Today’s business world is increasingly turning toward digitization. Many essential tasks have shifted to digital processes, including cloud-based and automated systems, and even more tasks are expected to make the shift in the future. With this movement toward increasing digitization, information technology (IT) capability development and operations have become increasingly business-critical, with responsibility for managing resources and facilitating changes while keeping current business functions afloat.
With IT becoming more of a focus in sustaining and building a business, IT departments are struggling to keep up with demand. Too many IT teams end up focusing on fixing emergencies and managing massive amounts of incoming data on a daily basis. Teams are less able to support the innovation and agility needed to push the company forward.
To keep up with demand and streamline IT functions ranging from the development of new capabilities to management and sustainment of existing capabilities, many companies have adopted Development Operations (DevOps). DevOps applies Lean development principles, originally created for Lean Manufacturing, that concentrate on (1) automating development-integration-deployment-maintenance pipelines to enable people to create more by not engaging in repetitive, time-consuming activities, (2) delivering with a cadence to meet customer expectations rather than being constrained by resource limitations, and (3) incorporating a whole life-cycle viewpoint to avoid delivery of problematic IT capabilities that consume more labor hours rather than less.
DevOps’ faster cycle times, more predictable delivery cadence, and full-life-cycle world view have actually increased the pressure on IT departments, rather than diminishing it. The introduction of the DevOps toolchain, which automates the IT life-cycle pipeline, and the use of IT infrastructure support services, many of which are available through the cloud, are generating too much critical development and operations health and status data for an organization to utilize effectively. Monitoring, analyzing, understanding, and using this data torrent does not scale up smoothly when the only way to scale is by hiring more human beings.
AIOps is the solution to the deluge of data about IT development and operations. “Artificial Intelligence”— more sophisticated IT — is constructed and trained to help human beings benefit from all of the information generated by using information technology.
AIOps stands for artificial intelligence for IT operations and refers to a system where an AI (1) manages data and information about the IT environment and (2) improves and optimizes the configuration and operation of the IT environment and development-deployment-operations-maintenance life cycle. AIOps platforms leverage machine learning and big data to intelligently filter meaningful information from data streams that are produced by the DevOps toolchain and customer-facing operations, providing continuous insights across IT functions. In action, AIOps, directly and indirectly, pulls data from and enhances three key IT operations:
By streamlining these IT functions, AIOps delivers greater control to IT teams and ultimately company leaders and customers. With the amount of data created for a single business in a single day, IT teams can’t draw out patterns from the raw data. AIOps identifies the patterns and handles tasks for them, allowing IT development and operations teams to quickly detect issues, prioritize key operations, and refocus efforts.
When discussing AIOps, other terms may make their way into the conversation, including machine learning operations (MLOps). While these terms relate to machine learning, they address slightly different approaches and goals. Consider an overview of each term for clarity:
In short, while MLOps focuses on collecting and analyzing data about IT, modeling and predicting IT behaviors, and diagnosing problems but leaves any IT changes and optimizations to human beings, AIOps using the IT systems knowledge generated from MLOps to actually pro-actively manage IT activities with little or no human intervention.
Due to AIOps platforms’ abilities to process and analyze large volumes of data, they allow IT teams to improve tasks in a variety of areas. AIOps use cases have a range of capabilities, some of which include:
Today’s logging processes focus on telling development, testing, integration, deployment, maintenance, and operations teams what is currently happening. With the sheer amount of incoming data, this logging can often be overwhelming, and DevOps teams may miss key data and patterns that could provide better visualization of development activities and system operations. Especially critical from a DevOps viewpoint is understanding the linkage between all activities starting with development and culminating in deployed operational systems and applications. AIOps platforms mitigate this problem.
AIOps can provide valuable system performance metrics and logs that can offer detailed visuals of systems in real-time. AIOps takes all incoming data from multiple streams–development, patching, operations–and processes it in combination with historical data to provide meaningful metrics for improved visualization, trending, and prediction.
The key benefit to having improved metrics and visualization for IT teams is improved prediction and reaction time. With better visualization, IT teams can identify problems and patterns quickly, which can help identify opportunities for optimization and remediation. Solving problems and improving capabilities is much easier when data are analyzed and visually represented.
One primary use of AIOps platforms is ingesting, filtering, and correlating data, which is highly useful for event (also known as time series) analysis. Using both current and historical data, AIOps correlate events and predict causality. The system identifies anomalies at local and environmental levels, then correlates these with other alerts and metrics to provide a complete report of an event, including the triggers for the event. Detailed event correlation provides IT with a complete view of the incident, including the root causes, impacts, and context. Comprehensive AIOps solutions can also prescribe solutions based on historical data.
In addition to providing more information, this process of analysis is transparent and open to feedback. Each report provides transparency, detailing traceability about where the information was gathered and how it correlated events. Teams can then build trust in the AIOps system and its event correlation processes. The DevOps team can also give feedback for how the results used or what conclusions were drawn back into the AI engine, thereby training the system to learn and improve.
