Responsible data and analytics is a framework that ensures the ethical, legal, societal, and environmental implications of data and analytics are carefully considered in enterprise decision-making. It encompasses data quality, privacy, security, compliance, transparency, explainability, ethics, equity, and sustainability, ensuring that data-driven insights and AI models are trustworthy and fair. According to TDWI research, enterprises prioritize governance, security, and compliance, with growing—but still limited—focus on data ethics, accessibility, and sustainability. While transparency in AI/ML models is a rising concern, organizations are still developing capabilities for bias mitigation, equitable access to analytics, and reducing environmental impact. The journey toward responsible data and analytics depends on building awareness, training professionals, and adopting best practices.