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Description

Minimize false positives and leverage machine learning (ML) to effectively identify anomalies in business performance indicators. Investigate the underlying causes of these anomalies by clustering similar outliers together for analysis. Provide a summary of these root causes and prioritize them based on their impact. Ensure a smooth integration with AWS databases, storage services, and external SaaS platforms for comprehensive metrics monitoring and anomaly detection. Set up automated alerts and responses tailored to the detection of anomalies. Utilize Lookout for Metrics, which employs ML to both discover and analyze anomalies in business and operational datasets. The challenge of recognizing unexpected anomalies is compounded by the limitations of traditional manual methods that are prone to errors. Lookout for Metrics simplifies the detection and diagnosis of data inconsistencies without requiring any expertise in artificial intelligence (AI). Monitor irregular fluctuations in subscriptions, conversion rates, and revenue to remain vigilant about sudden market shifts, ultimately enhancing strategic decision-making capabilities. By adopting these advanced techniques, businesses can improve their overall performance management and response strategies.

Description

IBM Z Anomaly Analytics is a sophisticated software solution designed to detect and categorize anomalies, enabling organizations to proactively address operational challenges within their environments. By leveraging historical log and metric data from IBM Z, the software constructs a model that represents typical operational behavior. This model is then utilized to assess real-time data for any deviations that indicate unusual behavior. Following this, a correlation algorithm systematically organizes and evaluates these anomalies, offering timely alerts to operational teams regarding potential issues. In the fast-paced digital landscape today, maintaining the availability of essential services and applications is crucial. For businesses operating with hybrid applications, including those on IBM Z, identifying the root causes of issues has become increasingly challenging due to factors such as escalating costs, a shortage of skilled professionals, and shifts in user behavior. By detecting anomalies in both log and metric data, organizations can proactively uncover operational issues, thereby preventing expensive incidents and ensuring smoother operations. Ultimately, this advanced analytics capability not only enhances operational efficiency but also supports better decision-making processes within enterprises.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AWS Lambda Yes 
Amazon CloudWatch Yes 
Amazon Redshift Yes 
Amazon S3 Yes 
Amazon Simple Notification Service (SNS) Yes 
IBM Z No 

Integrations

AWS Lambda No 
Amazon CloudWatch No 
Amazon Redshift No 
Amazon S3 No 
Amazon Simple Notification Service (SNS) No 
IBM Z Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/lookout-for-metrics/

Vendor Details

Company Name

IBM

Founded

1911

Country

United States

Website

www.ibm.com/products/z-anomaly-analytics

Product Features

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Alternatives

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