Sumo Logic Offers Updates to Machine Data Analytics Platform

Sumo Logic has announced the availability of a new Transaction Analytics offering as a component of its Sumo Logic analytics platform to deliver visibility into causal relationships across distributed IT systems in order to better inform business decisions. 

Powered by the Sumo Logic service that scans more than five petabytes of machine data per day, Transaction Analytics takes machine-learning algorithms to the next level by unveiling causal relationships between events as they occur. The Sumo Logic service is powered by patent-pending Elastic Log Processing and LogReduce technologies, and transforms machine data into actionable insights for IT operations, application management, and security/compliance teams.

Transaction Analytics help businesses quickly ascertain how various events, such as revenue, security or compliance, are correlated. With the proliferation of data sources and increasingly complex interactions between them, understanding these event relationships is fundamental to optimizing customer interactions, business processes and security procedures. The Sumo Logic Transaction Analytics capabilities help define and visualize the flows of each transaction, providing real-time and compelling business insights.

 

Intelligent Analytics offers real-time dashboards, pattern-recognition and anomaly detection capabilities. Using the platform provides the ability to uncover the relationships between disparate events inside highly complex applications.

- Reduce MTTI (mean time to identification) and expedite root cause analysis by surfacing components of transactions across distributed environments

- Automate processes for collection and analysis of transactional context to decrease time associated with compiling and applying intelligence 

- Real-time transaction analysis helps identify and remediate issues before they impact critical systems 

- Drive strategic and informed business decisions by identifying core-user behaviors

- Clear visualization to clearly outline complex transactional relationships in real-time