AppDynamics, headquartered in San Francisco, provides a leading application performance management (APM) platform, which is used by corporations around the world to monitor the performance of their software systems.

Application owners and developers use the BizIQ feature of the APM to quickly correlate business consequences with application performance.

For example, imagine that users with Acme credit cards and hyphenated surnames are experiencing lengthy response times while making purchases on an e-commerce store. Lower customer satisfaction rates ensue, leading to quantifiable revenue risk.

BizIQ monitors this software issue, investigates the root causes of the performance bottlenecks, and delivers actionable insights. However, BizIQ is currently unable to automatically recognize unique combinations of factors, such as Acme users with hyphenated surnames that are causing issues.

Segmented Data Anomaly Detection utilizes the copious amounts of customer data collected by the APM to improve the diagnostic aspect of BizIQ with machine learning.

Leveraging cluster analysis and unsupervised machine learning, anomalies are explored across hundreds of performance metrics. This leads to the discovery of specific combinations of factors that cause performance issues.

Automating this diagnosis in parallel with data collection saves time and determines the root cause of an issue more accurately.

Segmented Data Anomaly Detection uses Node.js to pull data from the APM, and scikit-learn running on Python to perform data analysis. The results of the analysis are rendered on a web app, which will be developed using JavaScript and includes cluster visualizations powered by D3.js.