AIOps For Proactive IT Operations

The Guide to Everything AIOps

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The State of AIOps 2023: Delivering Value but Falling Short of Expectations

What is AIOps?

Artificial Intelligence for IT Operations (AIOps) is an emerging discipline in the world of IT operations. But what is an AIOps platform? How does AIOps work? What can it do for an IT organization? And most importantly, how do you get started? This AIOps market guide reviews the definition of AIOps and the primary questions that teams may have as they learn how it can fundamentally transform their modern IT organizations.

AIOps stands for Artificial Intelligence for IT Operations. AIOps leverages a broad set of technologies, including machine learning, network science, combinatorial optimization, and other computational approaches, for solving everyday IT operational problems at scale. In simple terms, AIOps collects data from various sources and analyzes that data to give actionable insights to organizations.

Enterprises can address a wide variety of IT management activities using AIOps, including intelligent alerting, alert correlation, alert escalation, auto-remediation, root-cause analysis and capacity optimization. Traditional AIOps tools are not real-time and require manual intervention, however modern AIOps tools can be integrated with any existing platform. Modern AIOps not only analyzes data but also enables IT to make quick decisions based on actionable insights.

Top Trends In AIOps Adoption: The Future of Digital Operations Management?

“68% of IT decision-makers are piloting AIOps technologies to better manage the availability and performance of business-critical IT services”.

Who Are the AIOps Vendors?

There are a number of AIOps vendors whose AIOps tools address a variety of use cases. Many of these must be continuously tuned and optimized for data ingestion, while others use native application, network and/or infrastructure monitoring instrumentation to provide a richer, more contextual view of service health and incident remediation workflows. Look for robust integrations and native instrumentation while selecting an AIOps provider.

Why AIOps?

AIOps solutions help IT infrastructure teams turn data (like alert streams) into actionable insights and anticipate problems while still delivering compelling end-user digital experiences. In fact, as demands on IT continue to increase, the ability to leverage AI as a service will soon be critical to successful operations. Here’s how AIOps solutions help enterprises run and optimize mission-critical systems:

Improve Response Time For Digital Interactions

Boost key metrics for incident management including mean-time-to-detection, mean-time-to-response, mean-time-to-restoration, and incident volume handled within a service window using AIOps platforms. The combination of machine learning and data science techniques in AIOps not only delivers faster incident coordination and response but also reduces the human time spent per alert with advanced analytics and probabilistic root cause analysis.

Eliminate Siloed/Redundant Processes

AIOps solutions offer the ability to consolidate event and incident insights from different IT management tools across on-prem and hybrid, public, and multi-cloud environments. A shared AIOps platform offers centralized visibility, faster impact analysis, and improved collaboration for a diverse set of stakeholders, including application owners, infrastructure teams, and business sponsors.

How Does AIOps Help?

AIOps Adoption And The Modern Enterprise

  • Data Ingestion and Consolidation

    With greater digital infrastructure delivery in the modern enterprise, it’s only natural that ITOps teams are experiencing exponential data growth.

  • Actionable Insights

    This rise in ITOps data volume, velocity, and variety have contributed to an increase in event noise. Modern ITOps environments are constantly generating alerts for incorrect configurations, events, and more.

  • Proactive Service Availability and Health

    IT professionals are now drowning in ‘alert storms’ that negatively impact service availability and increase resolution time for IT outages. AIOps platforms will help navigate these alert storms and escalate mission-critical alerts to the proper teams for remediation and uptime restoration.

The OpsRamp State of AIOps Report

The Signal in the Noise: The truth on how AIOps is truly impacting business performance.

AIOps Use Cases

In order to understand the true impact of AIOps solutions, OpsRamp recently published “The State of AIOps” Report that is based on data from AIOps practitioners who are currently using machine learning-powered event management analysis. This survey identified can help to identify the most popular high-impact use cases for quick AIOps, including:

How To Implement AIOps?

In order to implement AIOps, it is first important to identify the key problems that your enterprise is trying to solve.

Here are five essential steps any organization should undertake before adopting AIOps:

Define how AIOps solutions will be used

More than the “what” or “how”, it is more important to understand the “Why” of AIOps.

Set success benchmarks

There must be certain benchmarks set in place to determine whether the AIOps investment will be worthwhile and at the same time provide validation on effectiveness and accomplishment of the use case.

Segment data that matters

IT leaders need to focus on the specific data that matters to realize the full value from an AIOps investment.

Make an adaptable data collection and analysis plan

Collecting the right data requires a comprehensive, well-thought-out data aggregation plan that enables any company to become an AIOps-powered enterprise.

Setup the automation

Once you’ve identified the data, it’s time to automate as much as possible and replace the routine tasks which are normally

Rollout the solution

Finally, it’s important to build team momentum for this new approach to routine task elimination with AIOps tools.

What’s the Future of AIOps?

AIOps tools ingest a wide variety of data (logs, metrics, APIs, and text) to analyze historical behavior and predict future IT performance. Most enterprises today use AIOps to handle anomaly detection and root cause analysis. In the future, machine-learning powered insights will help transform IT operations and overall business performance by overcoming the complexities of the day-to-day IT management and free up room for greater enterprise innovation. What’s driving accelerated AIOps adoption over the next five years? Two chief developments call for a new way of doing things:

How is OpsRamp’s service-centric AIOps solution better than the average AIOps tools?

While stand-alone AIOps tools can flag critical event patterns, OpsRamp’s service-centric AIOps solution combines data, context, and insights for end-to-end incident management. With OpsRamp’s AIOps solution, DevOps teams can handle incident workflow activities like event recognition, impact analysis, root cause identification, incident escalation, and automated remediation all in a single place.

Find out how service-centric AIOps can help enterprise IT teams manage alerts and data lakes with less effort