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AIOPS is here! What can it do?

AIOPS is here! What can it do?

AIOPS is here! What can it do?

You might be reading the term artificial intelligence for IT operations (AIOps) and wondering to yourself what it is and what it can do. Gartner has published a Market Guide for AIOps Platforms and released a definition for it:

“AIOps platforms are software systems that combine big data and AI or machine learning functionality to enhance and partially replace a broad range of IT operations processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management, and automation.”
Source: Gartner Market Guide for AIOps Platforms. Published: 03 August 2017.

We’ve reached a point in time with modern day technology where artificial intelligence and machine learning are being successfully used to automate traditionally manual tasks and processes in IT Operations. Automation such as anomaly detection and even automated remediation processes are being implemented as readily available tools that allow organisations to simplify operations by liberating humans from time consuming and error prone processes.

Automation can reduce time consuming tasks down to just seconds, when a machine is carrying out the practice rather than a human. It can also complete the work with far better precision.

Key elements of an AIOps approach

Implementing an AIOps approach goes beyond getting better analytics for existing data. Building the basis for a machine learning systems that will yield continuous insights requires:

  • Open data access including multiple, consumable sources of historical and streaming IT data.
  • A big data platform that can support real-time visualisation and deep queries.
  • Machine learning that refines the algorithms based on the data without human intervention.
  • Analytical algorithms that yield automated IT insights on IT data for IT purposes.

Open data access

Of the four key elements, the most critical is open data access. Core IT will always have multiple technologies and systems of record from different vendors. These will also vary across IT disciplines. Freeing data from its organisational silos for big data aggregation and analysis is perhaps the most difficult challenge facing IT teams trying to implement AIOps.

An effective AIOps platform must have a data schema that can consume data from a variety of IT sources, and structure, tag, and organize it to be useful for consistent and repeatable analysis.

What Problems Does AIOps Help You Solve?

The emphasis on AIOps platforms is in the ability to collect all formats of data in varying velocity and volume. It then applies automated analysis on that data to empower your IT teams to be smarter, more responsive and proactive—accelerating data-validated decisions. With an AIOps platform, you can:

  • Avoid costly downtime and improve customer satisfaction: Better predict sources of downtime to proactively fix problems
  • Dissolve IT silos and siloed responses: Gain value from data that’s trapped in silos to reduce downtime through accelerated root-cause analysis and remediation
  • Eliminate tedious manual tasks: Use automation to reduce inconsistency in response, eradicate errors that are hard to troubleshoot, and enable IT teams to focus more time and energy on analysis and optimization
  • Collaborate with your business peers: Work together to demonstrate the business value of strategic organizational initiatives
Digital Transformation

Through digital transformation, you can add more business value by saving tons of time and effort, and spend more time in innovation. AIOps adoption can help your organization to get end-to-end visibility into infrastructure and applications.

Faster Deployment

Almost half of the organisations dealing with IT monitoring issues, spend on an average 1 hour repairing performance issues per incident. With AIOps, you can deploy automated actions (response mechanisms) for known events with embedded business logic. AIOps makes life easier for you.

Reduced MTTD and Faster MTTR

AIOps reduces the Mean Time to Detect (MTTD) by 60%. It does so by improving business agility through identifying intelligent management layers. With AIOps adoption you can also reduce MTTR (Mean Time to Repair) by more than 50%. With faster MTTR, you can eliminate alerts, repetitive events, and respond quickly to production incidents.

Greater visibility

Get greater visibility into your enterprise, information, network, and operations infrastructure. AIOps makes sure that you have the optimal visibility that you need for your business.

Real-time analysis

AIOps platforms analyse vast volumes of IT telemetry from disparate sources, by applying various types of algorithms to the data in real time. Get used to getting real-time analysis and diagnosis of issues and actionable insights.

Alerts and notifications

AIOps reduces alert noise and by filtering and prioritizing important notifications. You can reduce operational noise across your production stack with AIOps.

Causal Analysis and Apply Analytics

With AIOps, you can conduct causal analysis and apply analytics to a broad and wide set of data. This will help you to easily identify and compare probable root causes of problematic issues.

Data-Driven recommendations

AIOps provides you with data-driven recommendations. These recommendations are based on both real-time as well as historical data to help you to carry out an informed decision-making process.

Adds value

Some of the key areas that AIOps adds value to include complete alert management, automation, machine learning, correlation, micro services monitoring, data management, advanced real-time analytics, and full-stack data monitoring.

AIOps is not ITOA

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AIOps and Automation

AIOps connects and drives automation in the hyper-complex, multi-source cloud environment. Delivering machine-assisted analytics at scale on high volumes of digital IT data is useless if the outcomes still require human intervention.

AIOps can generate workflows and measure the effects of those processes, feeding the results back into the system as data to be analysed and learned from. Additionally, AIOps should be applied by the system automatically based on the data, without the need for user intervention and decision.