Nov 10, 2020. By Aleena Mathew
We all know how versatile data is getting. The evolving change of data day to day is unpredictable. Data largely changes in volume and variety to. This varying change requires continuous monitoring to know how much of this change is affecting the system. A research study shows that almost around 60 to 73 percent of the data never gets into good use. Moreover we need to identify whether these data leads to any downtime in the system .That is we require a mechanism to deeply observe our IT environment to gain better visibility. Manual monitoring or identification of data is not a simple or easy process, it requires a lot of time and effort to identify any abnormality in the data. Moreover, manual processing may not always give accurate results.
One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Anomaly detection refers to identification of items or events that do not follow an expected pattern in a dataset and which are usually undetectable by a human expert. The importance of anomaly detection is due to the fact that anomalies in data translate to significant actionable information, that is, provide a deep insight of what is actually troubling the system.
Most IT organizations are evolving around emerging technology. This change in technology will lead to the generation of large variety of data that comes in the form of KPI, logs and traces. Most of the IT data monitoring comes in the observing of huge volume of KPI(Key Performance Indicators), identifying the log and detecting the events that occur in the system. The basic task is to identify any abnormality in the KPI and log as they can provide vital clues as to how well the business is doing as a whole. A given KPI and log needs to be transformed by analysis and visualization from raw data into business insight which provides more in depth details as to which how effectively is the IT system is operating and identify if business requirements are met. There is an overwhelming amount of different metrics and systems to track, making it increasingly difficult to evaluate business patterns and, more importantly, deviations.
AI based Anomaly Detection:
As mentioned before, manual observing or identifying of abnormality is not going to be 100% effective with huge volume of IT data. We need a high end and an advanced mechanism where in which we can identify these abnormality from KPI and logs and also detect events in a much more effective manner. That is where the introduction of artificial intelligence and machine learning capabilities can put into great use. That is, to be simple, bringing in AI based anomaly detection into the picture. By the extensive use of AI and machine learning models, the process of observing and monitoring large volume of KPI and logs are put too an ease. Moreover, by the use of AI real time anomaly detection is also made possible in a finger snap time.
Anomaly detection is about identifying outliers in a time series data using mathematical models, correlating it with various influencing factors and delivering insights to business decisions. Anomaly detection helps in the monitoring of KPI and log by detecting outliers, and informing the responsible authorities to act on the issues. Most IT enterprise use anomaly detection for intrusion detection, for data cleaning and getting an assessment on the health of the IT system.
AI-driven anomaly detection algorithms can automatically analyze datasets, dynamically fine-tune the parameters of normal behavior and identify breaches in the patterns.AI enhances the accuracy of anomaly detection avoiding unwanted alerts and false positives/negatives triggered by static thresholds. For this anomaly detection to take place, we have a trained machine learning model into which the dataset is fed. These models are trained with data which shows the normal flow or the normal working of the system. These trained models are then put to prediction for any deviation in the data set that is coming. When ever an altered data comes, the model will be compared with trained data. When a slight deviation is found from the normal trained it will be altered as an anomaly. By this the anomaly detection for KPI and log are really made simple. No manual inference is needed to understand any outliers that is occurring, the automated AI based anomaly detection mechanism will easily identify these outliers before it can even effect the system.
Anomaly detection is going to become the way of life for IT managers. Advanced machine learning-based model serving pipeline for detecting multivariate KPI anomalies and cross topology log anomalies. In this way, IT operators can effectively monitor the KPI and log data and easily identify anomalies in the system using real time anomaly detection methods.
To know more about AI based Anomaly Detection, please visit our Algomox AIOps Platform Page.