Improving Operational Resilience and Efficiency With AIOPs

Like the sensitive ecosystems in the natural world, today’s IT systems are complex, interconnected and subject to ripple effects when an unexpected change in the environment occurs.

While these IT systems may not be as visually stunning as an exotic rainforest, maintaining harmony and operational efficiency is extremely valuable to sustaining a functioning environment.

Implementing artificial intelligence for IT operations (AIOps), can make it less challenging to manage  your sprawling IT systems and the tsunami of data collected from the various “species” (devices, systems and applications) in your network ecosystem.

AIOps uses data analytics, machine learning, and automation to help IT teams proactively and efficiently detect problems before they become major incidents and identify the root cause of issues.

Here are some ways AIOps can help organizations:

• Data aggregation: By setting up data pipelines to collect telemetry data from logs, metrics and traces along with data from other sources such as networks, applications, databases and more, organizations can combine data in a single location, creating a common framework, breaking down silos and enabling advanced analytics to better inform decision makers about issues.
• Predictive service management: Machine learning models can analyze data, identify patterns and predict and isolate potential issues by finding events or data points that differ from the rest of a dataset or historical trends. This helps predict potential problems, defects, fraud and infrastructure failures, such as overloads.
• Faster incident response: AIOps provides event correlation to analyze real-time data to determine patterns, identify anomalies and provide immediate insight to determine the root cause of issues.
• Efficiency through automation: Automation scripts can be built to handle common issues, optimize a network, conduct predictive maintenance and more, reducing the need for human intervention, improving system availability and minimizing downtime in the event of an outage.
• Reduce operations cost: By gaining actionable insights, improving root cause analysis and reducing the amount of issue alerts through automation, AIOps can help mitigate the amount of time IT operations teams spend on routine, manual tasks and unimportant alerts.
• Improve the customer experience: AIOps can help prevent costly service disruptions and improve an organization’s mean time to resolution (MTTR), which enables a seamless, uninterrupted experience for customers.

While it may be tempting to take a DIY approach when implementing AIOps, most organizations do not have the staff needed to build a system and mature it over the years. Doing so without an extensive IT team of data scientists and software engineers puts an organization at risk at wasting valuable time and resources.

Being able to maintain the system and having the knowledge and skills necessary to keep an AIOps platform running over several years is critical; a lack of available staff and skills will eventually result in an obsolete system.

In addition, there is a significant amount of work involved to simply prep your organization for AIOps. Having the right data identified for ingestion and storage is key — and often a big hurdle. Insufficient or low-quality data hinders the ability of a platform to deliver capabilities at the heart of AIOps, such as predictive analytics and machine learning.

On the other hand, it is important to be informed before choosing an AIOps platform vendor. An AIOps platform is not something that can be set up and forgotten about — your organization needs stakeholders continually engaged with a long-term strategy in place.