What is AIOps? The Definitive Guide
You cannot manage today’s dynamic and constantly changing IT landscape using yesterday’s tools. There exists an ongoing rapid evolution of infrastructure models that demands dynamic processes and technology for its management.
The business environment is moving from the static and predictable physical systems that have defined the space for decades to a software-defined resource environment that changes and reconfigures on the fly. Moreover, as network infrastructure evolves, old model-based software systems require more and more effort to maintain effectiveness, yet continue to fall further and further behind.
Due to this ITOps (IT Operations) revolution, digital business transformation forces have necessitated a change to the traditional IT management techniques. Consequently, there is a significant change in the present ITOps procedures and processes as well as a restructuring in the management of IT ecosystems.
Gartner coined the term Artificial Intelligence for IT Operations or AIOps in 2017 to capture the spirit of these changes.
AIOps uses data science and machine learning to give ITOps teams a real-time understanding of the issues affecting the performance or availability of the systems under their care.
Over the last few years, the AIOps market category has exploded while the number of inquiries that Gartner fields exponentially increase as businesses scramble to understand and get ahead of this new development.
This definitive guide discusses everything you need to know about AIOps, the market and technology dynamics driving its emergence, and how it can respond to those challenges.
The Road to AIOps
It is essential first to understand digital transformation and how it gives rise to AIOps.
Digital transformation encompasses the implementation of new technologies, cloud adoption, and rapid change. It requires a shift in focus to developers and applications and an increased pace of innovation. It also involves the acquisition of things such as:
- Internet of Things (IOT) devices
- New digital users-machine agents
- Application Program Interfaces (APIs)
All these new users and technologies are straining traditional service and performance management tools and strategies to the breaking point.
Successful digital transformation is reliant on AIOps to enable IT to function at the speed that most modern businesses require. Therefore, AIOps describes the paradigm shift needed to handle digital transformation in ITOps.
What is AIOps?
AIOps is an acronym for “Artificial Intelligence for IT Operations.” It is the future of ITOps (IT Operations). It combines human and algorithmic intelligence to offer full visibility into the performance and state of the IT systems that companies and businesses rely on in their daily operations.
It refers to high-end multi-layered technology platforms that enhance and automate IT operations by using machine learning and analytics to analyze the big data collected from different ITOps devices and tools to identify and then react to issues in real-time automatically.
AIOps requires you to move from siloed IT data to aggregate observational data (for example, job logs and monitoring systems) and engagement data (such as that found in a ticket, event, or incident recording) inside a big data platform.
AIOps then implements machine learning and analytics against the combined data. The outcome is continuous insights capable of yielding continuous improvements with automation implementation. Therefore, you can think of AIOps as CI/CD (Continuous Integration and Continuous Deployment) for core IT functions.
AIOps bridges three IT disciplines (automation, service management, and performance management) to achieve its goals of continuous insights and improvements. It’s the recognition that in the new accelerated and hyper-scaled IT environments, there exists a new approach that can leverage advances in machine learning and big data to overcome human and legacy tool limitations.
How AIOps Works
AIOps works with an organization’s existing data sources, including log events, traditional IT monitoring, network performance anomalies, and more. The data collected from all these source systems is processed using a mathematical model that can automatically identify significant events without requiring laborious manual pre-filtering.
Another layer of algorithms analyzes the events and identifies any clusters of related activities that are symptoms of a similar underlying issue. The algorithmic filtering significantly reduces the noise level that ITOps teams would otherwise have to contend with and also avoids duplication that can occur from the redundant routing of tickets to different groups.
Instead, you can assemble virtual teams on the fly and enable different specialists to throng around an issue spanning across organizational or technological boundaries. Existing incident management and ticketing systems can take advantage of the capabilities of AIOps and integrate directly into existing processes.
AIOps, further, improves automation. It enables the triggering of workflows with or without human intervention. Present ChatOps capabilities avail existing automation functionality as an essential part of the normal collaborative process of diagnostic and remediation.
As machine-learning systems increasingly become accurate and reliable, it now possible to trigger routine and well-understood actions without human intervention, which can potentially resolve issues before they impact users.
The Elements of AIOps
Below are the technologies that make-up AIOps Platforms.
- Data Sources. They are extensive and diverse. They come from presently siloed tools and IT disciplines, including events, logs, metrics, tickets, monitoring, job data, etc.
- Big Data. Include modern big data platforms that permit real-time processing. Examples include Elastic Stack, Hadoop 2.0, or some Apache technologies.
- Rules and Patterns. AIOps platform’s rule application and pattern recognition enforce leverage and can discover context while uncovering data normalities and regularities. They may or may not be specific to the domain.
- Machine learning (ML). ML can automatically create new or alter existing algorithms based on the output of newly introduced data and algorithmic analysis.
- Domain algorithms. Leverage IT domain expertise to intelligently interpret rules and patterns and apply them as dictated by an enterprise’s data and desired outcomes. Domain algorithms enable organizations to achieve IT specific goals such as correlating unstructured data, eliminating noise, alerting on abnormalities, identifying the probable causes, and establishing baselines.
