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Sumo Logic Launch Outlier Detection

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DateThursday, April 16, 2015
AuthorLeonid Botnarenko

Sumo Logic Launches Outlier Detection

Sumo Logic Claim Outlier Detection and Predictive Analytics Rapidly Identifies Abnormal Behavior 

Bruno KurticSumo Logic launches Outlier Detection and Predictive Analystic, than rapidly identifies abnormal behavior  Sumo Logic (@SumoLogic), the cloud-based machine data analytics service, has launched Outlier Detection and Predictive Analytics capabilities that augment its rich and proven machine learning and Anomaly Detection engine with statistical analysis and projection models.



The company's outlier detection capability is powered by an algorithm that can analyze thousands of data streams with a single query, adding outlier detection and predictive analytics capabilities to its cloud-based data analytics platform in real-time. 



Dante Shareiff, Senior DevOps Engineer, TuneIn said: "Outlier Detection represents an enormous opportunity to our teams to rapidly identify abnormal behavior in our KPIs, including response time, exception rates and network traffic, the ability to visualize and detect abnormal characteristics across multiple data sources eliminates the guesswork to identify the root cause of spikes and errors and helps expedite troubleshooting to keep our service running smoothly."

The powerful capabilities make Sumo Logic's service offering all-inclusive with built-in Pattern Detection, Anomaly Detection, Transaction Analytics, and now Outlier Detection and Predictive Analytics. The full spectrum capabilities empower DevOps, IT Operations and Compliance and Security teams to gain real-time visibility across thousands of data streams and detect and predict conditions that indicate an onset of performance, reliability or security issues within their apps and services.



Sahir Azam, Director of Product Management at Sumo commented: "Analyzing trends, predicting violations and abnormal behaviors can also guide organizations when planning and executing critical strategic and operational decisions, and become especially critical for customer-facing and revenue-generating apps, advancements in machine learning and predictive analytics are making it possible for organizations to optimize business and security processes to determine and address impending issues rather than running in place to continuously fix them."



The predictive analytics capability extends and complements Outlier detection by predicting future KPI violations and abnormal behaviors through a linear projection model. The ability to observe violations that may occur in the future, such as declining transaction volumes, rise in latency, and decrease in available application resources, helps users address issues in advance of business impact.


Azam added: "You can expect continued innovation and sophistication in our analytics capabilities as we move toward helping customers better manage and secure their applications and services."


Purpose-built visualization highlights abnormal behaviors giving Operations and Security teams visibility into critical KPIs (Key Performance Indicators) and KRIs (Key Risk Indicators). Real-time alerts help teams react to and remediate critical issues as they are detected, such as a sudden rise in response time, unusual spike in network traffic or drop in request volume. Users can customize simple input parameters to manage sensitivity, baselines, direction and duration of change.



Sumo Logic help over 500 customers including Netflix, McGraw-Hill and GoGo Inflight gain instant insights into the growing pool of machine data within their complex environments. The company's Founding Vice President of Product and Strategy, Bruno Kurtic (pictured), mentioned: "Detecting an issue in behavior of a single host that indicates the onset of a larger problem or predicting a critical event a few minutes into the future can mean the difference between a system-wide outage and a minor operational procedure that goes unnoticed."


Key Features and Benefits:

Outlier Detection
• Monitor multi-dimensional KPIs with dynamic thresholds
• Simple parameter tuning to customize KPI-specific analysis
• Purpose-built outlier visualization
• Real-time alerts on outliers


Predictive Analytics:
• Linear predictive analysis
• Real-time alerts for predicted future events


Both Outlier Detection and Predictive Analytics are currently available for Sumo Logic Enterprise Edition




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