How AIOps is Transforming IT Operations
January 11, 2021
The world of technology is in an era of digital transformation. Part of this transformation includes DevOps and the adoption of technologies such as containers and the cloud.
There is also a shift from centralized IT towards a more developers and applications centric approach along with a heightened innovation and deployment rate. There is a higher influx of digital users such as machine agents, application program interfaces (API), and internet of things devices.
These new users and technologies are straining traditional service management strategies and performance tools and systems to a near breaking point. AIOps was introduced as an IT operations solution to solve these digital transformation challenges.
This article discusses the benefits and challenges of AIOps, how it works, and highlights the best AIOps software.
What is AIOps?
AIOps uses machine learning (ML), artificial intelligence (AI), and analytics techniques to train data to note and address potential problems in real-time automatically. Machine learning algorithms learn from data without necessarily depending on rule-based programming.
AIOps relies on the algorithmic analysis of IT data to help DevOps and IT operations teams to work faster and smarter. AIOps helps these teams detect and react to digital issues early enough to prevent an impact on customers and business operations.
Modern IT environments generate complex and large quantities of data. AIOps enables Ops teams to handle this data, helps prevent outages, thus maintaining uptime, and attaining continuous service delivery.
Benefits of AIOps
The main benefit of AIOps is automating IT operations, enabling IT teams to identify and address outages and slow-downs faster than they could manually. It relies on machine learning and analytics to enhance and automate IT operations.
AIOps platforms leverage big data to collect critical information from IT operations devices and tools. This data helps these platforms identify and address issues in real-time automatically. They also provide traditional historical analytics in the process.
Data-driven decision making
When an organization is adopting AIOps, it is introducing important ML techniques to its IT operations. These include predictive analysis, pattern matching, casual analysis, and historical data analysis.
With these techniques, an organization makes decisions primarily driven by data and provides automated responses to incidences. This eliminates data noise and human error. Data with a lot of meaningless information is referred to as noisy data. This kind of data is challenging to understand and interpret.
Eliminates data silos
Data silos is a common problem for many organizations and a source of inefficiency. A data silo is a colection of data held by one group in an organiation such that it is not fully or easly accessible to other groups. Organizations faced with data silos find that only particular employees or one group of employees has access to the source of data or a specific set of data in the organization. In such a situation, you will find multiple teams storing the same data or complementary data separately.
This leads to wasted resources in terms of storage costs and inhibited productivity. With AIOps, organizations break down data silos and the consequent challenges with full effect across all IT environments. AIOps promotes collaboration among teams and makes data available for all teams to analyze and monitor.
It does this by absorbing data in the form of metrics, events, and logs and taking the data through a set of algorithms to select specific data points. These data points help identify data patterns and correlations and draw inferences. The inferences are then shared across departments, thus promoting a collaborative work environment.
Fast data processing
It takes a lot of time for humans to process large amounts of data. AIOps uses algorithms powered by big data and machine learning to derive cognitive insights from raw datasets.
AIOps reduces metrics such as the Mean Time to Repair (MTTR) and the Mean Time to Detect (MTTD) dramatically. MTTR represents the duration that the IT team takes to eradicate, remediate, or control the organization’s already discovered threats.
MTTD is the duration that the IT team takes to notice a potential threat. The ability to process data at light fast speed saves IT operations (ITOps) teams’ time, effort and reduces the risk of operational fatigue.
Challenges of AIOps
AIOps offers many benefits, but its implementation has its fair share of drawbacks. The implementation of AIOps includes the introduction of significant changes to IT processes. It also transforms the roles and responsibilities of IT teams. Workers see this as a threat as they believe it could lead to reassignment or job loss.
It requires an understanding of AIOps to automate operations successfully. While this tool automates most of the tasks, it is not completely autonomous. That means you need a person in the organization that fully understands its operation.
AIOps mainly automates the more routine tasks that do not require advanced skills. This frees up the IT personnel to focus on higher-value tasks, including process improvements, and system optimization. However, if the employees are limited to tasks that AIOps can perform, this will create a problem.
How AIOps works
We are dealing with extremely complex systems today. Alert noise is a widespread issue for engineers and developers. Following up every alert may lead to alert fatigue, thus critical alerts ending up unnoticed. Relying on human labor to select the high-priority alerts and the harmless quirks may not work in the long run. That is why we need AIOps.
AIOps comes to help IT teams evaluate and act on data more quickly while reducing manual labor. For example, the modern IT environment generates large volumes of highly redundant and noisy data. What AIOps tools do is select the data elements indicating a possible presence of a problem(s) and deriving intelligent insights from this data. It can filter up to 99 percent of noisy data.
In other words, AIOps works by enriching data and offering data intelligence. AIOps does not replace developers’ roles. Rather, it helps developers save on time, enabling greater observability, and delivery of more accurate results.
AIOps’ focus is not limited to the present problems but learns continually to enhance future challenges. Machine learning uses analytics to create new algorithms or change the current ones to identify possible issues and recommend solutions earlier.
List of the top AIOps software
Dynatrace - An artificial intelligence-based monitoring platform for monitoring and optimizing application development and performance, user experience, and IT infrastructure for organizations.
Splunk Enterprise - A software tool that enables users to search, analyze, and visualize data gathered from their IT infrastructure components.
PagerDuty - An incident management platform providing notifications, on-call scheduling, and automatic escalations to quickly help IT teams detect and solve infrastructure problems.
LogicMonitor - A cloud-based network performance monitoring solution for monitoring on-premise, hybrid, and cloud-based data centers and physical devices from a single platform.
Instana - A fully automated application performance management (APM) solution for managing cloud-native applications and microservice architectures.
AppDynamics - An APM and IT operations analytics solution for managing the availability and performance of applications inside data centers and across cloud computing environments.
New Relic - A Software as a Service (SaaS)-based platform that uses application performance index score to monitor and rate application performance.
Datadog - A monitoring tool for cloud-scale applications. It monitors databases and servers through a SaaS-based data analytics solution.
BigPanda - A cloud-based software that uses algorithms to detect and analyze issues in IT systems.
Embracing AIOps is a significant step towards getting to the root cause of IT teams’ problems every day. AIOps prevents alert fatigue and empowers teams to perform their tasks more efficiently.
Peer Review Contributions by: Lalithnarayan C
About the authorEric Kahuha
Eric is a data scientist interested in using scientific methods, algorithms, and processes to extract insights from both structural and unstructured data. Enjoys converting raw data into meaningful information and contributing to data science topical issues.