Sumo Logic, the next generation machine data intelligence company, has announced availability of Transaction Analytics as a component of the Sumo Logic analytics platform to deliver deep 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.
Businesses today face a critical challenge related to quickly ascertaining how various events – whether focused on revenue, security or compliance – are correlated. Given 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
As a rapidly growing e-commerce company, we are passionately focused on the online shopping experience,” said Josh Brown, Engineering Manager at TOBI. “As a long-time user of Sumo Logic we’re privy to how quickly we can uncover interesting patterns in our machine data that impact our application operations. With the new Sumo Logic Transaction Analytics capabilities, we now obtain much deeper insights into how customers interact with our site that will help us optimize our business processes.”
Intelligent Analytics has been central to our vision of tapping this great corporate asset known as machine data, from our real-time dashboards to our patent-pending pattern-recognition and anomaly detection capabilities,” said Vance Loiselle, CEO at Sumo Logic. “The next critical step lies within uncovering the relationships between disparate events inside highly complex applications. Transaction Analytics adds a level of nuance and insight that enterprises have never had access to in real-time.”
Transaction Analytics Key Features and Benefits:
- 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
The IT operations analytics market has grown rapidly as machine data analysis capabilities have expanded,” said Tim Grieser, program vice president for enterprise system management software at IDC. “Transactional analysis can help IT organizations discover and understand causal relationships between events to better understand service delivery components and diagnose service impacting issues before they affect end users.”
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