What is AIOps?

You’ve probably heard this term used over and over again as the next big thing in IT management. And maybe you’ve read something about it having a significant impact on system operations and administration in numerous trade publications. But what exactly is AIOps?  And why should you care about it?

AIOps or artificial intelligence for IT operations is a term first coined by Gartner. It is the application of advanced analytics—in the form of machine learning (ML) and artificial intelligence (AI), towards automating operations so that your ITOps team can move at the speed that your business expects today.

AIOps marries big data with ML to create predictive outcomes that help drive faster root-cause analysis (RCA) and accelerate mean time to repair (MTTR). By providing intelligent, actionable insights that drive a higher level of automation and collaboration, your ITOps can continuously improve, saving your organization time and resources in the process.

Why is AIOps important?

And because there are so many different layers of technologies making up your IT infrastructure, there are an increasingly complex set of dependencies between these technologies. Adding to the complexity, your IT infrastructure is shared across an ever-expanding set of business services and applications. Any type of change to one of these services, applications, or the underlying infrastructure occurs so fast and frequently that we are beyond the point where humans can figure out how these parts are related. We need a machine to do it for us.

AIOps builds real-time systems in the form of context-rich data lakes that traverse the full application stack in order to reduce noise in modern performance and fault management systems and drive automation—with the ultimate goal of improving time to resolution.

data automation

Why You Need AIOps Now More Than Ever

And because there are so many different layers of technologies making up your IT infrastructure, there are an increasingly complex set of dependencies between these technologies. Adding to the complexity, your IT infrastructure is shared across an ever-expanding set of business services and applications. Any type of change to one of these services, applications, or the underlying infrastructure occurs so fast and frequently that we are beyond the point where humans can figure out how these parts are related. We need a machine to do it for us.

AIOps builds real-time systems in the form of context-rich data lakes that traverse the full application stack in order to reduce noise in modern performance and fault management systems and drive automation—with the ultimate goal of improving time to resolution.

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Your Guide to Getting Started with AIOps

When there’s lots of buzz around a new technology, it’s easy to suspect there’s a lot of buzz and hype to follow. And often you’d be right, but with some effort, you can find the truth. It’s no different with the buzz around AIOps, but knowing what’s what and where to start can be quite the effort.

Buzzwords aside, it looks like AIOps is going to be a permanent fixture. In fact, a recent study by Forrester states that “68% of companies surveyed are actively investing in AIOps-enabled monitoring solutions within the next 12 months.”

 

AIOps: A Journey to Fully Automated Operations

Ask yourself these questions:

  • Is your organization ready to migrate to the cloud?
  • Do you need large capacity scalability?
  • Do you need help growing IT to support the variety of tasks essential to the success of your organization’s goals?
  • Does your future include automation and a need for smart, predictive analytics?

If you answer yes to the following questions, you are ready for AIOps to help you meet the challenges of today and the challenges you may face in the future.

AIOps

The final goal for enterprises is a system that automatically predicts and addresses operational disturbances before they arise. Your system should then make recommendations or advise on the next steps, and an operator can then make more informed decisions. The AIOps journey is not for the faint of heart, but the results are well worth the investment over time. And every journey begins with a single step. At ScienceLogic, we have created a maturity model to help our customers and partners think through their current starting point on the AIOps journey. And we’re here to help you each step of the way.

Context-infused AIOps brings meaning to your data.

A new approach to IT operations and operations management is needed–one that works at machine speed. But to transform operations, IT leaders must commit not only to collecting data but to enriching data quality with context, enabling automated outcomes.

So, the central challenge of AIOps is: How do you collect, consolidate, and contextualize data–in real-time–so that it becomes actionable?

5 Steps Towards Actionable, IT Operational Data

Step 1 - Collect

Data collection includes the initial and continuing discovery of data from various sources–including agents, devices, applications, and services–while the collection process itself should match the type of asset being monitored. AIOps demands continuous knowledge of the current state and health of the IT environment.

Step 2 - Consolidate

There are multiple aspects to data cleansing and consolidating, including a common data model, data deduplication, time synchronization, and a single data lake.  AIOps cannot succeed if data is incomplete, imprecise, or out of alignment.

Step 3 - Contextualize

The most critical element of data enrichment for AIOps is context. Because context brings additional insight to raw data by adding meta-data related to your device or service metrics, infrastructure, application, and business service mapping should be in place in order to be successful.

Step 4 - Advise

The vast amounts of operational data collected by IT management systems place a significant burden on operations teams and incur significant analysis costs, in terms of staffing, compute, and storage. AIOps applies machine learning to solve problems–rapidly eliminating and consolidating data where possible.

Step 5 - Autonomize

Once data is collected and organized with context, decisions can be made with real insight, based on timely and accurate data. Automated actions can be initiated to make changes, recommendations, or notifications to ecosystem components or users. AIOps empowers automation made possible by context.

The ScienceLogic SL1 AIOps Platform

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