Digital transformations can entail significant shifts in technology, such as migrating from on-site architecture to cloud services, and these complex transformations generate massive amounts of data. Data transparency is a must-have, and observability with AIOps delivers the solution. Through a unified view of data, AIOps guides DevOps and SRE teams through the swamp of information
So much data, so little time: How your observability tool can help teams make better use of data
For years, the term “digital transformation” has essentially been a buzzword used to describe numerous levels of organizational change. But digital transformation is all too real for IT teams.
Digital transformations can entail significant shifts in technology, such as migrating from on-site architecture to cloud services, and these complex transformations generate massive amounts of data. Data transparency is a must-have, and observability with AIOps delivers the solution. Through a unified view of data, AIOps guides DevOps and SRE teams through an overwhelming amount of information. Here’s how:
Superior alert correlation
There’s a difference between general observability and meaningful observability, and correlation is one of the most essential elements. In conjunction with AIOps, correlation establishes connections between multiple data sources from different areas of the IT ecosystem. Going a step further, alert correlation allows SREs and DevOps practitioners to visualize patterns across the system to ensure all applications run at peak performance.
An observability solution with AIOps uses correlation algorithms to cluster similar incidents, so DevOps practitioners and SREs can take action on the most critical, service-affecting issues. At the same time, the algorithm finds remediation strategies for recurring incidents based on previous incidents or by searching local and external resources.
Effective use of data
Digital transformations often involve significant shifts in technology like migrating from legacy and distributed systems to container-based, service-oriented environments or modern cloud architectures. Regardless, every shift in technology creates additional complexity through increased and dynamic data sources.
For example, SAP SuccessFactors, one of the world’s largest providers of cloud HCM software, was facing a cloud transformation that triggered exponential growth in transactions resulting in the manual analysis and correlation of 100,000 alerts per day. Not only was the manual data combing slowing teams down, but many were missed as end-users were identifying nearly 80% of the incidents.
Using Moogsoft, observability with AIOps provided event correlation across multiple domains, integrated with ServiceNow, and improved cross-team collaboration resulting in a 99.6% reduction in event noise and a 40% reduction in the meantime to detect incidents.
So, while more data was produced, the platform helped teams make effective use of the new data, rather than spinning their wheels to keep up with alerts.
Another area where observability with AIOps benefits DevOps practitioners and SREs is by enriching data produced through a digital transformation. AIOps algorithms analyze internal operational data, along with data integrated from third-party tools to fulfill enrichment. A robust solution, like Moogsoft, seamlessly draws requisite data from existing configurations and information repositories, creating a baseline to enrich operational information inputs to lead to effective decision making.
Providing DevOps and SRE teams with authoritative visual information like incident timelines leads to easy triage and quick remediation.
Efficiency is vital in today’s world of rapid digital transformation. DevOps practitioners and SREs are forced to move rapidly and don’t have time to slow down and manually evaluate data. Observability with AIOps provides the transparency and automation needed to maximize productivity.
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