Best Consulting Companies to Modernize Legacy Analytics Platforms and Data Warehouses (2026)

Why Legacy Analytics Platforms Are Blocking Enterprise AI Initiatives

Legacy data warehouses are built on old architectures and do not support real-time processing. Since they were designed for batch reporting and historical analysis, the cost of inaction is substantial. 

Yesterday’s architecture vs. the AI of Today

Legacy data warehouses were brilliantly engineered for a different era. They were designed to ingest structured data (such as transactional tables and spreadsheets) on a batch schedule, process it, and give static reports. But enterprise AI requires a fundamentally different architecture. AI systems need to process structured and unstructured data, react in near real time, and continuously serve context to machine learning models and intelligent agents. Modern BI and AI agents query data simultaneously across thousands of nodes. The infrastructure cannot handle the speed or the volume of data.

Real-time Data Access

AI applications need fresh operational data, not yesterday’s things. But enterprise AI requires a fundamentally different architecture. AI systems need to process structured and unstructured data, react in near real time, and continuously serve context to machine learning models and intelligent agents. But modern enterprise initiatives require continuous data ingestion via cloud-native streaming platforms.

Limitations of Legacy Analytics Platforms

Legacy Analytics platforms have certain limitations, such as 

• Batch-oriented architecture.
• High Infrastructure costs.
• Poor support for unstructured data
• Rigid schemas.
• Operating costs become higher.
• Slower Time to Market.
• Innovation becomes less.
• Data preparation takes most of the time.

What Defines Legacy in the Analytics and Data Warehouse Context?

Legacy is often defined as harder to recognize in the analytics world. A data warehouse may still run reliably, producing reports and supporting executives. But it cannot efficiently support modern requirements such as real-time analytics, cloud scalability, self-service BI, automated data pipelines, and ML. So it lacks the flexibility, scalability, and speed required for modern analytics and AI. Many legacy analytics platforms are

• Stable and Business-critical
• Highly customized
• Trusted by many teams
• Maintenance is high
• Difficult to adapt to changes

Still, Teradata, Oracle Exadata, on-premises Microsoft SQL Server, and traditional data warehouses work correctly, but they support and are designed for yesterday’s needs.

On-Premise Batch ETL

Legacy environments rely on traditional Extract, Transform, Load (ETL) pipelines that run on a rigid schedule. In this model, data is pulled from production databases, crunched in batches, and loaded into the warehouse while the business sleeps. 

• Executive dashboards are only accurate as of “Yesterday at midnight”.
• Running a major data transformation during business hours slows down the entire company's reporting.
• Your engineering team spends hours managing complex "batch windows" and fixing failed nightly jobs.

Outdated BL layers

Legacy environments depend on semantic layers and reporting tools built for static dashboards. Some of the challenges faced are slow dashboard refreshes, limited ad hoc exploration, and heavy IT dependence. These BI layers were optimized for a small set of standardized reports.

Siloed Data 

Legacy architectures were limited by physical storage and compute constraints. To cope, IT departments split specialized subsets of the data warehouse designed for specific teams (e.g., one for Finance, one for Marketing, one for HR), but it has led to conflicting KPIs, duplicate data pipelines, and governance inconsistencies.

Unstructured data

Legacy warehouses were designed to only handle structured data and not unstructured ones, such as PDFs, Emails, images, Contracts, Chat transcripts, and sensor logs. But these are important as they are the inputs for generative AI and advanced analytics.

Lack of Real-time Streaming

Modern businesses need data continuously, but legacy platforms usually depend on periodic batch loads. Without streaming capabilities, organizations struggle to support:

• Live operational dashboards
• Event-driven automation
• IoT analytics
• Agentic AI workflows

Key Services to Look for in a Legacy Analytics Modernization Consulting Firm

Modernizing a legacy analytics platform is more than a lift-and-shift. It is a shifting of the whole data architecture, pipelines, governance model, and AI strategy so the organization can support real-time analytics, self-service BI, and enterprise AI.

