The text discusses the evolution of data quality and management in today's AI era, where traditional approaches are no longer sufficient. It highlights four categories of data quality programs: policy-driven, ML-driven, reports and roll-ups, and anomaly detection-based systems. However, these approaches are not enough to meet the demands of modern enterprises, which require a more agentic, dynamic, and operationally embedded approach to managing data. The article proposes five key points for evolving the narrative on data management: 1) from discrete approaches to layered orchestration, 2) autonomy is a goal but agency is the system, 3) static metadata can't keep up with dynamic data systems, 4) data quality is only one thread in a much larger system of interdependencies, and 5) Acceldata's view combines observability, intelligence, and action to achieve operational excellence across the entire data stack. The future of data management is agentic, requiring systems that reason, act, and continuously adapt to meet evolving business needs.