AIOps leverages machine learning, analytics, and big data technologies to reduce MTTR and drive the digital enterprise.
AIOps employs big data, machine learning, and analytics to help ITOps predict, find, and fix issues faster.
Digital transformation requires an automated and machine‑assisted approach
Digital business transformation is forcing IT organizations to reconsider how to ensure infrastructure and application performance. Speed, scale, and complexity brought on by multi-cloud infrastructures and digitization stress traditional rules-based performance monitoring and management. AIOps applies machine learning and advanced analytics techniques to identify patterns in monitoring, service desk, and automation data that is so vast it is otherwise beyond human comprehension. Adopting AIOps empowers IT operations to:
- Reduce event noise and prioritize the most business-critical issues to improve performance
- Support the speed of application architecture change and DevOps adoption
- Proactively identify problems and quickly drill into root cause to reduce MTTR
- Model and predict workload capacity requirements to optimize resource usage and cost
“AIOps platforms combine big data and machine learning functionality to support all primary IT operations functions through the scalable ingestion and analysis of the ever-increasing volume, variety and velocity of data generated by IT.”Source: Gartner Market Guide for AIOps Platforms, 12 November 2018
Key elements of an AIOps approach
Implementing an AIOps approach goes beyond getting better analytics for existing data. Building the basis for a machine learning system that will yield continuous insights requires:
- Open data access including multiple, consumable sources of historical and streaming IT data
- A big data platform that can support real-time visualization and deep queries
- Machine learning that refines the algorithms based on the data without human intervention
- Analytical algorithms that yield automated IT insights on IT data for IT purposes
Of the four key elements, the most critical is open data access. Core IT will always have multiple technologies and systems of record from different vendors. These will also vary across IT disciplines. Freeing data from its organizational silos for big data aggregation and analysis is perhaps the most difficult challenge facing IT teams trying to implement AIOps.
An effective AIOps platform must have a data schema that can consume data from a variety of IT sources, and structure, tag, and organize it to be useful for consistent and repeatable analysis.
Digital transformation means high volumes of rapidly changing IT data. Traditional relational data warehouses are neither scalable nor responsive enough to support the quantity and speed of digital data. Analysis needs to take place in real-time on data as it comes in – not only offline when resources are available.
An AIOps big data platform must also support responsive ad-hoc data exploration and deep queries. Big data technologies, originally created to handle large data lakes from data warehouses, have rapidly evolved into not just scalable but responsive data manipulation engines that can meet the needs of AIOps. AIOps represents the unification of deep data research and online, real-time analytics to elevate IT decision making.
AIOps enables IT to move from rule-based, human management of analysis to machine-assisted analysis and machine learning systems. This is required not only because of limits to the amount and complexity of analysis human agents can achieve, but also to enable a level of change adaptation that hasn’t been possible.
IT analytics is ultimately about pattern matching. IT systems, users and ecosystems exhibit behaviors and relationships that can point to root causes, isolate issues, and indicate future problems. Machine learning applies the computational power and speed of machines to the discovery and correlation of patterns in IT data. It does this more and faster than human agents and dynamically changes the algorithms used by analytics based on changes in the data.
AIOps connects and drives automation in the hyper-complex, multi-source cloud environment. Delivering machine-assisted analytics at scale on high volumes of digital IT data is useless if the outcomes still require human intervention. AIOps can generate workflows and measure the effects of those processes, feeding the results back into the system as data to be analyzed and learned from. Additionally, AIOps should be applied by the system automatically based on the data, without the need for user intervention and decision.
The democracy of data
IT needs to focus on enabling digital business transformation. It should not have to have dedicated data specialists or scientists to feed and create analytics systems, nor hire or develop data analysis skills in IT personnel. AIOps leverages machines to do this work without the need for specialized resources. The output of analysis can be consumed and customized by anyone in the IT organization and can be easily extended to partners across the business.