What is AIOps? Short for "AI for IT operations", AIOps uses big data and analytics, and increasingly AI itself, to enhance
IT operations
.
Here's how: AIOps employs machine learning to automate various tasks. AI plays a crucial role in IT operations functions. These include anomaly detection and event correlation. Also, AIOps analyzes large volumes of network and machine data. It identifies patterns and determines the cause of existing problems. Additionally, it forecasts and prevents future issues effectively.
Today's data environments are
messy
, with data spread across microservices, multi- and hybrid cloud, containers, along with the proliferation of
distributed systems
. The result? Massive and unwieldy volumes of
log data
and performance data that can:
Quickly overwhelm and prevent IT analysts from doing valuable work.
Impede visibility into the health and safety of the network.
AIOps solutions help It professionals resolve these issues by effectively monitoring assets and expanding visibility into dependencies, both internally as well as outside of IT systems — and all without human intervention.
In this post, we’ll articulate how AIOps work, its myriad use cases and many benefits, and how you can get started effectively implementing AIOps in your organization.
Using AI and machine learning, ITSI correlates data collected from
monitoring sources and delivers a single live view of relevant IT and business services, reducing alert noise and
proactively preventing outages.
Within a year or so, however, Gartner shifted the phrase to "Artificial Intelligence for IT Operations" — a subtle but powerful change in the marketing of the concept.
AIOps is designed to bring the speed and accuracy of AI to IT operations.
IT operations management
has become increasingly challenging as networks have become larger and more complex. Traditional operations management tools and practices struggle to keep up with the ever-growing volumes of data. This data comes from many sources within complex and varied network environments. To combat these challenges, AIOps tools:
Bring together data from multiple sources
: Conventional approaches, tools, and solutions weren’t designed in anticipation of the volume, variety, and velocity generated by today’s complex and connected IT environments. Instead, they consolidate and
aggregate data
and roll them up into averages, compromising data fidelity. A fundamental tenet of an AIOps platform is its ability to capture large data sets of any type across the environment while maintaining data fidelity for comprehensive analysis.
Simplify data analysis
: One of the big differentiators for AIOps platforms is their ability to collect all formats of big data in varying velocity and volume. The platform then applies automated advanced
data analytics
on that data to predict and prevent future issues and identify the cause of existing issues that enable better decision-making.
Using machine learning and big data, an AI platform helps IT operations deliver greater business value.
How AIOps works
Now that we know what AIOps is, let's discuss how it works. Most often, you'll perform AIOps via an AIOps platform. AIOps platform needs to be able to both analyze stored data and provide real-time analytics at the point of ingestion. According to
Gartner
:
"An AIOps platform combines 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. The platform enables the concurrent use of multiple data sources, data collection methods, and analytical and presentation technologies."
More specifically, they say that any AIOps platform is defined by five characteristics: cross-domain ingestion of events, topology generation, event correlation, incident identification, and remediation augmentation.
Let's break down how AIOps works, as defined by Gartner
Ingests data
An AIOps platform gathers data from multiple sources, independent of the vendor or the source.
Performs real-time analytics
During data ingestion, AIOps perform
data analysis in real-time
. Thus allowing immediate insight into what is going on. As this detects performance problems or issues immediately, you will be able to respond faster in case of critical events.
Performs historical analysis
Apart from real-time data analysis during ingestion, AIOps also analyses previously stored data. Thereby, providing a detailed record of trends or anomalies (if any) that occurred in the past.
This allows the IT team to find out repetitive problems. They can learn from past issues and thus optimize the performance.
Leverages machine learning
AIOps uses machine learning to constantly improve its capability to analyze, predict, and adhere to operational problems. With time, the system becomes able to make smarter decisions by learning from data patterns, so you can more accurately forecast potential issues.
Initiates automated actions
As we have learned in the previous points, AIOps analyze historical as well as
real-time data
and gather insights. Based on that, an AIOps platform can take automated actions. Thereby, reducing the requirement of manual intervention. Teams can therefore, focus on planning or other strategic task while the system automatically takes care of routine operations.
In the next section, we will discuss what AIOps does on a practical level.
According to Gartner, there are five primary capabilities for AIOps. We'll look at each one, below.
Big data management (volume, variety, variability and velocity)
Performance monitoring and analysis
Anomaly detection
Event correlation and analysis
IT service management
Performance analysis
AIOps is a key use case for
application performance analysis and management
, using AI and machine learning to rapidly gather and analyze vast amounts of event data to identify the root cause of an issue.
A key IT function, performance analysis has become more complex as the volume and types of data have increased. It’s become increasingly difficult for IT professionals to analyze their data using traditional IT methods, even as those methods have incorporated machine learning technology. AIOps help solve the problem of increasing the volume and complexity of data by applying more sophisticated AI techniques to analyze bigger data sets. It can predict likely issues and quickly perform root-cause analysis, often preventing problems before they happen.
Anomaly detection
in IT (also known as "outlier detection") is the identification of data outliers — that is, events and activities in a data set that stand out enough from historical data to suggest a potential problem. These outliers are called anomalous events.
