Wondering if anyone has aligned the two governance approaches in terms of approach and policies etc

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Quick question for the governance folks here 👋 We talk a lot about data quality, lineage, access, policies… But how many teams actually have clear accountability at the compute level? In large BigQuery setups, we often see: - Pipelines still running with no downstream usage - No clear owner of slot consumption - Cost visibility that stops at “the invoice" We looked at this through the lens of slot efficiency and workload-level governance — basically how to tie compute, ownership, and efficiency together across projects. If relevant, sharing the guide here:https://mastheadata.com/slot-efficiency-guide Curious how others approach compute governance.

From this article. Workiva's 2026 Executive Benchmark Survey just dropped, and the signal is loud: 79% of executives are now prioritizing data automation and governance — not because it's trendy, but because geopolitical instability has brutally exposed their enterprise-wide data gaps. This is a cross-industry survey (finance, accounting, sustainability, audit, operations, legal), and the findings echo what Deloitte recently found in life sciences: chaos doesn't wait for your data to be ready. Key Takeaways: 🔹 Data governance is getting real budget and real teams. 73% report dedicated IT support and 71% have secured dedicated budgets for transformation. This isn't lip service anymore — it's line items. 🔹 AI is delivering, but under supervision. 91% say AI has improved the timeliness and value of their decisions. But leaders aren't going all-in blindly: 76% have internal audit teams testing their AI models, and 65% use AI only in select components of disclosures. The keyword here? Guardrails. 🔹 The C-Suite must converge. This might be the most telling stat: 96% agree that CFOs, CIOs, and CSOs must unite around a shared data governance strategy. Data governance is no longer an IT problem. It's a boardroom problem. The Bigger Picture? When the world gets more unpredictable, your data infrastructure becomes your strategic backbone — or your biggest liability. The organizations that invested in governed, automated data pipelines before the chaos are the ones making faster, better decisions during it. Let's Discuss: 💬 The Budget Test: Your company says data governance is a priority — but does it have a dedicated budget, or is it buried inside "IT maintenance"? There's your real answer. 💬 The C-Suite Alignment Gap: CFO, CIO, CSO — 96% say they must align. What percentage actually do? What's the #1 thing blocking that convergence in your organization?

From this article. A new report from StateTech (Feb 2026) aimed at government agencies reveals a universal truth for the private sector too: AI doesn't fix broken data; it amplifies it. When you feed an AI with "imperfect data" (silos, gaps, bias), you don't just get bad answers—you get hallucinations at scale. The Solution? "Minimum Viable Governance" (MVG). Stop trying to fix all your data at once. Instead: 1. Target Specific Use Cases: Don't govern for the sake of governing. Govern the data needed for that specific AI pilot. 2. Automate Quality Checks: AI eats data faster than humans can verify it. If your quality checks aren't automated, you are already too slow. 3. Human-in-the-Loop: Accountability cannot be outsourced to an algorithm. You don't need a "perfect" data foundation to start AI. You need a governed one. The difference? One is a fantasy; the other is a strategy. Let’s Discuss: 1. The "Good Enough" Trap: Are you assuming your data is "good enough" just because your current dashboards work? (Hint: AI will disagree). 2. MVG Strategy: If you had to pick just one dataset to govern perfectly today to enable an AI agent, which one would it be?