Data Quality Modernization: Evolving from Rules to Unsupervised Monitoring
Webinar Speaker: David Loshin, President of Knowledge Integrity
Date: Wednesday, May 31, 2023
Time: 9:00 a.m. PT / 12:00 p.m. ET
As organizations accumulate both acquired and newly built data products in their cloud-based data lakes and lakehouses, data quality becomes critical for ensuring data consumer trust. Yet the anticipated scale and breadth of cloud-based data can overwhelm the traditional approach to data quality management, which relied heavily on predefined rules and thresholds to identify and correct errors in data.
The advent of machine learning and AI techniques is rapidly influencing a modernized approach to data quality management. Advanced techniques for pattern recognition and inferencing enables the unsupervised monitoring of data and automating the detection and correction of data flaws without relying on predefined rules. In this webinar, we consider how influential guidance in data quality management coupled with modern AI/ML techniques has informed and modernized data quality management. We then discuss the role of unsupervised monitoring in continuous data quality improvement.
Attendees will learn about:
- Historical approaches to data quality management
- Scalability, explainability, and sustainability of data monitoring
- Pillars of data quality: validation rules, metric anomalies, and unsupervised data monitoring
- Establishing unified governance for data, analytics, and AI