first master its data foundation China generates an unmatched volume of data, but this article highlights a critical paradox: massive data does not equal meaningful intelligence. While the country has the raw "fuel," it is currently choked by unstructured formats, isolated legacy systems, and quality issues that act as barriers rather than catalysts. The strategic takeaway is clear: the next phase of the AI race won't be won by who has the most data, but by who has the most governed, clean, and interoperable data foundation. China is now pivoting to treat data quality not as an IT fix, but as a national strategic imperative—a move that defines whether they will lead or just lag in reliable AI deployment.

Hi everyone, great to meet you all! I’m Merve Deniz. My background isn’t in data engineering, but I work on Growth & Partnerships at Masthead Data; it's a platform focused on data observability and cost management on BigQuery. We recently hosted a webinar on metadata use on Google Cloud Platform, where we walked through topics like cost visibility, pipeline reliability, and AI readiness. If you’re working with GCP or planning to ramp up there, it could be a useful watch. Here’s the replay:https://www.linkedin.com/events/7391541243450654720/ I’m excited to learn from the discussions in this community and contribute where I can.Looking forward to connecting! Merve

I am currently designing an Information Governance webinar for February next year. I don't want to self-promote or spam this group, but if anyone is interested in being invited, please let me know.

Hello everyone, my name is Yulia and I am a Data Analyst & Governance Specialist at Medicins Sans Frontieres in Belgium. I'm looking forward to sharing with other warriors of data chaos and hopefully learn from each other :)

The AI Data Readiness Assessment helps you evaluate whether your data foundations are strong enough to support reliable, scalable, and responsible AI.It breaks down the five critical pillars of AI readiness — quality, governance, metadata, access, and architecture — and gives you a clear maturity score from Level 1 (ad-hoc) to Level 5 (optimized). Use this template to: ✅ Identify where your data limits your AI potential ✅ Spot gaps in governance, lineage, quality, and ownership ✅ Prioritize improvements across teams ✅ Build a realistic roadmap before deploying or scaling AI It’s a practical, no-fluff tool to ensure your AI initiatives rest on solid, trustworthy, and well-governed data foundations.