Core Insights - The failure of data governance has been recognized as a systemic risk factor in the financial industry, necessitating a transition from traditional human-driven governance to AI-driven governance [1][2] Group 1: Current State of Data Governance - In 2024, the volume of AI-generated data in the global banking sector is expected to surge by 470% compared to 2021, encompassing dynamic and real-time information streams [2] - Many financial institutions still rely on outdated data governance models based on manual input and static compliance, which are inadequate for modern high-speed trading and risk management needs [2] - The financial regulatory authority has officially included "data governance failure" in its systemic risk assessment criteria, indicating that insufficient governance capabilities could trigger industry-wide risks [2] Group 2: Challenges and Structural Issues - There is a significant imbalance between investment in data governance and its returns, with state-owned banks investing over 2 billion yuan annually but achieving only a 1.5x return on investment [2] - Traditional data governance practices are facing structural challenges, as resources are often wasted on repetitive tasks without translating into business value [2] Group 3: Transition to AI-Driven Governance - The financial industry must undergo three fundamental shifts: from "humans finding data" to "data finding humans," from static compliance to dynamic value creation, and from "data-driven governance" to "AI-driven governance" [3] - AI is reshaping the data ecosystem, with examples of banks and insurance companies significantly improving their operations through AI technologies [3][4] Group 4: New Governance Paradigms - The governance model is evolving from "human-led, AI-assisted" to "AI-executed, human-supervised," expanding the governance scope to include all data modalities [4] - The emergence of "Data Governance Agents" (DGA) represents a shift towards autonomous governance engines capable of decision-making and execution [4] Group 5: Strategies for Intelligent Governance - Five major challenges in intelligent data governance include technical adaptation, ownership clarification, increased privacy risks, algorithmic bias, and long ROI cycles [5] - Six strategies proposed for overcoming these challenges include building agile technology architectures, establishing clear ownership mechanisms, creating robust security frameworks, ensuring ethical governance, developing hybrid talent, and planning long-term resource investments [6]