2 The role of data stewardship in AI transformation: Origins, quality, and reliability Data quality: Data reliability: Quality drives the value Ensuring trustworthy data of AI responses Data reliability means data consistently Data quality directly affects the reliability meets quality standards and is available when needed. For AI-powered businesses, of AI outputs. High-quality data provides the context for AI models to deliver reliable data is crucial for promoting trust correct and valuable responses to user in AI tools and the decisions they inform. Ensuring data reliability involves: inputs. Key aspects of data quality include: Accuracy: Data must correctly Data redundancy: Implementing represent real-world conditions. backup systems that prevent loss and increase availability. Completeness: All necessary data must be present and accounted for. Regular backups: Conducting frequent backups to safeguard Consistency: Data must be consistent against data corruption or loss. across different systems and over time. Monitoring and alerts: Setting up Timeliness: Data must be up to date monitoring systems to detect and alert stakeholders to data issues in and available when needed. real time. Disaster recovery plans: Developing and testing plans to recover data and resume operations quickly after disruptions. 10