Anomaly Detection Solutions with Data Science

Use cases

OUTLIERS IN APP METRICS

Detect customer experience issues

We build advanced solutions that collect thousands of application metrics, leveraging machine learning models to detect anomalies in real time. These algorithms detect unexpected drops and spikes in traffic, conversion rates, session durations, and other mission-critical IT metrics. React quickly to issues affecting user experience and minimize service disruptions with near real-time anomaly detection.

OUTLIERS IN SYSTEM METRICS

Detect stability issues

Monitor vast amounts of system metrics in data centers and clouds with advanced outlier detection solutions. Correlate metrics and identify complex anomalous patterns and outliers involving multiple metrics, allowing you to detect issues that single-metric analysis would miss.

ANALYTICS AND INVESTIGATION

Perform root cause analysis in seconds

Experience faster and more convenient incident investigation with tools that emphasize operational efficiency. Analyze metric dependencies and automatically identify segments related to current incidents in the full volume of data. Enable your IT operations teams to quickly perform root cause analysis and troubleshoot issues.

OUTLIERS IN DATA

Detect data quality issues

Integrate models and algorithms into your data processing pipelines and data lakes to continuously monitor data quality. Automatically prevent corrupted or suspicious data from propagating into downstream data processing jobs and analytical reports. Process massive data volumes and integrate with big data platforms and public cloud services like Spark, AWS EMR, and Google Dataproc using algorithms specifically designed for data quality applications in IT environments.

ANOMALY DETECTION FOR SECURITY

Detect security breaches

Solve various fintech, e-commerce, and technology security-related problems such as intrusion detection, unauthorized access attempts, and data breach prevention. Achieve maximum efficiency in protecting your IT infrastructure by combining supervised and unsupervised anomaly detection algorithms with short-term and long-term security monitoring models.

Our clients

RETAIL

HI-TECH

MANUFACTURING & CPG

FINANCE & INSURANCE

HEALTHCARE

How our anomaly detection solutions work

Autonomous anomaly detection using unsupervised algorithms

Leverage unsupervised machine learning algorithms to detect outliers and anomalies, even when “normal” metric patterns and thresholds are unknown. Eliminate the need for manual threshold management, enable detection of complex multi-metric patterns, and scale out to dozens of use cases and thousands of metrics.

Ready for extreme volumes of data

Monitor vast amounts of metrics in real time, detect unusual patterns involving multiple metrics simultaneously, and automate root cause analysis at scale.

Simplified root cause analysis

Integrate these solutions with your incident management and alerting systems to automatically send notifications and create tickets for operations teams. Receive notifications that include the most relevant segments of metrics potentially related to the incident. Quickly react to issues, reduce investigation time, and prevent failure propagation or security-related losses in your environments.

Industries

We develop anomaly detection solutions for several industries including retail, ecommerce, manufacturing, technology, and video games

Retail

Optimize retail operations by monitoring point-of-sale and inventory systems for unusual patterns. Flag potential fraud in transaction data, alert to unexpected stock fluctuations, and detect abnormal e-commerce traffic. This helps prevent system errors, stockouts, and website performance issues, ensuring smooth operations and customer experiences.

Manufacturing

Enhance IT operations by monitoring ERP systems and production management software. Identify anomalies in data flows between systems, predicting potential integration failures. Detect unusual patterns in inventory or order data to prevent disruptions, and optimize network performance by identifying irregular server loads, ensuring reliable access to critical manufacturing applications.

Technology

Anomaly detection is vital for tech, media, and telecom IT, optimizing network infrastructures and cloud services. Identify unusual traffic in content delivery systems to prevent outages, detect anomalies in server performance for proactive maintenance, and spot irregular patterns in telecom networks to prevent service degradation and security breaches.

Wealth management

Anomaly detection optimizes wealth management IT by monitoring portfolio software and financial tools. Identify unusual trading patterns or account activities to alert potential security breaches, detect anomalies in client reporting data aggregation, and spot irregular patterns in market data feeds to maintain real-time financial information integrity.

Insurance

For insurance, anomaly detection enhances policy management and claims processing. Identify unusual claims patterns to flag potential fraud, detect anomalies in risk assessment calculations for accurate pricing, and spot unusual customer behavior in CRM systems to optimize performance and prevent data breaches.

Pharma

Anomaly detection in pharma IT enhances quality control and compliance. It monitors manufacturing processes, flagging deviations in critical parameters to prevent batch failures. In clinical trials, it identifies unusual patterns in patient data, uncovering potential adverse reactions. For drug safety, it analyzes post-market data to detect rare side effects.

Insights

Unsupervised real time anomaly detection

In this article, we present a solution for real-time automated outlier detection in metrics collected from large-scale distributed systems (e.g., clusters of ecommerce applications) or data centers. We focus on handling a large number of metrics and detecting anomaly patterns in combinations of metrics rather than individual time series.

Insights

Add anomaly detection to your data with Grid Dynamics Starter Kit

In this article, we describe our real-time cloud-based anomaly detection reference solution. We cover its design and applicability to the most common use cases: monitoring, root cause analysis, data quality, and intelligent alerting. The solution is AI-driven and implements a flexible approach based on normal state and behavior patterns extraction but does not rely on purely statistical methods. Therefore, it not only can catch suddenly occurring outliers but also can reveal changes in the distribution of very noisy data.

Accelerate your journey to AI

We provide flexible engagement options to design and build an automated anomaly detection solution for your company. Contact us today to start with a workshop, discovery, or proof-of-concept (POC).

Workshop

We offer free half-day workshops with our top experts in application modernization to discuss your stream processing strategy, challenges, modernization opportunities, and industry best practices.

Discovery

If you’re at the application modernization and cloud migration strategy development stage, we can start with a 2–3 week discovery phase to perform an analysis of your existing application portfolio, create a target architecture, and build a modernization roadmap.

Proof of concept

If you’ve already identified a specific use case for application modernization, we can start with a 4–8 week proof-of-concept project to modernize a few key applications to deliver tangible results for your company.

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Anomaly detection