AIOps - Artificial Intelligence for IT Operations

What is AIOps?

AIOps is the application of advanced analytics — in the form of machine learning (ML) and AI, towards automating operations to move at the speed of business.  Coined by Gartner, AIOps marries big data with ML to create predictive outcomes that help drive faster RCA (root cause analysis) and accelerate MTTR (mean time to repair).

Why AIOps?

The push for business agility leaves an undesirable by-product in complexity which outstrips human capacity to keep pace. 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. 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 a goal of improving time to resolution.

Adopting AIOps Empowers IT Operations 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 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.”

Read ScienceLogic Whitepaper – Unlock the Power of AIOps >>

 

 

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 enrich 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, such 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.

 

Read EMA Analyst Report – How to Enable IT to be a Change Agent >>

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. It brings additional insight to raw data by adding meta-data related to device or service metrics. AIOps needs Infrastructure, Application, and Business Service Mapping 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 non-actionable data, 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.   

ScienceLogic SL1 Platform

Automation Engine for AIOps Across Cloud & Distributed Architectures
Visibility, context, and action across your entire IT operations to maximize business performance.

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