• Cloud Data Warehouse(DW) migration: It is one of the most important services in migrating your legacy warehouse to a modern cloud platform. The top IT consulting company should provide a comprehensive migration framework. Look for partners with proven experience migrating to major platforms like Snowflake, Google BigQuery, AWS Redshift, or Microsoft Azure. They must be able to assess your current workloads, minimize operational downtime during the cutover, and optimize your new cloud architecture to prevent unexpected billing surprises. 
• ETL/ELT Modernization: Traditional ETL pipelines are often slow, brittle, and difficult to maintain. The consulting partner must be adept at transforming legacy ETL processes into modern, code-based, or low-code ELT (Extract, Load, Transform) pipelines using tools like dbt, Apache Airflow, or cloud-native integrators. By shifting the transformation step to the cloud data warehouse itself, they help you achieve near-real-time data processing and significantly reduce compute overhead. 
• Data Lakehouse Architecture: The modern standard is the Data Lakehouse—a hybrid architecture that pairs the structure and governance of a warehouse with the low-cost storage and flexibility of a lake. A consulting partner should be able to design and implement a lakehouse architecture that combines the governance of a warehouse with the flexibility of a data lake. A lakehouse approach creates a scalable foundation for analytics and AI.
• AI-readiness Assessment: A modern analytics platform should be supportive of generative AI, machine learning, and intelligent agents. Consulting firms should evaluate whether your data environment is ready for AI initiatives.
• Governance & Security: Security is the biggest concern. Though the cloud expands your perimeter, we should never take security as an afterthought. The ideal partner integrates robust data governance and security from day one. This includes establishing automated data lineage, role-based access controls (RBAC), data masking, and ensuring strict compliance with industry regulations such as GDPR, CCPA, or HIPAA. 

Best Consulting Companies for Legacy Analytics Platform Modernization and Data Warehouses

Below are the top IT consulting companies modernizing legacy warehouses and analytics platforms.

1. Entrans

Entrans is an AI-first digital engineering partner that helps organizations through Agentic AI, generative AI, and legacy modernization services. Overall, we provide end-to-end legacy modernization services that bundle assessment, dependency mapping, technical debt mapping, cloud migration, code refactoring, microservices transformation, API-first integration, and data modernization.

Key Services of Entrans

• Entrans provides end-to-end legacy modernization services for analytics platforms and data warehouses by migrating from legacy systems such as Teradata, including Snowflake, AWS Redshift, and Google BigQuery, to modern cloud architecture. Our services include platform assessments, migration planning, schema and SQL conversion, ETL/ELT modernization, data lake house design, BI modernization, governance, and AI-readiness assessments. 
• Well known for expertise in Snowflake and Databricks implementation, we optimize the entire modern analytics technology stack from Dbt-powered ELT pipelines to Apache Airflow orchestration.
• With a structured migration framework, we guarantee minimal operational downtime, robust data governance, and up to a 50% reduction in infrastructure overhead, delivering a secure, highly optimized, and AI-ready enterprise data foundation.
• We have deep expertise in Snowflake, Databricks, Amazon Redshift, and Google BigQuery with Apache Airflow, Apache Kafka, Tableau, and Power BI, AWS, Azure, and Google Cloud. We also have deep experience in Teradata to Snowflake migrations, modern lakehouse migrations, and modern lakehouse implementations.

Navigating the complexities of legacy to AI modernization can be helped by establishing an automated framework that balances infrastructure costs with long-term computational flexibility. 

2. Accenture

Accenture is a leading Indian IT giant that provides legacy system modernization consulting services for analytics platforms and data warehouses. Their approach is towards breaking down legacy data silos to create a unified, modern data foundation in the cloud.

Key Services of Accenture

• Accenture offers end-to-end modernization services for analytics and data warehouse platforms. They help in migrating from legacy environments such as Teradata, Oracle, and SQL Server to modern cloud architectures. 
• Accenture possesses extensive analytics stack expertise, seamlessly integrating advanced data lakehouse architectures, modern ELT pipelines, and next-gen BI tools. 
• The company supports Teradata-to-cloud migrations using automation accelerators and industry frameworks, and its dedicated Snowflake Business Group includes more than 5,000 certified professionals focused on data modernization and AI enablement. 

3. Deloitte

Deloitte is a leading global provider of audit, assurance, and consulting services. Coined under “Big Four” for accounting and professional services, they describe their legacy analytics and warehouse modernization services as an end-to-end framework to build "insight-driven organizations."

Key Services of Deloitte

• Deloitte provides end-to-end legacy modernization services for analytics and data warehouse platforms, which help organizations move from on-premise systems such as Teradata, Oracle, and SQL Server to cloud-native architecture.
• They follow strict governance, security, managed services, and platform assessments.
• Deloitte shows its deep expertise in Snowflake, Databricks, Google BigQuery, and Amazon Redshift, along with dbt, Apache Airflow, and Tableau. They are effectively turning fractured legacy systems into highly optimized, insight-driven, and AI-ready data foundations for global enterprises. 

4. Cognizant

Cognizant is a global professional services company providing legacy modernization services. One of Cognizant’s key strengths is its "Neuro IT Operations" platform; they focus on transforming legacy systems into “AI-fueled innovation engines”.

