Getting agile with AI — Log Analysis, Error Detection & Prevention

Getting agile with AI — Log Analysis, Error Detection & Prevention

Latest technological advances have made it easier and cost effective for companies to capture and store huge volume of data. Improved analytics and machine learning techniques are changing the ability of businesses to gain insights around the data thus gathered. Additionally, applying cognitive computing solutions on top of data analysis will elicit better, timely and strategic business decisions.

Server and application logs reveal patterns that indicate potential failures. In an evolving digital economy, exceptional customer experience is the key differentiator that will drive success. Preempting application failures using anomaly detection and pattern recognition from log data can drastically help improve Customer Satisfaction Index (CSI). This becomes all the more critical to digital businesses.


By adopting automated error detection and incident prediction methods (Cognitive Computing technologies) for log analysis, production support in any enterprise application can be enhanced with minimum down time. Due to the inherent dynamic nature of log data, traditional automated search mechanisms cannot be used to identify entries that are capable of causing errors. Forecasting, prediction and alerting of incidents goes a long way in enabling businesses to proactively alleviate huge downtimes. AI/ML and NLP techniques help to process logs and filter out abnormal entries and classify them to incidents that have occurred in the past. Supervised learning methods are used to classify new incidents into buckets for further analysis, classification and prevention.

Discussed thus far are a snapshot of machine learning capabilities that could be used by agile enterprises today for being AI ready tomorrow. These can be extended to automatic dependency detection, root cause analysis, recommend resolutions, auto- updating/ adjusting algorithm and so on.

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