According to Gartner, AIOps is “the application of machine learning (ML) and data science to IT operations problems. AIOps platforms combine big data and ML functionality to enhance and partially replace all primary IT operations functions, including availability and performance monitoring, event correlation and analysis, and IT service management and automation.”
Modern IT systems require real-time visibility across the entire IT estate and application landscape. This helps operation managers understand the relationships, interdependencies, and associations between their systems and their data. In general, AIOps can help in the following five areas:
- Data ingestion
- Noise reduction
- Data-driven decision making
- Machine learning
- Predictive Asset Maintenance
Today’s AIOps platforms can ingest data-at-rest, i.e., historical data, as well as data-in-motion or real-time streaming data. Once ingested, the data can be analyzed immediately, then stored, indexed, tagged with metadata, graphed, and documented. This early data ingestion gives operations a chance to get ahead of the data game, which helps throughout the entire organization.
Modern IT estates produce massive amounts of data – the term ‘Big Data’ was coined over thirty years ago and data volumes have increased exponentially since then, as have the capability of modern-day chips. Today’s software environments generate terabytes of data, and structured, unstructured, and semi-structured data is being combined, often in real-time streams. The data “noise” created in all these systems can be overwhelming. It can also cause performance issues. In rare cases, this “noise” can create risks to the entire IT estate.
An AIOps system can proactively ingest data, cleanse it, and log it into a single repository to create a centralized location for ongoing IT intelligence. AIOps can also log and correlate events to both reduce noise and boost data context. A blueprint of how an efficient system functions properly can be created and this blueprint can be used as a baseline to proactively ensure everything in IT is working both correctly and efficiently. An AIOps system can spot issues that are about to become problematic, then offer solutions to mitigate the issues.
An AIOps system utilizes key ML techniques to help IT operations function properly. Raw data gets ingested into machine learning algorithms use pattern matching to detect anomalies and real-time data correlation to drive new insights from raw data sets. Predictive and prescriptive analysis, historical data analysis, and causal analysis can help understand and improve agent performance as well as increase efficiencies. All of this analysis can proactively help IT systems detect problems before they occur, thereby reducing the meantime to repair.
Being “data-driven” means a company has decided to make strategic decisions based on data analysis and interpretation. Many of the most successful companies, like Google, Apple, Netflix, Facebook, and Amazon are data-driven, and it’s an approach many companies are embracing, for good reason.
Because AIOps tools collect, correlate, catalog, metatag and allow the analysis of data so effectively, it is a powerful weapon that helps a company become data-driven. Its ability to enable data-driven, automated responses helps alleviate eliminate human error and data noise. Company staff can focus 100% on problem resolution rather than on problem detection.
Predictive Asset Maintenance
Predictive Asset Maintenance (PAM) utilizes a combination of good information governance and leading predictive analytical modeling technology to reduce system downtimes. It is used heavily in the oil and gas, airline, and logistics industry, but the technology can improve availability and reliability while reducing costs in a multitude of industries. PAM is a proactive technology that predicts when technology, or parts within, might fail. It offers suggestions on how to resolve a potential issue based upon a database of historical solutions or recognition of recurring problems.
PAM also uses root cause determination to understand correlations between part lifecycles and system failures. Automated pattern discovery and prediction undercovers patterns that may be used to predict incidents with varying degrees of probability. Using the patterns discovered in the previous component lifecycles a timeline of normal behavior can be created and any deviation from this can be considered an anomaly and alerts of warning can be sent to those who can make appropriates fixes or changes.
AIOps is a platform that helps enhance an IT’s operation from the inside. It captures data as it is streaming into an organization and builds a multilayer network of understanding and protection throughout it. With the help of machine learning, an AIOps platform can help augment IT functions, such as event correlation and analysis, anomaly detection, and root cause analysis. AIOps are proactive. They collect data from throughout the IT operation and resolve potential issues proactively, all in real-time. The speed to the problem is considerably reduced.
In today’s competitive business landscape, it’s simply not good enough to let system problems become customer-facing problems. Downtime is unacceptable for many customers. AIOps ensures a business runs smoothly and customers remain satisfied in their experiences with the company.
Most of all, AIOps reduces system downtime, which is important in a world where customers are becoming less and less forgiving. AIOps will ensure companies avoid that next downtime, which could be the one that pushes a customer towards a competitor. Because you never know when that next downtime will become that last downtime and your customer is lost for good.