Scaling Data Quality for Trusted Machine Learning, AI and Analytics

Successful business initiatives today rely on higher volumes of data — from more sources than ever — processed across divergent, hybrid architectures. And, of course, the quality of that data must be trusted. With recent studies indicating that 80% of AI and machine learning projects are failing due to data quality related issues, and most business executives don’t fully trust their data, it’s critical to think holistically about this fact. This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage. To have confidence in decision-making, regulatory compliance and more, enterprises require data quality tools that can handle these growing and complex data sets. Join this webinar to learn about the latest advancements in industry-leading data profiling and data quality at scale, designed specifically to meet the challenges presented by today’s data environments. Topics to be covered include: • Understanding and addressing critical data quality issues and requirements • Getting insight into big data, relational, and text-based data sources for rapid understanding of your data sources • High-scale, high-performance execution for critical data quality processes including global data enrichment and multi-domain entity resolution • Consistent data quality processing across traditional, distributed and cloud environments