Hello! I fell into data from a background in experimental psychology, education, and survey research. Now working in a NFP with no IT team, with data management, data governance, SharePoint modernization, and data analytics as part of my responsibilities. Then change management. Looking to hear people’s knowledge and experience to grow my understanding and data strategy for my organization.

Someone from my circle just posted an interesting position in pharma data governance: https://takedareferrals.erinapp.com/newreferral/e4591639-282f-4932-b37c-0b40539a3a06/fc853cbe-1bd7-418c-a300-93c4eb1b42ab/EN

From this article. Published on April 28, 2026, the World Economic Forum’s latest analysis highlights a critical shift in how global leaders perceive AI risks. For the first time, "adverse outcomes of AI technologies" has surged to the #5 spot in the 10-year global risk outlook. The root of this fear isn't just "rogue AI," but the disconnect between siloed enterprise data and automated decision-making. Workiva’s 2026 Executive Benchmark Survey reinforces this, showing that over 50% of business leaders believe "data problems"—specifically a lack of real-time access and fragmented silos—are the primary barriers preventing AI from delivering strategic impact. We are moving from an era of "Systems of Record" to "Systems of Trust," where data quality is the only currency that matters. Key Takeaways: 🔹 The Sustainability-AI Link: Organizations are successfully scaling AI by starting with one "material" data stream (like sustainability metrics) and linking it directly to financial outcomes. This creates a focused, governed feedback loop that proves ROI quickly. 🔹 Siloed Data is an AI Blocker: 28% of executives identify siloed data as their top obstacle. AI cannot "reason" across a business if it only sees 20% of the departmental context, leading to brittle and unreliable decisions. 🔹 The Board-Level Playbook: Governing "Agentic AI" (AI that acts on its own) is now a top-tier board priority. Governance is shifting from a technical checklist to a "Trust Dividend"—a measurable competitive advantage gained by organizations that can prove their AI’s data lineage. The Verdict: If your data isn't "connected," your AI is effectively hallucinating in a vacuum. The organizations winning in 2026 aren't the ones with the largest models; they are the ones that have solved the "data connectivity" puzzle. By linking environmental, social, and financial data into a single governed fabric, they turn AI from a cost center into a "Trust Machine" that drives both sustainability and profit.