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

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?

Hi everyone 👋 I’m Abel, a software engineer currently building AI-powered tracking and automation tools (Notiqo). My focus is on: - Turning behavioral data into actionable insights - Lightweight AI systems for SMEs - Product-driven analytics (not just dashboards, but decision engines) I’m particularly interested in how others here are using AI beyond experimentation — into production environments. What are you currently building with AI that’s actually generating measurable impact?

Lots of orgs are getting excited about LLM visibility and getting tools to help them monitor how they appear for various prompts. And, because everyone and their brother is interested in LLMs, i see way more people, from far wider across the organization, wanting access to these tools than anything we've brought online before. And for each person that wants access, 5 people are asking questions they have decided can be answered by the new tool. And some orgs are doing a really bad job of controlling who gets access at what permission level. Not to mention a bad job of prioritizing ad hoc asks. For most orgs the two following steps are working at least for day to day: - I've suggested people attend 20 minutes of training or watch a 20 minute video before they get access. - Ive drafted a tiny policy about what the tool is for (monitoring progress of llm visibility initiatives, not idle curiosity) and how to tag prompts you add/check before removing prompts. But ive got an org where everyone is too busy for the training and not sure if they really need a policy. Anyone have tips or recommended reading on how to sell people in this kind of situation on the need for governance?

One of the biggest data problems is not lack of data. It’s lack of understanding. When teams don’t know what data means, where it comes from, or how it should be used, trust breaks down and AI becomes risky. I’ve just added a new resource to the classroom: Metadata & Lineage Starter Map This template helps you: - clarify business definitions, - map data flows end to end, - reduce misinterpretation, - build explainable and reusable data foundations. You can access it now in the Data Governance from Scratch classroom. As always, start small and focus on impact.