What is anomaly detection? Anomaly detection (aka outlier analysis) is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset’s normal behavior. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance a change in consumer behavior. Machine learning is progressively being used to automate anomaly detection.

In Part 2 of this episode, Jennifer Forrester deep dives into the value and application of external data. She talks about how data warehouses help real estate developers, telecom companies, and city planners look at traffic patterns to help determine public and product demands. Rise of the Data Cloud is brought to you by Snowflake.

In our recent “Test Automation Landscape 2020” report, we found that "Quality at Speed” is now industry-standard, and the adoption of Agile and DevOps continues to increase. These two trends fuel the demand for automation QA professionals. If you are currently a manual tester transitioning to automation, our speaker’s stories will inspire action and prepare you for the journey ahead.

In this episode, Jennifer Belissent, Principal Analyst at Forrester Research, talks about driving the value and use of data and the importance of data literacy at her company. Rise of the Data Cloud is brought to you by Snowflake.

Sauce Labs solutions architect Nikolay Advolodkin continues his series on iOS automation in Safari with a discussion and a demo of some advanced debugging techniques.

A quick overview of Xray Test Management - cutting-edge test management app for Jira. More than 4.5 million testers, developers, and QA managers trust Xray to manage 100+ million test cases each month. Xray is a mission-critical tool at over 5,000 companies in 65 countries, including 137 of the Global 500 like BMW, Samsung and Airbus.