Systems | Development | Analytics | API | Testing

March 2021

Powering Algorithmic Trading via Correlation Analysis

Finding relationships between disparate events and patterns can reveal a common thread, an underlying cause of occurrences that, on a surface level, may appear unrelated and unexplainable. The process of discovering the relationships among data metrics is known as correlation analysis. For data scientists and those tasked with monitoring data, correlation analysis is incredibly valuable when used for root cause analysis and reducing time to remediation.

The Route to Automated Remediation

An abundance of information can be daunting for any company. If internal teams do not know where the data is, it might hamper their efficiency at the cost of data quality and cleanliness. From a cost-effectiveness viewpoint, organizations are likely to waste excessively by hanging on to redundant data or storing varied data in one location irrespective of their sensitivity level.

The Road to Zero Touch Goes Through Machine Learning

The telecom industry is in the midst of a massive shift to new service offerings enabled by 5G and edge computing technologies. With this digital transformation, networks and network services are becoming increasingly complex: RAN, Core and Transport are only a few of the network’s many layers and integrated components. Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks.

Correlation Analysis Explained

When you detect that something is off in your business, how long does it take you to find the root cause? The longer it takes, the more it can cost you. Correlation analysis identifies relationships between KPIs, which business teams use to accelerate root cause analysis (RCA) and mean time to remediation (MTTR). Doing it manually however can be tedious and limit your visibility.