Key Services of Cognizant

• Cognizant provides end-to-end legacy modernization services for analytics platforms and data warehouses, helping enterprises migrate from Teradata, Oracle, SQL Server, Hadoop, and other legacy environments to cloud-native architectures. 
• Through Cognizant Ignition™ and Neuro® AI platforms, they accelerate migrations from legacy systems like Teradata into modern engines.
• Their analytics stack expertise ensures up to 90% automation in script conversion, ETL schema refactoring, and data validation, delivering secure, scalable, and AI-ready enterprise data foundations.

5. Tata Consultancy Services

Tata Consultancy Services is a global leader in IT services, consulting, and business solutions as a part of the Tata Group. They describe their legacy analytics and data warehouse modernization around their "Machine First™ Delivery Model" and proprietary DAEzMo™ suite.

Key Services of TCS

• TCS specializes in the decommissioning of legacy systems such as Teradata, seamlessly migrating enterprise data to modern cloud platforms, including Snowflake, Databricks, Amazon RedShift, and Google BigQuery.
• By utilizing AI-driven accelerators like TCS Dezypher, they automate schema conversion and report migration, reducing manual effort by up to 75% and delivering secure, AI-ready data foundations. 
• Through its TCS DAEzMo and AI-powered accelerators, TCS automates code conversion, testing, and validation to reduce migration risk, improve performance, and build AI-ready analytics ecosystems.

6. Wipro

Wipro is a popular IT giant in India. They describe their approach to modernizing legacy analytics platforms and data warehouses through their Wipro FullStride Cloud Services framework and proprietary Wipro Data Intelligence Suite (WDIS).

Key Services of Wipro

• Wipro’s legacy modernization services include data estate assessment, migration planning, schema and SQL conversion, ETL/ELT modernization, governance, and managed services.
• Using an automated, low-code/no-code delivery approach, Wipro seamlessly migrates complex legacy estates like Teradata to leading modern cloud engines, including Snowflake, AWS Redshift, Databricks, and Google BigQuery.
• Wipro has shown a reduction in migration costs up to 40% and the total cost of ownership by 30% to build future-proof and AI-ready foundations.

7. Infosys

Infosys is one of the major IT giants in India. They provide comprehensive “Experience Design” and “Digital Interaction” services focusing on personalized and omni-channel customer experience. They aim to deliver a modern backbone that increases processing scale while cutting down data latency and total operational costs. 

Key Services of Infosys

• Infosys helps enterprises to migrate from legacy systems such as Teradata, Oracle, SQL Server, SAP BW, and Hadoop to cloud-native architectures optimized for real-time analytics and AI.
• Their services include data landscape assessment, migration strategy, schema and SQL conversion, ETL/ELT modernization, lakehouse design, governance, and managed operations.
• Its delivery approach focuses on “optimize, transform, and digitize” to reduce latency, improve scalability, and build AI-ready data ecosystems. 

8. Slalom Consulting

Slalom Consulting is a technology consulting company that leads with outcomes to bring more value. They focus on breaking down silos to build scalable solutions and cloud-native platforms such as enterprise data marketplaces by ensuring business strategy, data literacy, and cultural adoption evolve alongside the infrastructure.

Key Services of Slalom Consulting

• Slalom Consulting provides enterprise legacy modernization services through its "Zero Legacy" philosophy and an AI-powered accelerated framework. 
• They excel at deploying unified data lakehouses, building modern risk applications, and setting up centralized governance via Databricks Unity Catalog. 
• Their delivery approach relies on proprietary AI accelerators to map legacy logic, refactor ETL/ELT pipelines, and establish enterprise Data Marketplaces—cutting processing times by up to 40% while preparing teams to scale production-ready AI. 

9. Keyhole Software

Keyhole Software is a software development consulting firm specializing in enterprise solutions tailored to clients' needs. They commonly describe their approach to legacy modernization through a senior-led, AI-accelerated delivery model. 

Key Services of Keyhole Software

• Keyhole leverages its proprietary AI/works™ platform to map complex system dependencies and automate codebase analysis. 
• Its services include architecture assessments, cloud migration, custom data engineering, API integration, and performance optimization for reporting and analytics workloads. 
• Their architect-led delivery model emphasizes phased modernization, minimal business disruption, and scalable cloud-native design to improve analytics performance and reduce technical debt.

10. Thoughtworks

Thoughtworks is a premier global digital engineering consultancy that pioneers the Data Mesh architecture. They focus on shifting organizations away from monolithic legacy platforms toward decentralized, business-driven cloud data estates.

Key Services of Thoughtworks

• Thoughtworks helps enterprises migrate from Teradata, Oracle, SQL Server, and Hadoop to modern cloud architectures through data strategy, platform modernization, and data product development. 
• Thoughtworks delivers end-to-end data platform modernization by shifting enterprises away from monolithic, centralized warehouses toward decentralized, cloud-native architectures.
• They use AI-powered migration accelerators, data mesh principles, and an iterative MVP-based delivery to modernize warehouses, improve data quality, and build AI-ready analytics.

