As organizations prioritize data democratization and AI-driven analytics, ensuring high-quality, trustworthy data has never been more crucial. Data quality management is a continuous, complex process that directly impacts business intelligence, machine learning, and strategic decision-making. Without trusted data, organizations risk inaccurate insights and flawed models.
As organizations prioritize data democratization and AI-driven analytics, ensuring high-quality, trustworthy data has never been more crucial. Data quality management is a continuous, complex process that directly impacts business intelligence, machine learning, and strategic decision-making. Without trusted data, organizations risk inaccurate insights and flawed models.
Join TDWI’s October Virtual Summit for the frameworks, tools, and expert insights to help your organization advance data quality strategies and drive business success.
Register for Free and Explore Modern Strategies for Data Quality Programs
What You’ll Gain:
- 🚀 Proven Data Quality Strategies – Learn how to define and implement data quality objectives and metrics.
- 💡 Real-World Use Cases – See how leading organizations extract value from high-quality data.
- 🎯 Automated Data Quality Management – Explore technologies that support profiling, assessment, cleansing, and monitoring.
- 🔑 Metadata and Data Lineage Insights – Understand how data observability, lineage tracking, and metadata standards enhance quality.
- 🛡️ Continuous Data Quality Improvement – Build frameworks for ongoing monitoring and enhancement.
Key topics will include:
- Expanding the Scope of Data Quality Management – Addressing new challenges in AI and analytics-driven enterprises.
- Defining and Measuring Data Quality – Establishing clear objectives, metrics, and governance strategies.
- Automation and Data Observability – Leveraging AI-driven tools for scalable data quality management.
- Ensuring Data Trust for Analytics and AI – Implementing data lineage, metadata management, and validation techniques.
- Building a Culture of Data Quality – Defining roles, responsibilities, and processes for sustained improvement.