Before AI can transform your business, fix your data
AI can’t deliver business value until your data does. In many enterprises, legacy pipelines, inconsistent definitions, and siloed datasets introduce friction that prevents scalable, production-grade AI. These technical debts slow down innovation.
Grid Dynamics helps enterprise data teams modernize their stack end-to-end—with cloud-native architectures, automated lineage and observability, and scalable DataOps pipelines. We implement robust governance models and metadata management frameworks to ensure your data is accurate, accessible, and AI-ready at scale.
It all starts with a data assessment where we provide a clear view of your current state, a target state design that considers your ambitions and needs, and a clear actionable plan to get there.
Enabling enterprise-wide data modernization to support AI maturity
To successfully implement data techniques that align with AI maturity, enterprises must modernize their data architecture and operational model.
REDEFINE THE OPERATIONAL MODEL
From data gatekeepers to AI accelerators
Transform your data team from custodians to catalysts. Let business units own their data while your specialists build the AI ecosystem that powers them. Your new data talent equation puts business teams in charge of collection and quality, frees engineers to master tooling and governance, and empowers scientists to craft the prompts and models that matter. Position your data team as the vital connective tissue between vision and execution—the accelerant for enterprise-wide transformation.
SECURE & ACCESSIBLE DATA
Embrace data as a product mindset
Treat data as a product, not a byproduct. Clear ownership, intuitive discovery, and secure access transform information from hidden asset to strategic advantage. AI-powered catalogs replace tribal knowledge, AI capabilities enable self-service analytics, dynamic controls like data masking and lineage protect without stifling innovation, and modern data build tools bring software discipline to data transformation. The result? Data products that drive decisions instead of collecting dust.
MODERN DATA ARCHITECTURES
Ground your data in reality
Enable meaningful AI interactions grounded in enterprise knowledge with Retrieval-Augmented Generation (RAG) architectures. RAG ensures responses are accurate, relevant, and traceable to source documents—particularly valuable for proprietary information and rapidly changing data. By generating vector embeddings that capture semantic meaning, RAG enables AI to understand relationships between concepts while providing clear lineage to verified sources. This means reduced hallucinations and context-aware AI applications for customer support, knowledge assistance, and intelligent search.
SKILLS & COMPOSITION
Build an AI-era data team
Prepare your workforce for the AI era by evolving skillsets to drive performance, innovation, and long-term AI success. Transform data scientists into AI engineers specializing in prompt engineering, advanced observability, and automation. Expand ML engineers’ expertise in LLM infrastructure, and RAG to support scalable AI applications. Leverage AI to automate repetitive processes, optimize workflows, and implement structured modernization—freeing your teams from operational bottlenecks to focus on high-impact innovation.
Establish advanced AI-enabled DataOps practices for continued success
As data practices and operating models evolve, traditional data operations simply can’t keep up. Advanced DataOps techniques are critical to the success of data modernization efforts, as they help businesses manage modern data complexity. Enterprises can standardize processes to efficiently ingest and process both structured and unstructured data, implement robust frameworks for prompt engineering and vectorization to optimize unstructured data management, and enforce guardrails and semantic caching to ensure data integrity and reliability.
These practices help organizations adapt to evolving data formats and use cases, aligning storage and retrieval strategies with business needs. However, no single tool fits all purposes—which is why we help businesses establish customized DataOps processes and best practices to maintain long-term efficiency, scalability, and AI readiness.
Leverage AI to power DataOps
AI is also a tool for data teams to drive more efficient processes. It can be applied across all areas data teams are responsible for, such as:
- Catalog and metadata management services: AI helps manage metadata with robust schema management and full-text search, maintains a glossary-based knowledge base, provides lineage tracking, and seamlessly integrates with quality checks and pipelines.
- Data quality services: AI generates automated quality checks, deploys anomaly detection algorithms, and integrates tightly with existing data pipelines and metadata systems, providing continuous, proactive data integrity.
- Data pipeline orchestration: Complex, interdependent data workflows become scalable through AI-powered orchestration, which handles hundreds of flows and proactively alerts teams of issues.
- Access control: Ensuring compliance with privacy regulations such as GDPR and CCPA, AI supports rigorous dataset- and field-level security through encryption and access governance.
Get started: Data platform assessment
Accelerate your organization’s ability to embrace AI with a modern data platform. Grid Dynamics offers a comprehensive data readiness assessment that looks at your existing infrastructure and provides you with clear, actionable steps to mature with a recommended roadmap.
* Timeline may vary based on organizational complexity and scope
Case studies
Data modernization starter kits
Industries
Retail & CPG
Unify fragmented customer data across physical and digital touchpoints to enable personalized experiences at scale. Modernized data foundation powers AI applications from demand forecasting to dynamic pricing and inventory optimization, creating seamless omnichannel journeys. Transform siloed data into actionable insights that drive loyalty, reduce stockouts, and optimize supply chain resilience.
Wealth management
Convert disparate client financial data into comprehensive profiles that power hyper-personalized investment recommendations and risk assessments. Your advisors gain AI-enhanced tools that identify portfolio opportunities while ensuring regulatory compliance. This data-first approach enables scaled personalization that combines human expertise with algorithmic precision to strengthen client relationships and increase assets under management.
Manufacturing
Transform operational data from equipment sensors, production systems, and supply networks into a unified view that powers predictive maintenance and quality optimization. Your modernized data architecture enables real-time decision support across facilities, reducing downtime and streamlining operations. This approach bridges IT/OT gaps to create AI-enabled manufacturing excellence with measurable efficiency improvements.
Our latest innovations in data modernization
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