AI tools and techniques have seen increasing implementation, but AIOps is a recently developed approach that builds off of Agile & DevOps advances.
The growing demand for AIOps
It’s been five years since Gartner coined the term AIOps, which brings artificial intelligence, machine learning, and big data to IT operations. Gartner defines AIOps (or Artificial Intelligence for IT Operations) as a software platform that “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 demand for AIOps software platforms continues to grow steadily, with complex IT organizations and enterprises behind much of the drive.
With AIOps, complex and distributed enterprises can address large volumes of intricate data, often located in multiple silos. Gartner states that digital transformation is boosting the AIOps market as business operations become more complex. As organizations expand their digital transformation efforts, their ability to analyze growing volumes of data becomes both more critical and difficult. Data volumes are reaching levels far in excess of multiple gigabytes per minute, across multiple domains, making timely manual human analysis alone nearly impossible.
Gartner also recently predicted that by 2023, nearly one-third of large enterprises will use AIOps tools to monitor applications and infrastructure. Businesses are viewing AIOps as one of the must-have next generation IT solutions.
Continual insights – key aspect of AIOps
In their guide to AIOps, Gartner has identified the following three key elements of an AIOps strategy:
- Observe – includes collecting, ingesting, and storing data from many sources.
- Engage – applying analytics, risk analysis, and knowledge management to discover root causes and patterns
- Act – using automation to find insights and address known issues.
In our recent webinar on how AI-powered analytics can empower high velocity IT operations, Troy DuMoulin, VP of Research and Development at Pink Elephant observed, “We have no shortage of digital information. But the data isn’t enough. Data without context is very useless.” With an AIOps platform, automation, AI and machine learning is applied to the data, progressing through the following four steps (as shown in the below illustration):
- Set up to observe: preliminary step for continuous monitoring
- Ingest, collect and store data – specifically real time data from multiple sources
- Query and mine data for patterns – apply automation, ML
- Generate automated predictive and preventive insights -AIOps filters meaningful data to make predictions, find root cause analysis.
As DuMoulin added, AIOps identifies opportunities for change, enables the change, and eventually allows for proactive detection and even prediction of issues.
AIOps offers IT organizations critical data-driven insights, helping them increase the speed and efficiency of processes and services while adding value. Furthermore, the New Stack notes: “Big data and machine learning are the two primary components of AIOps. But, it’s also important to note that there are three different IT disciplines within AIOps: automation, performance management and service management. The data associated with each of those areas then gets used by organizations that want to perpetually enhance their operations. Companies get continual insights, which drive constant progress.”
AIOps augments the benefits of DevOps
Even tools such as AI, NLP, and ML are seeing higher levels of implementation within IT processes, AIOps remains a relatively new approach that brings these techniques together to help organizations reduce costs and boost production stability. AIOPs solutions work by building on the foundation of Agile operations and DevOps within an IT environment. In the case of AIOps, removing unneeded steps and gatekeeping comes not merely from organizational process changes, but also from the use of tools that demystify decision-making while automating labor-intensive, manual tasks.
Combining DevOps and AIOps is a key emerging trend, as IT organizations are recognizing advantages that are augmented when these two strategies are combined. Organizations can boost and streamline collaboration between ITOps and DevOps with the implementation of AIOps. As recently discussed in IT Business Edge, “Integration with AIOps will help streamline the six stages of DevOps — plan, build, integration and deployment, monitor, operate and continuous feedback — by providing monitoring, testing, and security through this development cycle. As AI and machine learning continue to improve, the integration between AIOps and DevOps is on track to get tighter in the coming year.”
Benefits of outputs generated by AIOps insights
A key benefit of AIOps for organizations is the vast improvement of performance monitoring and service delivery. When AIOps tools are implemented, IT organizations have access to insights that generate a number of benefits for the IT service delivery, including:
- Issue detection and prediction that helps ITSM respond more quickly to incidents.
- Proactive system maintenance and tuning that reduces human effort and manual errors.
- Threshold analysis that provides a more complete and accurate picture of a system’s normal range of operations.
According to Tech Beacon, AIOps accelerates ITSM as it “focuses on the most likely source of a problem by applying probable cause analytics. It helps to identify the underlying problems driving incidents by using clustering and anomaly detection. AI, machine learning, and automation can lift the burden from your help desk team by assessing the patterns of support tickets, usage patterns, and information regarding user interaction.”
Meanwhile, Accenture further emphasizes the significance of the ML aspect of AIOps, especially in light of the overwhelming amount of alerts that IT operations need to address. “That’s why the machine learning component of an AIOps platform is so important,” Accenture noted in a recent article. “Machine learning can create alert groups that contain related alerts based on historical alert data, so instead of seeing individual symptoms, IT operations professionals see a single underlying issue with its list of associated symptoms.
Cultural change needed for AIOps
Much like an organization’s journey to DevOps, implementing AIOps also requires an extensive cultural change. We recently discussed why successful DevOps adoption requires a shift in mindset, culture, and processes. We noted that the focus on organization-wide culture change is crucial. This is equally true for the successful adoption of AIOps. In the case of AIOps, the culture change needs to reach departments outside of IT operations. In order to set the stage for AIOps success, stakeholders need to be part of the process.
In an article addressing some misconceptions surrounding the adoption of AIOps, the Enterprisers Project states that “A change of culture requires champions, sponsors, and role-models in an organization …You may be surprised what you can accomplish with a small group of open-minded engineers, operations professionals, and a state of the art platform and operations stack.”
Humans can’t possibly sift through all the data that is generated by today’s complex and distributed IT organizations. Organizations suffer when they languish and don’t focus on continual improvements.
IT organizations can optimize the best use of their data and keep their IT leaders informed With AIOps. Using AIOps tools with extensive capabilities gives IT organizations the power to use their data to make smarter decisions that improve service delivery, ITSM, service management, and ultimately deliver more value to their customers. AIOps helps IT organizations reduce incidents, accelerate their ITSM, and reach their objectives of constant innovation and adaptability in constantly evolving business environments.
CTA: Digital.ai’s AIOps tool capabilities are embedded across our solutions portfolio. Learn more at https://digital.ai/aiops