Anomaly detection solutions with Data Science

Use cases

OUTLIERS IN APP METRICS

Detect customer experience issues

We build solutions that collect thousands of application metrics and use machine learning models to detect anomalies in real time. Our algorithms can detect unexpected drops and spikes in traffic, conversion rates, session durations, and other mission-critical business metrics. Near real-time anomaly detection helps to quickly react to issues that affect user experience and minimize losses.

OUTLIERS IN SYSTEM METRICS

Detect stability issues

Our outlier detection solutions can monitor an extremely large number of system metrics in data centers and clouds, correlate them, and identify complex anomalous patterns and outliers that involve multiple metrics and cannot be detected through the analysis of individual metrics in isolation.

ANOMALY DETECTION IN IMAGES

Detect equipment failures using IoT data

Our data scientists have analyzed numerous case studies and real-life projects to develop a comprehensive toolkit of computer vision algorithms for anomaly detection. We work with a wide range signals and data sources, including IoT sensor data, infrared and X-ray imagery, and video streams, to detect anomalies and prevent larger failures and outages.

ANALYTICS AND INVESTIGATION

Perform root cause analysis in seconds

We put a lot of emphasis on the operational aspect of anomaly detection, including speed and convenience of incident investigation. Our tools analyze the dependencies between metrics and automatically identify the segments potentially related to the current incident in the full volume of data. This helps operations teams to quickly perform root cause analysis and troubleshoot the issue.

OUTLIERS IN DATA

Detect data quality issues

We integrate our models and algorithms into data processing pipelines and data lakes to continuously monitor data quality and automatically stop damaged or suspicious data from propagating into downstream data processing jobs and analytical reports. We use algorithms that are specifically designed for data quality applications and are capable of processing extremely large data volumes and integrating with big data platforms and public cloud services such as Spark, AWS EMR, and Google Dataproc.

ANOMALY DETECTION FOR SECURITY

Detect security breaches

Our solutions for fintech, ecommerce, and video games help to solve various security-related problems such as fraud detection, intrusion detection, and loss prevention. We combine supervised and unsupervised anomaly detection algorithms as well as short-term and long-term fraud detection models to achieve maximum efficiency.

Our clients

RETAIL

HI-TECH

MANUFACTURING & CPG

FINANCE & INSURANCE

HEALTHCARE

Analytics Starter Kit

Accelerate the development of IoT analytics solutions in Google Cloud

Grid Dynamics and Google teamed up to create a starter kit for industrial IoT analytics using Vertex AI platform. The solution is designed to reliably evaluate system health and detect anomalies in a large number of IoT metrics received from multiple sensors and machines.

How our anomaly detection solutions work

Autonomous anomaly detection using unsupervised algorithms

We extensively use unsupervised machine learning algorithms to detect outliers and anomalies even in settings where “normal” metric patterns and thresholds are unknown. This eliminates the need for manual threshold management, enables the detection of complex multi-metric patterns, and allows scale-out to dozens of use cases and thousands of metrics.

Ready for extreme volumes of data

Our solutions are designed to monitor a very large number of metrics in real time, detect unusual patterns that involve multiple metrics at the same time, and automate root cause analysis at scale.

Simplified root cause analysis

We integrate our solutions with incident management and alerting systems to automatically send notifications and create tickets for operations teams. Notifications include most relevant segments of metrics that are potentially related to the incident. This helps to quickly react to incidents, reduce the amount of time needed for investigation, and prevent failure propagation or security-related losses.

Book

Introduction to Algorithmic Marketing

Introduction to Algorithmic Marketing is a comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers.

Industries

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

Retail

Large retailers commonly operate complex ecosystems of transactional applications, marketing automation software, and customer data management systems. We create smart solutions that continuously monitor streams of metrics from hundreds of applications, detect issues in real time, and automatically send alerts with comprehensive reports to operations teams.

Manufacturing

Manufacturers collect large amounts of data from sensors and IoT that need to be monitored and analyzed. We help them to automate these processes and identify anomalies using data science.

Technology

Technology companies often monitor streams of online transactions to detect technical issues, fraud, and other types of abnormal behavior. We help them to build solutions that automate these processes.

Video games

Virtual currency fraud and security and stability issues are common concern for video game developers and publishers. We help our clients in these industries to continuously monitor metrics collected from applications and to isolate and analyze issues.

Learn more about solutions for your industry

Solutions for IT operations

Solutions for Industry 4.0

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 the 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.

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.

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.

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Anomaly detection