Anomaly detection relies on algorithms. A trending algorithm monitors a single
KPI
by comparing its current behavior to its past. If the score grows anomalously large, the algorithm raises an alert. A cohesive algorithm looks at a group of KPIs expected to behave similarly and raises alerts if the behavior of one or more changes.
AIOps makes anomaly detection faster and more effective. Once a behavior has been identified, AIOps can monitor the difference between the actual value of the KPI versus what the
machine learning model
predicts, and watch for significant deviations. For example,
Netflix
uses AIOps to detect irregularities in their streaming service. This improves user experience by minimizing downtime.
Event correlation and analysis
Together,
event correlation
and
event analysis
offer the ability to see through an “event storm” of multiple, related warnings to the
underlying cause of events
and make a determination on how to fix it. The problem with traditional IT tools, however, is that they don’t provide insights into the problem, just a storm of warnings.
AIOps uses AI algorithms to automatically group notable events based on their similarity. This reduces the burden on IT teams to manage events continuously and reduces unnecessary (and annoying) event traffic and noise. AIOps then perform rule-based actions, such as:
Consolidating duplicate events
Suppressing alerts
Closing notable events when they are received
IT service management
IT service management (ITSM)
is a vast term for everything involved in designing, building, delivering, supporting, and managing IT services within an organization. ITSM encompasses the policies, processes, and procedures of
delivering IT services
to end users within an organization.
AIOps provides benefits to ITSM by applying AI to data to identify issues and help fix them quickly, thereby helping IT departments be more efficient and effective. For ITSM, AIOps can be applied to data, from monitoring the IT service desk to managing devices.
Make more accurate predictions for capacity planning
Maximize storage resources by automatically adjusting capacity
Improve resource utilization based on historical data and predictions
Identify, predict, and prevent IT service issues
Manage connected devices across a network
Automation
Legacy monitoring tools often require manually cobbling information together from multiple sources before it’s possible to understand, troubleshoot, and resolve incidents. AIOps provides a significant advantage with its ability to automatically collect and correlate data from multiple sources, greatly increasing speed and accuracy.
The AIOps approach automates these functions across an organization’s IT operations, including:
Servers, OS, and networks:
Collect all logs, metrics, configurations, messages and traps to search, correlate, alert and report across
multiple servers
.
Containers:
Collect, search and correlate container data with other infrastructure data for better service context, monitoring and reporting.
Cloud monitoring
:
Monitor performance, usage and availability of cloud infrastructure.
Virtualization monitoring:
Gain visibility across the virtual stack, make faster event correlations, and search transactions spanning
virtual
and physical components.
Storage monitoring
: Understand storage systems in context with corresponding app performance, server response times and virtualization overhead.
Now, let's start with AIOps and step into a new world where AI will make your IT operations more efficient and smarter.
Get started with AIOps
The best way to get started with AIOps is an incremental, more agile approach.
Understand IT by data source
One best practice is to start small by reorganizing your IT domains by data source. Learn how to work with large, persistent data sets from a variety of sources. Let your IT operations team become familiar with the big data aspects of AIOps. Start with historical data, and gradually add new data sources as you improve your practice.
Focus on ingesting data first
Ingesting and analyzing all of the data effectively and quickly can be daunting. Instead, start by accessing and analyzing raw historical machine and metric data to establish a base understanding, and use
clustering
algorithms and analytics to identify trends and patterns. Raw data is the best type if you truly want real-time detection.
Then you can begin to analyze streaming data to see how it fits those patterns, applying AI powered by machine learning to introduce automation and, eventually, predictive analytics.
Ingest and analyze as many data types as you can
If you start by analyzing and understanding past states of your systems, you will be able to correlate what you learn with the present. To achieve this, organizations must ingest and provide access to a wide range of historical and streaming data types. The data type that you select — be it
log, metric
, text, wire or social media data — depends on the problem you’re solving.
For example, you can use:
Metric data from your infrastructure to monitor capacity
Application logs to ensure that you are providing an outstanding experience to your customers.
Ultimately, enterprises should select those platforms that are capable of ingesting and analyzing data from multiple sources.
Focus on finding the root cause of your highest-priority problem. Then progress to monitoring data. Only after this has been accomplished should AI be approached. Even then, take it step-by-step:
Start by implementing an AIOps platform that gives you an effective foundation for organizing large volumes of data, and that makes it easy to take action and monitoring capabilities that reveal patterns.
Next, explore the degree to which those patterns enable you to predict incidents and have a more proactive IT approach that allows you to decrease not only your
MTTR
but also the number of business-impacting incidents.
Finally, work with machine-learning-powered
root-cause analysis
to get to a predictive state in which you can determine the incident and its impact before it even affects your key business services and customer experience.
All good? Okay then! Let's find out how implementing AIOps will benefit your business.