In many systems that don’t use AIOps, a single failure can result in a domino effect in other systems. One issue in a core system can cause problems in related ones, and all of them may generate separate alerts. A resulting alert storm creates noise and distractions to IT specialists without presenting solutions. For example, a failure may be noticed first in one particular application, but the failure could impact many systems even though the failure’s root cause is from a single poorly constructed line of software written many years earlier. As the Boeing 737 Max MCAS disasters demonstrate, understanding the root cause of a failure and reacting to it correctly without exacerbating the problem is hard. Specialists then experience reduced productivity as they scramble to identify the sources of the alerts and determine how to resolve the issue. AIOps platforms’ can eliminate this through intelligent alerting and escalation processes.
AIOps intelligent alerting works using platforms’ event correlation capabilities, which identify related issues and drill down to the root cause of the problem. From there, the system can generate a single alert with all relevant context and information. If multiple issues are identified, the system can also prioritize them based on business impact. These functions help reduce alert fatigue and troubleshooting needs while refocusing IT specialists on resolving the issues at hand.
Additionally, AIOps platforms can implement automated escalation and remediation protocols for known issues. Once a problem is identified, the AIOps system can move it to the appropriate specialists. The system can also utilize historical data from past issues to suggest an approach for remediation. The result helps streamline remediation so problems are resolved as quickly as possible.
Rather than reacting to issues as they arise, AIOps allows IT teams to take a more proactive approach. AIOps platforms can ingest data, analyze it, and build predictive models to identify areas where optimization or remediation may be needed. Significant benefits to anomaly detection and predictive analytics come with having a data-driven model that informs IT teams about how the system is operating compared to how it should be operating.
With anomaly detection, AIOps platforms can identify performance problems early and in real-time. With fast identification and remediation suggestions, AIOps allows IT teams to fix problems quickly before they affect related systems. These quick reaction times can help resolve and even prevent system slowdowns and costly outages.
AIOps is also valuable for predictive analytics in development and testing environments. In a development environment, AIOps can help developers predict how changes to software code in one place have resulted in more or less stable operations after deployment. For instance, if the company delivers value to customers by using multiple interdependent applications, AIOps can estimate the risk of changes in particular subcomponents using both historical data and software code dependency graphs, allowing a prediction of how small changes could impact the overall system. This predictive change model provides developers with an opportunity to focus their efforts on reducing risk. In testing environments, AIOps can help check code for vulnerabilities, provide guidance for improvements, and even suggest efficient testing strategies focused on problematic system components. Once software code is ready for deployment, AIOps can identify which systems will be impacted by the code push and predict outcomes. As a whole, AIOps can streamline the development and testing processes and prevent errors due to software code development and deployment.
Due to the siloed nature of monitored environments, teams can be challenged to identify the impacts of processes on areas outside of a single segment of IT. AIOps helps resolve this by taking in data from all available sources in real-time for a holistic view of the application environment. With this improved observability, IT teams can gain a better view of processes and performance, particularly when the processes are connected in subtle ways or across multiple segments.
Along with improved observability, AIOps presents greater actionability. With real-time AIOps monitoring of the entire application environment, teams can identify how performance in one area affects other applications and business segments. AIOps can also help identify potential areas of improvement using this data. As a whole, this data insight allows leaders to make faster, more informed decisions on process improvements development, integration, testing, deployment, and operations to improve value delivery to customers and business outcomes.
AIOps presents a unique solution for businesses and is especially valuable to IT, IT management, DevOps, and DevSecOps teams. Automation and analytics provided through AIOps solutions present benefits for process improvement in these teams, including:
While these benefits of AIOps to IT are substantial, there is plenty of room for growth. At present, AIOps tools are mostly designed for traditional operations-focused teams. More and more capabilities across the entire IT life-cycle are available every day, though!
There’s also the challenge of AIOps-directed improvement versus AIOps-informed human improvement. While AIOps directly improves and automates certain functions, many functions like visualization and event correlation provide users with information so they can make faster and more informed decisions. In current DevOps AIOps platforms, functions tend to be more heavily weighted toward AIOps-informed human improvements.
AIOps technology is still relatively new. Terms and concepts involved in AIOps are still emergent and fluid, and there is still a lot of room to grow.
So what is the future of AIOps, and where is it headed? Industry professionals expect some of the following developments in AIOps in the next few years:
Forward-thinking IT leaders should keep a close eye on AIOPs and start thinking about the potential of AIOps adoption in their companies. Many companies are wondering, “Why do we need AIOps?” The reality, however, is that IT is steadily moving beyond the human scale, and AIOps presents the best solution for growing, innovating, and adapting in a world of big data.
AIOps has a promising future, and with today’s IT challenges, making the switch is a choice IT leaders need to make sooner rather than later. Whether you’re still wondering about the uses for AIOps or have made the decision to switch and want to know how to start AIOps, BTI is here to help.
BTI is dedicated to helping customers develop the best processes for their business, providing systems engineering, process improvement, and process automation services to companies across the United States. We serve a range of industries, and our customer base includes IT solution services, financial services, retail, utility, aerospace, and even government institutions. Our team of experts is dedicated to helping our customers transform their processes using a toolbox of process improvement approaches, including machine learning and artificial intelligence applied to the IT life cycle, DevOps, DevSecOps, CMMC, Lean Six Sigma, CMMI, systems engineering, Balanced Scorecard, Agile, and ITIL/ITSM.
Learn more about AIOps and how BTI can help your business. Contact BTI today.
