A Four-Layer Hybrid Intelligence Framework for Resource-Efficient and Sustainable Data Asset Governance Prioritization
TL;DR
A four-layer hybrid intelligence framework (FLHIF) that unifies cloud model uncertainty quantification, social network analysis of data lineage graphs, DeepSeek-V4-driven multi-agent semantic reasoning, and adaptive weight learning via proximal policy optimization, and adaptive weight learning via proximal policy optimization is proposed.
Abstract
Resource misallocation in data governance is an overlooked source of carbon emissions, since most prioritization methods disregard the environmental cost of computing and human effort. This study proposes a four-layer hybrid intelligence framework (FLHIF) that unifies cloud model uncertainty quantification, social network analysis of data lineage graphs, DeepSeek-V4-driven multi-agent semantic reasoning, and adaptive weight learning via proximal policy optimization (PPO). On a benchmark of 439 data asset instances, FLHIF achieves an NDCG@10 of 0.9500, outperforming all baselines. Beyond ranking accuracy, expert evaluation yields an interpretability score of 4.2/5.0 (ICC > 0.80), indicating that FLHIF’s recommendations are auditable as well as accurate, a requirement of growing importance for AI adoption in regulated governance settings. Holdout stability tests, sensitivity analysis, and zero-shot generalization across finance, healthcare, manufacturing, and government sectors further demonstrate the framework’s robustness across the tested benchmarks. Preliminary simulations indicate that FLHIF can reduce redundant governance operations by 15–22% in a typical enterprise setting, corresponding to an estimated annual reduction of 120–180 kg CO2-equivalent emissions per medium-sized data platform. FLHIF thus offers a systematic, resource-efficient, explainable, and adaptive approach to sustainable data governance, directly supporting SDG 9.1, SDG 11.3, and SDG 12.2/12.5.