Getting Started with AIOps: Event Correlation Basics and Alert Storm Detection in Splunk IT Service
Benefits of implementing AIOps
AIOps can provide significant business benefits to an organization. It does so by automating IT operations functions and using AI to enhance and improve system performance, for example:
AIOps provides countless benefits to organizations, including avoiding downtime, correlating data, accelerating root cause analysis, discovering and fixing errors — all of which give leadership more time to collaborate.
Bringing together data sources that had previously been siloed allows more complete analysis and insight.
Accelerating root-cause analysis and remediation saves time, money, and resources.
Increasing response time and consistency of response improves service delivery.
Finding and fixing errors that would be tedious and time-consuming for people to address increases job satisfaction and lets IT teams focus on higher-value analysis and optimization.
Giving IT leadership more time to collaborate with business peers demonstrates the strategic value of the IT organization.
Many of the challenges that AIOps can help IT operations resolve are common across all industries. There are, however, issues that are more prevalent or more threatening in particular industries, including healthcare, manufacturing and financial services. By automating IT operations and using AI to enhance and improve system performance, AIOps can provide significant business benefits to an organization. On that note, let's discuss some use cases of AIOps in different sectors.
AIOps use cases
The following use cases show how AI streamlines IT ops across various industries.
Healthcare IT (HIT)
Keeping electronic personal healthcare information (ePHI) safe in compliance with the Health Insurance Portability and Accountability Act (HIPAA).
Reducing the hazards of mobile networking and bring-your-own-device (BYOD) practices by medical professionals.
Preventing ransomware attacks, which disproportionately target healthcare organizations.
Making big data, both internal and external, available for research and diagnostic use.
Manufacturing
Automating the collection and analysis of disparate data sources created by the integration of supply chain, plant operations and product and service life-cycle management.
Using real-time monitoring to track every machine on the factory floor, bringing together such data as manufacturing cycle times, quality yields by machine and production run, capacity utilization and supplier quality levels.
Preventing production slowdowns and troubleshooting using historical data combined with AI-driven
predictive analytics
, thereby protecting revenue streams and increasing customer satisfaction.
Providing a framework for integrating multiple, large data sets to allow emerging technologies like blockchain.
Keeping up with consumer expectations of mobile and digital banking experiences.
Improving network speed and performance.
Now that you know how AIOps work and how beneficial it is for you, it's time to find out what the future has in store for AIOps.
The future of AIOps: stats & trends
In recent years, AIOps platforms have gained significant popularity in the enterprise, as organizations across multiple industries have deemed AIOps a critical tool in managing their data environment and expanded its use across ITOM functions. Consequently, the AIOps market is primed for significant growth without signs of a slowdown.
For some routine engineering tasks such as test generation
Observability functions
and automation of resilience workflows, such as penetration testing, could also be affected by generative AI.
To provide analysis on unstructured data sets that include audio and chat files.
Exactly how generative AI will impact these functions remains to be determined. But it’s likely that it will play an increasingly bigger and more significant role. Let's discuss what a leading expert in the industry has to say on AIOps.
An Expert's Perspective
To better understand the future role of AIOps, we spoke with
Sanjay Munshi
, Deputy Chief Operating Officer at
NETSCOUT
to get his perspective on the importance and future of AIOps.
“Executives are placing and investing significant trust and capital into AI, hoping for the game-changing outcomes they were promised. However, not all AI systems and platforms have the proper data foundation to improve business outcomes. AI can only be as good as the data it receives. Bad data equals bad AI. Models built using incomplete or abstracted data risk underperformance or, worse, misinformed business decisions.
A fundamental, foundational change to the data strategy is needed to properly fuel AI and AIOps systems. This requires a distributed sensor framework that does not rely on a static representation of infrastructure elements and is transparent, or not susceptible, to hacker activity. The sensor software captures, analyzes, and curates data intelligence at the source that not only provides the highest-fidelity data available, but also helps complete data models built on metrics, logs, or traces alone.
To achieve the promise of faster remediation, automated responses, and trustworthy results that deliver a better user experience, high-performing AI must be built on a foundation of high-quality, curated, actionable, and enriched data sourced from across the entire enterprise.”
Investing in AIOps will improve business operations
If you’re an IT and networking professional, you’ve heard repeatedly that data is your company’s most important asset. It will transform your world forever. AI is a revolution and it’s here to stay. And, AIOps provides a concrete way to turn the hype about AI and big data into reality for your business.
From improving security to streamlining operations to increasing productivity, AIOps is a practical, readily available way to help you grow. Using AIOps, you will also be able to scale your IT operations to meet future challenges. Thereby, solidifying IT’s role as a strategic enabler of business growth.
The Splunk platform removes the barriers between data and action, empowering observability, IT and security teams to ensure their organizations are secure, resilient and innovative.
Founded in 2003, Splunk is a global company — with over 7,500 employees, Splunkers have received over 1,020 patents to date and availability in 21 regions around the world — and offers an open, extensible data platform that supports shared data across any environment so that all teams in an organization can get end-to-end visibility, with context, for every interaction and business process. Build a strong data foundation with Splunk.
To better understand the future role of AIOps, we spoke with