The increasing complexity of IT shows the limits of
traditional, domain-centric monitoring and its IT operations management. The
flood of data generated by different IT domains can hardly be correlated in the
traditional way and consequently does not provide the necessary insights that
IT operations teams need to proactively manage their environments.
The increasing complexity of IT shows the limits of
traditional, domain-centric monitoring and its IT operations management. The
flood of data generated by different IT domains can hardly be correlated in the
traditional way and consequently does not provide the necessary insights that
IT operations teams need to proactively manage their environments.
Complexity
of Hybrid IT Environments
Many companies work with a hybrid IT infrastructure, which
consists of a large number of entities in the areas of network, computing and
storage, which are provided by various technology providers. The vendors who
deliver these technologies have their own culture, syntax, and language for
managing and interacting with other entities in the IT environment. One of the
greatest challenges for IT organizations is the complexity associated with
recognizing and understanding these different entities in a hybrid IT
environment. IT environments are changing rapidly as digital service
implementations bring newer technologies such as virtualization, hybrid cloud,
containers and microservices with them. The traditional detection tools and
mechanisms, those made available by legacy ITOA vendors are not flexible,
reliable, and complete enough to keep up with the changes that are taking place
in a multi-vendor, multi-domain, multi-technology environment. However, this is
essential for today's IT operations. Because troubleshooting, capacity
planning, maintenance and effective management depend on it.
AIOps - the
next step in IT analytics
A new class of technologies can eliminate the lack of
inaccurate IT operating data and the lack of an application-centric view of the
entire stack by using powerful algorithms for machine learning (ML) or
artificial intelligence (AI).is used to automatically identify relevant
patterns that indicate potentially disruptive operational events. It has become
essential for monitoring and managing modern IT environments that are hybrid,
dynamic, distributed, and component-based. We are talking about Artificial
Intelligence for IT Operations (AIOps), the next generation of IT Operation
Analytic (ITOA). Their value lies in helping organizations address IT
challenges on a number of fronts, including:
• Collect data from different IT units and silo-like
systems,
• to compare and analyze the flood of data from different
sources,
• present this information in such a way that it can be
quickly and easily understood by companies and offers added value,
to get the increasing complexity and dynamics of IT
architectures under control and to master the exponential growth of data.
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