- Automation. Uses the outcomes that machine learning and AI generate to create and apply responses for identified issues and situations automatically.
- Artificial Intelligence (AI). AI can adapt to the unknown and the new in an environment.
The Requirements and Capabilities of AIOps
All AIOps platforms should bring the following three capabilities to your enterprise.
- Automate routine practices, such as user requests or non-critical IT system alerts. For instance, AIOps can enable help desk systems to process and fulfill user requests to provision resources automatically. They can also evaluate alerts and determine whether they require action because the supporting data and relevant metrics fall within normal parameters.
- Recognize serious issues faster and more accurately than humans. IT personnel might address known malware events on noncritical systems but ignore unusual downloads or processes starting on a critical server since they are not watching or anticipating for this threat. AIOps systems address these scenarios differently. They prioritize events on critical systems as possible attacks or infections, since the behavior is not normal, and deprioritize known malware events by running antimalware.
- Streamline interactions between data center teams. AIOps provides all functional IT groups with relevant data and insights. Without these AI-enabled operations, teams must parse and share information by manually sending data or meeting physically. AIOps should learn what data to show each group from the organization’s large pool of resource metrics.
What Drives AIOps and Why Do You Need It?
The promise of artificial intelligence (AI) is doing what humans do but faster, better, and at scale. AIOps allows you to do this for your ITOps by addressing the size, complexity, and speed challenges of digital transformation. These challenges include:
- The Difficulty ITOps Faces in Manually Managing Its Infrastructure
- ITOps Needs to Retain an Increasingly Large Amount of Data
- The Need to Respond to Infrastructure Problems at Ever-Increasing Speeds
- Developers Enjoy Increased Power and Influence, but Accountability Remains with Core IT
- Moving More Computing Power from the Network Center
The term infrastructure is almost a misnomer since modern IT environments include mobile, managed and unmanaged cloud, third party services, SaaS integrations, and more. It’s evident that traditional approaches to managing business complexities no longer apply in dynamic, elastic environments effectively. Managing and tracking this complexity using manual, human oversight is getting harder. The current ITOps technology is beyond manual management.
Performance monitoring is increasingly generating exponentially larger numbers of alerts and events. Service ticket volumes have step function increases due to the introduction of mobile applications, IOT devices, APIs, and digital or machine users.
As organizations digitize their operations, IT becomes the business. Technology “consumerization” has changed user expectations across all industries. Today’s reactions to IT events need to occur immediately, especially when issues impact the user experience.
In DevOps organizations, programmers have assumed more application-level monitoring responsibility, but accountability for the health of the entire IT ecosystem as well as the interaction between infrastructure, applications, and services remains the domain of core IT.
The ease of adopting third party services and cloud infrastructure has empowered LOB (Line of Business) functions to build their IT applications and solutions. Control and budget are shifting to the edge of IT. Organizations can now add more computing power from outside core IT.
Integrating AIOps with Your Current Tools
AIOps integrates with existing processes and tools, bringing together useful information, capabilities, and insights. Businesses use different monitoring tools located in different areas and for various purposes. Each tool is valuable to a specific function, team, or company, but its value is not available to other interested parties.
Therefore, rather than engage laborious tool rationalization initiatives trying to force individual needs into a one-size-fits-all solution, AIOps allows the thriving of specific tools by enabling seamless visibility across domains, teams, and tools.
Similarly, AIOps enables IT service management (ITSM) by ensuring the creation of only real, actionable incidents, and avoiding duplication. AIOps addresses and removes a lot of the ITSM user-frustrations due to the sequential nature of the IT Infrastructure Library (ITIL).
AIOps also bring automation into the fold. It integrates orchestration and runs books, availing them directly to operators as full or partial automation. IT organizations have developed large automation solution libraries over the years, so they must ensure that only correct conditions trigger them. AIOps not only guarantees this but also minimizes risk and maximizes the value of existing automation investments.
Who is Using AIOps?
- Large, Complex Enterprise Heavily Reliant on Big Data and IT
- Cloud Computing
- DevOps Teams
- Digital Transformation
Today, companies with substantial IT environments that span multiple types of technology face issues of scale and complexities. When you compound these issues with business models heavily dependent on IT, AIOps is sure to make a significant difference to the companies’ success. Though these types of organizations may operate in different industries, they share a similar scale and a rapidly accelerating rate of change. The need for business agility creates more demand for IT agility.
Moving to cloud computing has its challenges. One such problem is scaling, where a wholesale IT move to the cloud may not be possible or desirable. You can find it challenging to operate hybrid models that incorporate different IT infrastructure delivery forms.
AIOps helps remove a lot of the risks of operating a hybrid cloud platform through the delivery of a holistic view across all your infrastructure types and by assisting operators in understanding the relationships that change too fast for documentation.
Companies with a DevOps model, or are in the process of adopting one, can find it difficult to maintain alignment between the various roles involved. The direct integration of development and operation systems into an AIOps model can smooth away a lot of the interface friction that can occur.