Analytics Modernization Technology Stack: What Leading Firms Use

Building a high-performance data platform requires more than just swapping out old hardware. Leading digital engineering firms rely on a highly orchestrated, unified technology stack to ensure scalability, real-time intelligence, and AI-readiness. A well-formed technology stack enables high-performance analytics and flexible support from structured and unstructured data.

• The Cloud Core - AWS/GCP/Azure: A new data strategy begins with a flexible infrastructure framework. Top-tier modernization firms use the major hyper-scalers - Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.
• The Data Engine - Snowflake / Databricks / BigQuery: Snowflake is widely used for enterprise multi-cloud data warehousing due to its ease of use, strong performance, and frictionless separation of storage and compute. 
  Databricks is good for teams pursuing a unified Data Lakehouse architecture, blending robust data lake flexibility with structured data warehouses.
  Google BigQuery is a serverless, massively scalable option that handles petabyte-scale analytics with built-in ML capabilities.‍
• Data Transformation - dbt (Data Build Tool): The dbt tool has become the industry standard for transforming data with cloud warehouses. It enables building modular SQL models, implementing automated testing, and helps in generating documentation.‍
• Real-Time Data Ingestion - Apache Kafka: Kafka is the leading platform for ingesting and processing real-time data streams. They help the organizations in moving beyond batch reporting toward real-time decision-making. ‍
• Workflow Orchestration - Apache Airflow: Airflow orchestrates complex workflows across data platforms and transformation tools. It allows data engineers to author, schedule, and programmatically monitor complex workflows as code.‍
• Business Intelligence - Tableau and Power BI: Leading firms bridge the gap between technical data layers and executive strategy through next-gen Business Intelligence (BI) tools like Tableau and Microsoft Power BI. Tableau is known for intuitive dashboards and strong visual analytics capabilities. Power BI is a good choice for organizations using the Microsoft ecosystem.

Entrans tech credentials

Entrans delivers analytics modernization using a proven, AI-ready technology stack and certified expertise across leading cloud and data platforms. We bring in multi-cloud expertise and an AI-first engineering approach.

• Cloud platform: Specialized deployment across AWS, Azure, and GCP, alongside elite engineering frameworks for Snowflake, Databricks, and BigQuery. 
• AI-driven Legacy Modernization: Utilizing advanced automation and proprietary Agentic AI frameworks to analyze, refactor, and migrate brittle legacy systems into scalable, AI-ready platforms. 
• Business Outcomes: Entrans has helped organizations achieve up to 50% reduction in infrastructure costs and 40% improvement in data accessibility.

How to Select the Right Analytics Modernization Consulting Partner for Your Enterprise

Choosing the right analytics modernization consulting partner is one of the critical investments an enterprise can make. A successful modern initiative drastically lowers infrastructure overhead and eliminates data silos.

• Industry expertise: The consulting partner should have strong technical credentials across modern cloud analytics platforms. Look for official certifications, partnerships, and delivery references. 
• Prior experience: Consider the analytics modernization consulting partner’s deep, certified experience across the entire modern data stack rather than pushing towards a single proprietary solution. They possess hands-on experience with Teradata, Oracle Exadata, MicroServer, IBM Netezza, SAP BW, and Hadoop-based requirements.
• Regulatory experience: Data democratization is a major goal of modernization, but opening up access cannot come at the expense of security. A consulting partner that treats data governance as an afterthought is a liability. 
• AI-Capabilities: Focus on the consulting partner’s ability to demonstrate how your newly designed architecture will securely feed data into GenAI models, Large Language Models (LLMs), and predictive analytics pipelines without compromising data privacy.

Why Entrans Is the Right Partner for Legacy Analytics and Data Warehouse Modernization

Entrans is a premier partner for legacy analytics and data warehouse modernization. We bridge the gap between aging infrastructure and future-ready intelligence in the following ways.

• We combine cloud platform expertise, automation-driven migration, and an AI-first approach to help enterprises transform legacy analytics environments into scalable, real-time, and AI-ready ecosystems.
• With our specialized expertise in environments such as Teradata, Oracle, Microsoft SQL Server, IBM Netezza, Hadoop, and SAP BW, we identify performance bottlenecks and design a phased roadmap to migrate to modern cloud platforms.
• We perform data platform assessment and roadmap, cloud data warehouse migration, ELT/ETL modernization, and Data lakehouse implementation.

Learn about how we build a scalable and secure analytics foundation for future growth. Book a consultation with us.