Many enterprises are either on the path of becoming data-first or already leveraging their data assets. Such a digital journey gets fuelled with data – an enormous amount of it, that is generated daily to empower such data-powered insights.
But maintaining data assets is not a trivial task. Infrastructure teams frequently face incidents that disrupt the data pipelines – which are well captured in logs that require an additional layer of analysis. This can also be combined with data from other streams, such as server observational analytics and third-party datasets, providing deeper insights into specific technologies and methodologies. These large datasets possess enormous amounts of valuable information that is yet untapped but is crucial in improving incident management strategies.
AIOps stands for Artificial Intelligence for IT Operations and has the potential to make seamless incident management a reality. It deploys machine learning models using big data to generate insights that proactively manage large-scale incidents.
It also helps companies manage their IT infrastructure and services, enabling them to respond quickly to any operational issues.
Figure 1: Gartner - AIOps
Incident Management with Big Data
Incident management is a critical component of IT operations, as it helps organizations quickly identify and resolve system problems. However, with the rapid increase in data generation, extracting meaningful insights from this data can be challenging to monitor incidents proactively. Big data technologies can help companies process and analyze large amounts of data, thereby providing predictive insights that can help manage incidents at scale.
Preventing Incidents
One of the prime examples of big data tech is using real-time streaming data analytics, which helps companies monitor their IT systems and quickly identify patterns or anomalies that indicate a potential problem. For example, an e-commerce company tracks the traffic on their website through the various server requests they are experiencing in real-time; a sudden surge in traffic can mean either a bot attack or an anomaly in product attributes such as price. This enables infrastructure teams to prevent or resolve incidents, reducing downtime and maintaining robust systems. A large part of preventing any incident to not recur in the future involves analyzing the root cause of its first occurrence. AIOps explains the RCA (Root Cause Analysis) to the IT teams by weeding out operational inefficiencies, thereby maintaining reliable infrastructure.
Automating Incident Management
Big data technologies help companies automate many tasks involved in incident management, such as classifying incidents or predictive maintenance. Such dynamic monitoring uses machine learning algorithms to improve the efficiency of its incident management processes. It saves the IT teams a lot of time by routing the issue to the appropriate team for a swift resolution. Such algorithmic issue management improves the uptime for mission-critical applications and drastically reduces the risk of human error.
Figure 2: Wipro Digital highlighting the key characteristics of AIOps
Cyber Security
AIOps also helps companies improve the security of their IT environments. Organizations can get intelligent alerts that identify potential security threats by analyzing large amounts of data. For example, big data technologies monitor network traffic and identify patterns that may indicate a security incident. Such early threat detection algorithms route the IT teams to take action to prevent data breaches and other security incidents.
The organizations leverage AIOps to improve the overall performance of their IT systems, as shown below:
Figure 3: Freepik with illustration by author
Challenges of working with big data
The excess of everything is bad, and that applies to data as well. But recognizing and mitigating some risks helps organizations get better results from their data. Some of the challenges while working with big data are listed below:
Data Volume: One of the biggest challenges of working with big data is the sheer volume of data that needs to be processed and analyzed. This can make it difficult to store and manage the data and make it tough to extract meaningful insights.
Data Variety: Data comes from various sources, including structured and unstructured forms, which can make it gruesome to integrate, making it difficult to ensure accuracy and consistency.
Data Velocity: Data is often generated in real-time, which aggravates the need to process and analyze it quickly to aid decisions in a timely manner.
Data Veracity: Data coming from disparate sources can often be complex and unstructured, making it difficult to ensure accuracy and reliability.
Data Governance: With big data, there are increasing concerns around managing and controlling access to the right authority. Enterprises are adopting smart data governance measures to comply with privacy and security regulations.
Data Processing: It requires specialized tools and technologies for processing and analyzing the data – which is a non-trivial task for organizations that lack the expertise and resources to invest in new technologies.
Why is AIOps Trending?
The IT systems have become complex as more and more data gets accumulated. AIOps utilizes the power of ML algorithms to identify, alert, recommend, and resolve infrastructure issues. The algorithms mine through heaps of data to detect patterns and anomalies that may highlight a problem. Directional and proactive alerts are paramount to data infrastructure teams – they reduce downtime and improve the system’s overall performance by rapidly fixing bugs.
In addition to managing incidents, AIOps prove valuable to companies in improving the security of their IT environments. ML algorithms can spot potential security threats, which is a tough task for traditional signature-style threat detection systems. Organizations can reduce the risk of data breaches and other security incidents, which is not just costly affair but also poses reputational damage.
AIOps in Autoscaling
AIOps also enables companies to automate many of their server operations, including but not limited to – provisioning and configuring new servers or scaling resources as needed. Such automatic operations reduce the need for manual intervention and lift the system's overall efficiency.
Further, AIOps helps autoscaling servers using machine learning algorithms to predict future server traffic and usage patterns. These predictions can automatically adjust the number of servers to match the expected demand, reducing the risk of over-provisioning or under-provisioning resources. Additionally, it comes with the benefit of optimizing the placement of resources within a data center to ensure that they are being used as efficiently as possible, contributing to cost optimization – an initiative that is always dear to organizations charting out their digital journey.
Summary
AIOps is an emerging trend that has the potential to revolutionize the way companies manage their IT operations. It analyzes large amounts of data by leveraging AI and ML algorithms to help companies quickly identify IT issues. Besides, it also improves visibility into their IT environment, automates many of their IT operations, and enhances the security of their IT systems. As technology continues to evolve, we can expect to see more such benefits from AIOps in the times to come.
