What is AIOps?

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.

Why invest in AIOps?

Today’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.

What are the benefits of AIOps?

AIOps drives four key benefits outlined below:

1. Decrease MTTR. Outages and performance problems hurt the bottom line of every business, so IT organizations must actively seek out ways to reduce the mean time to resolution (MTTR). With AIOps, IT teams can decrease MTTR and prevent emerging issues, and in doing so, significantly reduce the costs associated with performance problems.
2. Build a more predictive approach. With AIOps technology, there’s the ability to recognize patterns and detect potential issues. This can help organizations take action before small issues become larger problems.
3. Automate remediation for common issues. 42% of survey respondents said that they needed to build automated remediation into their strategy for performance monitoring. With embedded expert knowledge, AIOps can prescribe correction action and automate remediation for known issues.
4. Drive faster and better decision-making. AIOps platforms and related AI features have the potential to become smart enough about IT environments in order to automatically take action and address issues before anyone is aware of them. This extends beyond IT to the business, as AIOps tools can be a rich source of data for business intelligence (BI) platforms.

What is the difference between Machine Learning and AIOps?

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.

What data do I need for AIOps?

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.

What are some typical AIOps use cases?

Many organizations are embracing AIOps for:

• Troubleshooting: Accelerate MTTR by applying machine learning, advanced analytics, and visualizations to all your IT big data. Use application dependency mapping to visualize the complex, often short-lived, relationships between containers and microservices and the infrastructure that underpins them.
• Proactive issue identification: Be proactive with automated anomaly detection that alerts you to unusual performance behavior before end-user SLAs are breached. Surface unsuspected issues using pattern recognition and anomaly detection to quickly find the needle in the haystack.
• Strategic planning: Prioritize the DevOps efforts that will have the most overall business impact by identifying the backend components implicated in the most important transactions, by processing time, volume, or financial value. Provide business insight by analyzing all the transactions and associated metadata to understand past, present, and future trends.
• Event management: Apply AIOps to reduce alert floods and noise generated by false alarms or when multiple downstream events are triggered. What’s top of mind for IT today is to build more actionable context into alerts (events) and optimize the incident management workflow by incorporating automated remediation and cross-domain root cause analysis.
• Automated remediation: AIOps systems can be enabled to support automated remediation based on IT operations runbooks. When applied correctly, automation can accelerate fixes, improve user satisfaction, and free IT to focus on more strategic initiatives.
• Infrastructure automation: Re-route network traffic to reduce congestion and free up bandwidth, spin up additional cloud instances and concurrently expand the SD-WAN fabric, and re-distribute containerized workload, for optimum resource utilization and cost-savings.