Grid Dynamics helps technology and analytics leaders design and build powerful infrastructures for a data-driven enterprise:
Transform your data and analytics capabilities
Data engineering and analytics are now mission-critical components in virtually all industries: reporting, insights, and optimization of business decisions all critically depend on the data infrastructure.
Modern data platforms include many features and services ranging from elastic storage and computing to metadata discovery and anomaly detection in data. We have built data platforms and services for many Fortune 500 companies, as well as smaller clients, and we offer world-class expertise, engineering skills, and detailed solution blueprints in this area.
In-stream aggregation is an indispensable tool for applications that require processing an extremely large number of events or provide business users with near real-time reporting. We develop in-stream and real-time data solutions for the largest technology, media, and advertising companies offering unique expertise in building fault-tolerant, scalable, and performant pipelines using Spark, Kafka, Flink, Beam, and cloud-native technologies.
Self-Service data and analytics
Although data engineering, ad hoc analytics, and data science technologies are widely adopted in multiple industries, many companies struggle to make these capabilities available to all teams and organizations who need them. We help to remove barriers and build self-service solutions that require minimal engineering support and reduce the gap between prototyping and productization.
AI and decision automation
Data platforms and self-service capabilities provide a foundation for improving business performance using data-driven methods, but actual data commercialization is a separate challenge that requires considerable domain expertise and data science skills. We specialize in optimizing marketing, supply chain, and other enterprise operations using AI/ML methods and help our clients to build these capabilities on top of generic data processing and analytics infrastructure.
We develop data platforms and analytic tools for many companies in retail, telecom, video games, finance, and other industries.
Retailers need comprehensive data solutions to store, process, and analyze customer data, transactions, pricing data, and supply chain information. We help our retail clients to create data lakes, pipelines, and ML platforms that enable advanced personalization and supply chain optimization use cases.
Large telecom companies need to process customer and operational data on an extreme scale. We help our telecom clients to build data solutions that support advanced customer analytics, call center optimization, service operations, and more.
Video game companies collect huge volumes of data from in-game instrumentation and digital marketing channels. We help them to build data processing infrastructures and pipelines that enable advanced business analytics and data-drive optimization.
Insurance and finance
We help insurance and fintech clients create data and machine learning platforms that support productization and operation of hundreds of models needed for risk scoring, quantitative portfolio management, and customer analytics.
Manufacturing companies use data platforms for many applications, including IoT, predictive maintenance, price management, transportation, and distribution. We help our manufacturing clients to build both foundational data capabilities and domain-specific components related to production and B2B operations.
Global technology companies process billions of events and petabytes of data every day, and we help them to build data platforms, real-time pipelines, and analytics tools at an extreme scale.
Our latest thinking on data and analytics
Turn data into insight faster with Grid Dynamics Analytics Platform Starter Kit on AWS cloud
In this article, we present an Analytics Platform Starter Kit developed by Grid Dynamics in collaboration with AWS. This solution helps to handle increasing volumes, velocities, and varieties of data without sacrificing quality, security, or accessibility.
Why you need data quality automation to make data-driven decisions with confidence
In this blog post, we review a typical data flow, causes of data corruption, how to set confidence levels, and data quality goals. Lastly, we go under the hood and explain our solution to monitoring data quality in production in real-time.
FINANCE & INSURANCE
Accelerate your innovations
We provide flexible engagement options to design and build a customer intelligence and analytics solution for your enterprise. Contact us today to start with a workshop, discovery, or POC.
We offer free workshops with our top experts to discuss your challenges, potential areas of improvement, and industry best practices.
Proof of concept
If you have already identified a specific use case that can be solved using data science and other customer intelligence technologies, we usually can start with a 4–8 week proof-of-concept project to deliver tangible results for your enterprise.
If you are in the stage of requirements analysis and strategy development, we can start with a 2–3 week discovery phase to identify right use cases for customer intelligence and personalization, design your solution using industry best practices, and build an implementation roadmap.
All digital transformation solutions
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