You want your development teams to have a better understanding of the state of the IT environment. You also want your Operations teams to have full visibility of how and when developers plan to make changes or deployments into production. Having this holistic view ensures that your projects’ overall success and the achievement of agility and responsiveness.
There are many definitions of digital transformation initiatives, but a common factor is a requirement for agility and speed. Although technically, this is a business requirement, IT must operate at the speed of the business to avoid becoming a bottleneck or the achievement of broader goals. AIOps helps removes a lot of the friction that can prevent IT from delivering the support that most successful digital transformation projects need.
The Benefits of AIOps
With proper implementation, AIOps platforms reduce the time and attention that IT staff spend on mundane, routine, or everyday alerts. IT staff teach AIOps platforms, which later evolve using machine learning and algorithms. They then recycle knowledge acquired over time to improve the behavior and effectiveness of the software.
AIOps tools perform continuous monitoring without the need to rest or sleep. Human personnel is free to focus on serious, complex issues and initiatives that increase business stability and performance.
AIOps systems can observe causal relationships over an organization’s multiple operations, resources, and services – collating and clustering disparate data sources. Those machine learning and analytics capabilities enable the systems to perform useful root cause analysis that accelerates its ability to troubleshoot and remediate difficult and unusual issues.
AIOps improves workflow activities and collaboration between IT groups as well as between the IT department and other business units. Teams can understand their requirements and tasks quickly using tailored reports and dashboards. They can also interface with other groups without learning everything the other groups need to know.
AIOps removes noise and distractions, which enables IT personnel to focus on essential issues rather than distractions from irrelevant alerts.
AIOps help correlate information across multiple data resources, which not only eliminates silos but also provides a holistic vision across your entire IT environment – network, compute, and storage (virtual, physical, and cloud).
It allows for frictionless collaboration between service owners and specialists. This accelerates diagnosis, analysis, and resolution times, which minimizes disruption to end-users.
The Drawbacks of AIOps
Although the underlying AIOps technologies are relatively mature, there is still a long way to go in terms of creating and combining them for practical use. Below are some of its drawbacks:
- It is only as good as the algorithms you teach it and the data it receives. Thus, it cannot go beyond the limitations of its programming.
- The amount of effort and time required to implement, manage, and maintain AIOps platforms can be substantial.
- AIOps systems are reliant on diverse data sources, as well as data retention, protection, and storage.
- AIOps demands trust in tooling, a factor that some businesses may not like. This is because, for AIOps tools to act autonomously, they must follow the changes within their target environment precisely, gather and secure relevant data, form correct conclusions, prioritize actions, and finally take the appropriate automated measures.
Implementing AIOps into Your Organization
There is no definite universal roadmap to follow to ensure success. However, some of the general pointers below can help you get started.
- Get acquainted with the basics of machine learning and artificial intelligence now.
- Determine your organization’s most time-consuming tasks that your IT team undertakes. Pay attention to repetitive elements that automation could take over.
- Start small and branch out. Find your highest priority problems that AIOps could solve quickly.
- Feed your system as many different data types as possible.
- Come up with metrics to help you measure the effectiveness of your AIOps investment.
Where AIOps Fits into the Modern IT Environment
When you first look at AIOps, it may not immediately occur to you how it fits into your existing categories of tools. The reason is that it does not replace the current monitoring, orchestration, service desk, or log management tools. Instead, it intersects with all the different domains and tools, integrating and consuming information across all of them. It also gives useful output to provide a synchronized picture from every tool.
Separately, these tools are valuable in their own right. However, accessing the right information at the correct time is difficult when they remain disconnected. AIOps provides a flexible approach to assembling the different partial views into a comprehensive understanding of the big picture – what is essential for your ITOps teams to know.
Although AIOps is a radical departure for ITOps, it is not the first-time application of big data and machine learning. Stockbrokers implemented similar ML approaches when they moved from manual to machine trading. Social media has also long used ML and analytics in applications such as Google Maps, Yelp, and Waze, or online marketplaces like eBay and Amazon.
These techniques proved reliably and extensively useful in environments that require real-time responses to changing conditions and user customization.
The adoption of AI in AIOps is promising compared to Machine learning. Currently, you can address pressing use cases using either simple automation or by combining automation with ML. The evolution of AI and its new use cases is still ongoing. Regardless, it is essential to lay a strong AIOps foundation on ITOps as it presently exists before starting to model human behavior on it.
ITOps personnel are slow to adapt to AIOps environments because of the conservative nature of their jobs. It is their responsibility to ensure that the lights stay on and that they provide stability for the organization’s infrastructure. However, due to the emerging trends of widespread AIOps applications, more ITOps shops will have to adapt to the new AIOps technologies and strategies soon.
The Bottom Line
This definitive AIOps guide will help you determine whether it’s a good fit for your company as well as when to start incorporating it and how you might use it. Beyond that, it is advisable to stay abreast of AIOps' progress. Various signs indicate that this innovative technology is poised for growth.
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