Environmental, Social, and Governance (ESG) data are increasingly vital for strategic decision-making, yet remains fragmented and inconsistently reported across sources. Existing ESG analytics solutions typically address structuring, retrieval, or summarization in isolation, limiting their ability to capture complex, multi-entity relationships. We propose a unified ESG data processing framework that integrates knowledge graph construction, community detection, and Graph-based Retrieval-Augmented Generation (GraphRAG) to deliver structured, context-rich insights. Our system models interconnected ESG entities, applies network analysis to uncover hidden patterns, and generates traceable, decision-ready responses to complex queries. We evaluate an early implementation using a dataset of 8,350 ESG reports from 309 companies, demonstrating improvements in coverage, contextual relevance, and multi-perspective analysis. To address potential biases in ESG disclosures, we outline plans for cross-source validation and sentiment analysis. Our contributions establish a scalable and transparent foundation for ESG analytics, offering enhanced traceability, diversity, and decision support for analysts, regulators, and stakeholders.