Exclusive Report: Governing What Matters 2026
Report Overview
As enterprises accelerate AI and advanced analytics initiatives, many are discovering that traditional data governance models are no longer fit for scale. Ambitious AI strategies are colliding with fragmented data foundations, inconsistent definitions, and governance programs designed for a far simpler data landscape.
Governing What Matters explores why leading organizations are shifting away from “govern everything” approaches and toward intentional data prioritization — focusing governance efforts on the data that directly impacts regulatory compliance, executive decision-making, and high-value business outcomes.
Featuring insights from senior data leaders at Citigroup and ICBC, alongside Alation experts, the report reveals how enterprises are building data confidence by identifying Critical Data Elements (CDEs), reducing governance friction, and creating trusted foundations that enable AI at production scale.
Rather than treating governance as a compliance burden, the research shows how outcome-driven governance is becoming a strategic enabler of faster analytics, scalable AI, and enterprise-wide trust in data.
Key Findings & Takeaways
• Enterprise AI ambitions are outpacing the reality of existing data foundations, creating growing friction and inefficiency
• Traditional “govern everything” models are overwhelming data teams while failing to improve trust in critical metrics
• Analysts and data scientists often spend over 30% of their time simply finding, validating, and preparing data
• Governance programs are most effective when focused on Critical Data Elements (CDEs) tied to regulatory risk, executive reporting, and high-value outcomes
• Organizations that prioritize governance around business impact build stronger data confidence — not just documentation
• Intentional, outcome-led governance accelerates AI deployment while maintaining regulatory defensibility
• Data confidence is emerging as the foundation for scalable, production-grade AI and analytics
Report Overview
As enterprises accelerate AI and advanced analytics initiatives, many are discovering that traditional data governance models are no longer fit for scale. Ambitious AI strategies are colliding with fragmented data foundations, inconsistent definitions, and governance programs designed for a far simpler data landscape.
Governing What Matters explores why leading organizations are shifting away from “govern everything” approaches and toward intentional data prioritization — focusing governance efforts on the data that directly impacts regulatory compliance, executive decision-making, and high-value business outcomes.
Featuring insights from senior data leaders at Citigroup and ICBC, alongside Alation experts, the report reveals how enterprises are building data confidence by identifying Critical Data Elements (CDEs), reducing governance friction, and creating trusted foundations that enable AI at production scale.
Rather than treating governance as a compliance burden, the research shows how outcome-driven governance is becoming a strategic enabler of faster analytics, scalable AI, and enterprise-wide trust in data.
Key Findings & Takeaways
• Enterprise AI ambitions are outpacing the reality of existing data foundations, creating growing friction and inefficiency
• Traditional “govern everything” models are overwhelming data teams while failing to improve trust in critical metrics
• Analysts and data scientists often spend over 30% of their time simply finding, validating, and preparing data
• Governance programs are most effective when focused on Critical Data Elements (CDEs) tied to regulatory risk, executive reporting, and high-value outcomes
• Organizations that prioritize governance around business impact build stronger data confidence — not just documentation
• Intentional, outcome-led governance accelerates AI deployment while maintaining regulatory defensibility
• Data confidence is emerging as the foundation for scalable, production-grade AI and analytics
