EigenRules is an AI/ML-powered software designed to autonomously discover essential data quality rules directly from your datasets. By simply pointing EigenRules to your data, it analyzes and generates critical DQ rules in plain English within minutes, streamlining the data validation process and accelerating the onboarding of new data sources or applications. Key Features and Functionality: - AI/ML-Driven Analysis: Utilizes advanced algorithms to understand expected data behaviors and identify essential DQ rules necessary for validation. - Comprehensive Rule Discovery: Automatically uncovers rules across all six data quality dimensions: completeness, uniqueness, conformity, validity, consistency, and accuracy. - Plain English Output: Presents discovered rules in an easily understandable format, facilitating quick comprehension and implementation. - Rapid Deployment: Significantly reduces the time required to establish data quality rules, cutting down weeks of manual effort to just minutes. - Integration with Existing Processes: Complements existing rules by identifying gaps and augmenting current data quality frameworks. Primary Value and Problem Solved: EigenRules addresses the challenge of efficiently establishing comprehensive data quality rules without extensive manual intervention. By automating the discovery process, it empowers Subject Matter Experts to focus on higher-value tasks, reduces time-to-market for new data initiatives, and ensures robust data validation. This leads to improved data reliability, enhanced decision-making, and significant cost savings in data management processes.