Anomaly Detection for Data Quality and Metric Shifts at Netflix | Netflix

Recorded at DataEngConf SF17 in April, 2017 In the course of transforming, publishing and visualizing data, there’s risk of “bad data” creeping into your output at every turn, hurting data credibility and distracting teams from investigating real metric shifts. How does Netflix prevent bad data from causing bad decision-making? We use a variety of techniques to automate the basics, allowing us to focus our energy on the changes in data that indicate real problems with the Netflix product. Hear examples of 1) the checks we impose at multiple steps of the data pipeline to identify source data quality issues and business metric shifts, 2) techniques for anomaly detection on datasets with many dimensions that are highly cardinal, 3) how to set up evaluations in an automated fashion and 4) how we make it easy for humans to investigate issues. ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers. Make sure to subscribe to our channel for more videos, including DC_THURS, our series of live online interviews with leading data professionals from top open source projects and startups. FOLLOW DATA COUNCIL: Twitter:   / datacouncilai   LinkedIn:   / datacouncil-ai   Facebook:   / datacouncilai   Eventbrite: https://www.eventbrite.com/o/data-cou... - 🎟️ GET YOUR TICKET TO AI COUNCIL 2026 🎟️ Meet the world's top AI infrastructure minds where architects of AI share what works. Three days of high-quality technical talks and meaningful interactions. → https://aicouncil.com/sf-2026 ⚡ FIND US: X: https://x.com/AICouncilConf LinkedIn:   / aicouncilconf   Website: https://aicouncil.com/