AIOps - Artificial Intelligence for IT Operations
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?
A successful digital transformation requires AIOps. And the push for business agility leaves an undesirable by-product in complexity—making it extremely difficult for humans to keep up. While agility is core to business innovation and customer experiences, executing to it has created a highly ephemeral state of IT workloads and processes.
Major advances in distributed architectures, multi-clouds, containers, and microservices, to name a few, have created copious, multi-dimensional data flows that create excessive noise and stifle IT’s ability to identify and resolve service incidents.
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 occur 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.
Why is AIOps important? Because it can empower your ITOps team to:
Determine the service health of mission-critical services or applications.
Gain control and visibility to spiraling consumption of cloud resources.
Accelerate MTTR with automated incident management and real-time configuration management database (CMDB) updates.
Build context-rich data lakes integrating disparate, third-party data sources.
“For AIOps to succeed, real-time data – delivered with context – is a mandatory requirement and will be the only sound basis for advanced automation and machine learning to be successfully adopted in the enterprise.”
Context-infused AIOps brings meaning to your data.
A new approach to IT operations 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, organize, and contextualize data–in real time–so that it becomes actionable?
“The seismic shift toward next-generation solutions (e.g. cloud, IoT, containerization, etc.) demands IT operations to recalibrate their monitoring and management tools and adopt an automated, service-centric approach.” – EMA Research
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 - Prepare
There are multiple aspects to data cleansing and preparation including a common data model, data deduplication, time synchronization, and a single data lake. Each contributes to the preparation of clean data. AIOps cannot succeed if data is incomplete, imprecise, or out of alignment.
Step 3 - Enrich
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 - Analyze
The vast amounts of operational data collected by IT management systems place a significant burden on operations teams and incur significant analysis cost, in terms of staffing, compute, and storage. AIOps applies machine learning to solve problems–rapidly eliminating and consolidating data where possible.
Step 5 - Action
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.
Take the next steps towards AIOps.
The final goal for enterprises should be a system that automatically predicts and addresses operational disturbances before they arise. It should then make recommendations or advise on next steps, and an operator then makes informed decisions.
AIOps is a journey. 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.
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AIOps Platform to Be Used Across Cloud and Distributed Architectures
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