Irrespective of the industry, every business deploys various procedures, tools, and strategies to monitor and improve business performance. Various metrics are defined for performance monitoring tools to assess the performance of a business process or function, which can be something like the number of investments, customer satisfaction evaluation, monitoring number of orders placed or clicks in an ad campaign. Due to inappropriate tools and manual processes to evaluate these metrics, businesses find it difficult to identify, evaluate, alert, and fix the roadblocks or deviations in process performance. To solve this problem, AWS has introduced AWS Lookout for Metrics which is fully powered by machine learning capacities.

How AWS Lookout for Metrics Intelligently Detects Anomalies

AWS Lookout for Metrics detects variations or anomalies in various business processes based on metrics data defined to assess the performance of business operations (revenue performance, purchase transactions, retention rate, sales, marketing, and so on). AWS Lookout for Metrics effortlessly integrates with various existing data sources like Amazon S3, Redshift, RDS, CloudWatch, Salesforce, ServiceNow, Google Analytics, and so on.

Using AWS Lookout for Metrics, one can easily trace out anomalies and their root cause. It is not mandatory for businesses to input historic data to use Lookout, instead Lookout analyses real-time data streams and analyses data based on the metrics defined. It instinctively examines and organizes the data sourced from various sources to identify glitches swiftly and with precision, compared to the conventional procedures for anomaly identification.

To improve the precision of detection, users can provide feedback based on the detected anomaly results. Powered by machine learning capabilities, AWS Lookout can learn from the feedback and improvise the process for greater precision and performance. The extent of time taken by Lookout for Metrics to learn and discover inconsistencies differs with the data collected. The beauty here is that Lookout analyses the data and then chooses the best-suited machine learning algorithm for the detection process. Lookout for Metrics is responsible for choosing the right algorithm, training, structuring, and deploying the models. To use Lookout for Metrics, users do not require machine learning expertise.

Figure: Working of  AWS Lookout for Metrics

Lookout for Metrics analyses discovered anomalies, creates clusters of all the associated ones, assigns each cluster to a single event, and sends an alert that incorporates a synopsis of the probable reason for the anomaly. Based on the severity of the anomaly, they are graded and users can define their priority based on the impact of the anomaly on the business. Lookout for Metrics facilitates customers to give feedback on the identified anomalies, which can be utilized to enhance the precision and performance of the machine learning model incessantly.

As with other AWS solutions, Lookout for Metrics is pay-per-use, without any additional costs or minimum spending requirements. Billing for Lookout for Metrics is based on the number of metrics considered each month, regardless of the number of times they are evaluated. As the number of metrics used to analyze increases, the pay per metric decreases. Say, if for a particular metric no data points are examined in a month, then businesses don't pay anything.

Lookout for Metrics is great for businesses to obtain scalable solutions and continuously monitor their business and operations data to detect anomalies and fix them to boost their business performance.