Data Governance, Quality & MDM (Master Data Management) is a detailed, strategic course designed to equip professionals with the frameworks, tools, and techniques needed to establish data as a trusted, secure, and consistent asset across the enterprise. The course begins by explaining the importance of data governance as a discipline that ensures data is accurate, available, usable, and protected throughout its lifecycle. Learners are introduced to data governance frameworks such as DAMA-DMBOK, DCAM, and the EDM Council’s CDMC, which define key roles, responsibilities, policies, and organizational structures necessary for enterprise-wide data stewardship. The course outlines the foundational pillars of data governance—data ownership, data stewardship, data quality, metadata management, lineage, privacy, and compliance—providing actionable steps to implement each. Data quality management is covered in depth, with learners mastering dimensions such as completeness, accuracy, consistency, timeliness, validity, and uniqueness. Students work with tools like Great Expectations, Informatica Data Quality, and Talend to define quality rules, build validation pipelines, detect anomalies, and generate automated alerts. Real-world case studies demonstrate how poor data quality impacts decision-making, customer experience, and regulatory compliance, reinforcing the importance of proactive monitoring and remediation strategies. The course also covers data profiling techniques to assess source systems, identify quality issues, and determine data readiness for analytics. Master Data Management (MDM) is introduced as a discipline that creates a single, authoritative source of truth for critical business entities like customers, products, employees, and locations. Learners explore different MDM implementation styles—registry, consolidation, coexistence, and centralized—and understand the pros and cons of each based on data maturity and system landscape. They build golden records by matching, merging, and cleansing records using probabilistic and deterministic algorithms. Hierarchy management, survivorship rules, and data enrichment from external sources are demonstrated to enhance entity resolution and downstream usability. The course integrates metadata management and lineage tracking using tools like Collibra, Alation, Apache Atlas, and Microsoft Purview. Students learn to document business glossaries, technical metadata, and lineage diagrams to promote data literacy and ensure transparency in data usage. Security and privacy are major focus areas, covering data classification, role-based access control, encryption, and data masking. Compliance topics include GDPR, CCPA, HIPAA, and ISO standards, with labs focused on data retention policies, data subject access requests (DSARs), and audit trails. Learners design data governance operating models, including steering committees, working groups, and stewardship networks, to ensure governance is embedded into day-to-day data operations. Metrics and KPIs are established to measure data governance effectiveness, including data quality scores, resolution times, policy adherence rates, and usage metrics. The course also introduces automation strategies using AI/ML to scale data quality checks, anomaly detection, and metadata extraction in large environments. By the end of the course, learners will be able to lead data governance initiatives, implement enterprise-grade data quality frameworks, and manage master data assets across multiple domains. They will understand how to align governance with business strategy, enable regulatory compliance, and promote a culture of data accountability and trust. This course is ideal for data governance leads, data stewards, compliance officers, and enterprise data architects.