Amazon Lookout for Metrics:
Machine Learning for Business Metrics

Erik Rush | Jun. 15, 2021

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In today’s business environment, organizations collect massive amounts of data as a part of their analytics. Normally, this data is aggregated, filtered by a computer and presented to users through an analytics dashboard. While this is an eminently useful resource, leaving the interpretation of that data to fallible humans leaves a lot of insights potentially unrevealed. Another method of engaging computers to analyze data and provide actionable insights is called anomaly detection.

Using AI, one way that computers can help parse such data is to use that data over time to learn what the normal operating metrics are for an organization. The strength of anomaly detection comes into play when the computer is analyzing data on thousands of analytics variables continually. This allows it to detect much smaller anomalies within data sets. For example, if a retail outlet has a slower month than usual compared to past years, the insights provided with give users a good idea of where to look for potential causes. With digital anomaly detection, your organization can be alerted to more opportunities and potential pitfalls. 

Smaller Anomalies, Bigger Insights

In their ongoing quest to provide organizations with better insights into their internal metrics, Amazon Web Services (AWS) launched a tool in March that uses machine learning to identify anomalies in business data. AWS users can now engage Amazon Lookout for Metrics through the AWS console, as well as having the ability to work with AWS partners for customized deployments of this service.

Amazon Lookout for Metrics helps customers monitor the most important metrics for their business, such as revenue, web page views, active users, transaction volume, and mobile app installations with greater speed and accuracy. The service makes it easier to diagnose the root cause of anomalies like unexpected dips in revenue, high rates of abandoned shopping carts, spikes in payment transaction failures, increases in new user sign-ups, and many more—all with no machine learning experience required on the part of the user.

Third-Party Integration Made Easy

Lookout for Metrics uses machine learning to automatically detect and diagnose anomalies in business and operational data; in a few clicks, users can connect Amazon Lookout for Metrics to data stores like Amazon S3, Amazon Redshift, and Amazon Relational Database Service (RDS) and third-party SaaS applications, such as Salesforce, Servicenow, Zendesk, and Marketo. 

Amazon Lookout for Metrics automatically inspects and prepares the data from these sources to detect anomalies with greater speed and accuracy than manual computer aggregation and filtering followed by human analysis. Users can also provide feedback on detected anomalies, which allows them to fine-tune results and improve accuracy over time. This greatly simplifies diagnosing anomalies by grouping those related to the same event together and sending an alert that includes a summary of the potential cause. Amazon Lookout for Metrics also ranks anomalies in order of severity so that users can prioritize their efforts.

“Catching and diagnosing anomalies in metrics can be challenging, and by the time a root cause has been determined, much more damage has been done than if it had been identified earlier,” said Swami Sivasubramanian, Amazon’s head of Machine Learning. “Machine learning offers a compelling solution to the challenges posed by rule-based methods because of its ability to recognize patterns in vast amounts of information, quickly identify anomalies, and dynamically adapt to business cycles and seasonal patterns.” 


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