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competent partner in all questions of digital transformation
Next, the AIOps platform correlates this data in a contextual form. In addition to automatic detection, AIOps identifies the relationships between all entities in a hybrid IT environment, between the infrastructure elements, between an application and its infrastructure, and between the business transactions and the applications, and quickly finds the cause of complex problems that affect the business.
Weissenberg Intelligence - your competent partner in all questions of digital transformation
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Visualization
When the end-to-end correlation process is complete, the
data must be presented in an easy-to-use format. And that is exactly the task
of visualization. Visualization is important because it enables IT operations
to quickly identify problems and take corrective action. AIOps offers a new
generation of visualization that makes data usable.
Dependency
mapping
AIOps uses automated dependency mapping to build and track
dynamic, multi-layered relationships between hybrid infrastructure entities and
thus create service and application topologies across technology domains and
across data center and cloud environments. With dependency mapping, IT teams
can accelerate incident response, quickly calculate the business impact of
outages to ensure stakeholders are informed and issues are properly
prioritized, and improve the uptime of business-critical applications, which
contributes to customer satisfaction.
Machine
learning
Finding the source of a problem is critical. But it's even
more important to identify recurring patterns and predict likely future events.
ML can correlate and analyze data from different business application and
infrastructure areas while managing the volume, speed besides variety of the
data engendered. It can identify relationships between entities, process
millions of events, and reveal patterns of events in a time series to show what
happened to aid root cause analysis. It can use current conditions and learning
to identify business-level problems, spot anomalies in expected behaviors and
thresholds, and foresee outages and performance issues. ML can even make
suggestions.
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