Mudanças entre as edições de "Artificial Intelligence for IT Operations (AIOps). 2018"

Edição atual tal como às 02h09min de 5 de janeiro de 2019

Market Guide for AIOps Platforms Published 12 November 2018 - ID G00340492 - 18 min read

AIOps platforms enhance IT operations through greater insights by combining big data, machine learning and visualization. I&O leaders should initiate AIOps deployment to refine performance analysis today and augment to IT service management and automation over the next two to five years.

Overview

Key Findings

AIOps is getting entrenched in enterprises predominantly for IT operations, while some of the more mature organizations are using the technology to provide insights to business leaders.

AIOps skills and IT operations maturity are the usual inhibitors in ensuring quick time to value when using these tools, followed by data quality as an emerging challenge for some of the more mature deployments.

Enterprises adopting AIOps platforms use it to enhance and, occasionally, augment classical application performance monitoring (APM) and network performance monitoring and diagnostics (NPMD) tools.

Vendors are developing strategies to use machine learning — the primary technology within AIOps — to analyze data challenges for IT operations across the three dimensions of volume, variety and velocity. At the same time, they are building specialization across both data storage and AI practices.

Recommendations

I&O leaders responsible for optimizing IT operations should:

Deploy AIOps by adopting an incremental approach that starts with historical data, and progress to the use of streaming data, aligned with a continuously improving IT operations maturity.

Select platforms that enable comprehensive insight into past and present states of IT systems by identifying AIOps platforms that are capable of ingesting and providing access to text and metric data.

Deepen their IT operations team’s analytical skills by selecting tools that support the ability to incrementally deploy the four phases of IT-operations-oriented machine learning: descriptive, diagnostic, proactive capabilities and root cause analysis to help avoid high-severity outages.