AIOps refers to the application of big data, machine learning, analytics, and automation to IT Ops use cases in order to address today’s need to make sense of large quantities of mostly structured, specialized, cross-domain IT data. With AOps, IT teams can leverage machine learning and big data to drive continuous insights and automate remediation (when appropriate). These insights are used to drive incremental business value.
Source: Gartner (November 2018)
Figure 1: AIOps Platform enabling continuous insights across IT Operations Management (ITOM).
The power of AIOps is in analyzing IT big data and taking action faster than humanly possible–to drive better business outcomes.
oday’s IT organizations can benefit from AIOps, leveraging machine learning and visualizations across extremely large, cross-domain datasets. Using AIOps, you can accelerate root cause analysis, automate remediation, and ultimately drive better business outcomes. This is not hype; it’s happening today across all kinds of industries on a large scale.
Investments in AIOps tools and training are driven by two primary forces—the importance of digital transformation and the growing complexity of the IT environment.
Most enterprises today realize that in order to build customer loyalty, streamline operations, and increase workforce productivity, they must develop and deliver exceptional digital services—and do so faster and more effectively than the competition. AIOps provides both the IT and business the quantitative, data-driven insights to do so.
In particular, IT is faced with more apps, systems, and platforms than ever to keep running in peak condition. Containers, microservices, and other highly-dynamic environments generate large volumes of data that exceed the capacity of human processing, making AIOps necessary for modern cloud-native applications and greater IT automation.
AIOps drives four key benefits outlined below:
You can think of machine learning as a subset of AIOps. AIOps refers to the application of big data, machine learning, analytics, and automation to make sense of large quantities of mostly structured, specialized, cross-domain IT data. Machine learning, one component of AIOps, uses algorithms to predict outcomes based on input data and these outcomes are automatically updated as new data becomes available. Machine learning is often used for pattern recognition, anomaly detection, and to support visualizations. What’s required for machine learning? Lots of high-quality data, algorithms (both advanced and basic), scalability, and modeling.
You need all of the data you can get. Application traces and logs, infrastructure metrics, SNMP and API data, network flows, device health, user experience info, even packets and transactional metadata.
This big data demands scalability to collect, store, and analyze the billions of transactions, metadata, and metrics that are generated each day. The quality and completeness of the data drives artificial intelligence and machine learning insights, making scalability a critical component of effective AIOps. Equipped with the necessary data, next-gen AIOps tools can automatically map dependencies and build contextual models so that troubleshooters can quickly determine the root cause of an issue. Incorporating the related metadata into a user-centric data model provides much needed context and insight for IT and business operations, helping prioritize resource allocation and service delivery for the most valuable customers and processes.
Many organizations are embracing AIOps for:
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