Lookout for Metrics is a fully-managed machine learning tool for monitoring business metrics and tackling dips in business performance.
Lookout for Metrics identifies unexpected changes in data that might indicate critical performance issues” data-credit=”Image: nd3000/ iStock”>Lookout for Metrics identifies unexpected changes in data that might indicate critical performance issues
Image: nd3000/ iStock
Amazon Web Services (AWS) has launched Lookout for Metrics, a fully managed business intelligence service that uses machine learning to monitor performance metrics and detect irregularities.
Lookout for Metrics uses the same
machine learning technology
used internally by Amazon to monitor key performance indicators (KPIs) like revenue, web page views, active users, transaction volume, and mobile app installations.
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The service automatically flags any dips in performance and aims to help businesses identify the root cause of anomalies, such as unexpected falls in revenue, high rates of abandoned shopping carts, spikes in payment transaction failures and increases in new user registrations.
As well as automatically inspecting and preparing the data, Lookout for Metrics groups related anomalies together, summarizes potential root causes and ranks anomalies by severity, allowing enterprises to prioritise which to tackle first.
Lookout for Metrics can be connected to 19 popular data sources, including
Amazon Simple Storage Solution (Amazon S3),
Amazon CloudWatch, Amazon Relational Database Service (Amazon RDS), and Amazon Redshift, as well as software as a service SaaS applications like Salesforce, Marketo and Zendesk.
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“From marketing and sales to telecom and gaming, customers in all industries have KPIs that they need to be able to monitor for potential spikes, dips, and other anomalies outside of normal bounds across their business functions,” said Swami Sivasubramanian, vice president of Amazon Machine Learning for AWS.
“But 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.”
Organizations of all sizes gather metrics to help determine the overall health of the business and keep track of KPIs.
business intelligence (BI) tools
are used to manage data across disparate sources — for example, data warehouses, third-party customer relationship management (CRM) platforms, and operational metrics kept in local data stores.
Yet identifying anomalies can be challenging, AWS explained: traditional rule-based methods look for data that falls outside of arbitrarily defined numerical ranges, and can miss irregularities if ranges are too broad, or are static and don’t respond to changing conditions such as time of day, day of the week, or business cycles.
As a result, when anomalies are detected, it can take time for developers, analysts, and business owners to identify the root cause of the issue.
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Machine learning offers a solution to the challenges posed by rule-based methods thanks to its ability to recognise patterns in vast amounts of information, quickly identify anomalies, and dynamically adapt to business cycles and seasonal patterns, said AWS. Lookout for Metrics eliminates the hard work by automatically detecting anomalies in metrics and helping customers quickly identify the root cause.
Lookout for Metrics requires no specialist machine learning training to use, Amazon said. Also, as the service begins returning results, customers can provide feedback on the relevancy of detected anomalies via the AWS console or the Application Programming Interface (API), continuously improving the accuracy of the results over time.
Lookout for Metrics is available directly via the AWS console as well as through supporting partners in the AWS Partner Network for customers in select US, UK, EU and Asia-Pac regions, with availability for additional regions coming soon.
Sivasubramanian said: “We’re excited to deliver Amazon Lookout for Metrics to help customers monitor the metrics that are important to their business using an easy-to-use machine learning service that takes advantage of Amazon’s own experience in detecting anomalies at scale and with great accuracy and speed.”
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