With the growing complexity of IT environments, it has become increasingly challenging to manage IT operations manually. The advent of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) has led to the development of AIOps and Digital Experience Management (DEX), which leverage ITOM capabilities to deliver even more significant benefits. Before getting into how they are interlinked, let’s look at them individually first.

IT alerts are difficult to understand, even for experienced professionals. The language of IT alerts is akin to an enigmatic code requiring fluency in dozens of observability languages to extract technical meaning and business impact from a stream of seemingly disconnected events, alerts, and notifications.

Over the past several years, one topic that has become of increasing importance for DevOps and site reliability engineering (SRE) teams is AIOps. Artificial intelligence for IT Operations (AIOps) is the application of artificial intelligence (AI), machine learning (ML), and analytics to improve the day-to-day operational work for IT operations teams.

As the IT operations environment grows increasingly intricate, businesses are starting to recognize the significance of a flawless customer experience. Customer expectations are getting higher by the day, to the point where organizations cannot afford even a few minutes of downtime or service degradation. To prevent this, they need to avoid outdated methods of operations and prevent downtime-causing issues proactively.

Eliminating errors and streamlining the incident management process are top priorities for many ITOps, NOC, SRE, and DevOps teams. With organizations using multiple tools in their IT stack, manually finding the right information at the right time becomes crucial during incident triage. By automating tasks and workflows, businesses can eliminate manual tasks that are time-consuming, repetitive, and prone to mistakes.

There’s an increasingly high cost to poor data quality today. Poor customer data costs companies six percent of their total sales, as per a UK Royal Mail study. And, as per IBM, bad data costs U.S. businesses $3.1 trillion per year. As companies transform into data-driven businesses, we witness a sharp interest in data observability.

In the previous blog in our root cause analysis with logs series, we explored how to analyze logs in Elastic Observability with Elastic’s anomaly detection and log categorization capabilities. Elastic’s platform enables you to get started on machine learning (ML) quickly. You don’t need to have a data science team or design a system architecture. Additionally, there’s no need to move data to a third-party framework for model training.

Datacom and ScienceLogic have partnered to accelerate digital transformation efforts across the public sector.

If you’re in IT operations or manage NOC, SRE, and DevOps teams, chances are your IT environment is growing complex for you and your teams to manage. Any enterprise, large or small, around the globe, is continuously changing its IT stack due to evolving business requirements and significant industry trends. But digital transformation, hybrid infrastructure, DevOps adoption, and continuous integration and continuous delivery (CI/CD) pipelines are all causing major headaches.