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Avoid “IT Firefighting” With AIOps

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Avoid “IT Firefighting” With AIOps

Outdated domain-centric tools leave IT specialists unable to proactively troubleshoot and repair system issues.

· AI Zone ·
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In today’s 24x7 IT environments, nothing is more important than avoiding system outages and slowdowns that impact business. Without ready access to the desired applications, frustrated workers and customers are unable to complete their transactions. Business grinds to a halt, revenue is lost, and corporate reputation is damaged.

But manually detecting and diagnosing system glitches across a multi-layered, siloed infrastructure is time-consuming and cumbersome. Outdated domain-centric tools leave IT specialists unable to proactively troubleshoot and repair system issues. Your IT team can end up “fighting fires” rather than working on the important projects that add value to the business.

Solution: Predictive Analytics for Continuous Oversight of IT Operations

Predictive analytics, an emerging category of big data analytics, can help organizations predict future outcomes based on historical data. When reviewing the data, analysts can detect trends and patterns that may highlight risks, correlations, or current as well as future conditions.

Already used for applications such as inventory forecasts and customer service, predictive analytics can uncover abnormal trends, detect threats, and forecast issues before they impact operations and create emergencies. Examples include:

  • Multi-variate anomaly detection can identify anomalies in applications behaving abnormally. For example, utilization spikes on Monday are normal, while a similar surge on Sunday may indicate a security threat.
  • Capacity prediction — Don’t pay for unused servers or be caught short-footed by unanticipated server demand. Use analytics to forecast and optimize system resources usage, while minimizing your operational footprint.
  • Incident prediction — Predictive analytics, enhanced by data mining, can help analysts interpret the structured and unstructured data recorded in tickets. The results can be used to highlight and fix potential failures.

AIOps: Fixing IT Problems Before They Happen

Powered by Machine Learning, AIOps has advanced analytical capabilities to help IT organizations forecast and avoid system issues. Using its Artificial Intelligence capabilities, AIOps can be taught to observe and recognize patterns and anomalies over time. Then it can automatically analyze massive amounts of digital data, correlate leading indicators, and use historical behavior to help predict what could happen next. By delivering contextual operational insight, AIOps can help your team predict and prevent business outages before they cause actual problems.

AIOps can be taught to examine data trends and provide an early warning whenever it discovers possible issues. The application can detect trends and patterns within the “noise” of millions of system incident reports, highlighting potential risks and performance issues.

When system outages occur, the predictive insights from AIOps can speed up root-cause analysis and remediation. By quickly and accurately diagnosing the root cause, problems are fixed faster, often reducing downtime from hours to just minutes. The result is optimized system availability and enhanced business operations.

Conclusion

Depending on the platform, AIOps can quickly predict business application issues across an enterprise’s entire IT stack. Using its end-to-end view across all domains, AIOps helps customers rapidly identify issues and predict outages, so they can resolve problems proactively and avoid IT firefighting.

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Topics:
aiops ,artifical intelligence ,it issues and ai ,it firefighting ,how to fix it problems with ai

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