Legacy System Modernization: Data Applications and Analytics Transformation

Companies can no longer afford to stick with outdated legacy systems. Legacy system modernization has gone from an option to a requirement. To remain competitive, efficient and prepared for the future, companies must replace outdated systems, which pose security and operational risks to vital business processes.

In this post, we will look at the key challenges businesses face in modernizing legacy systems and share the best practices to turn legacy software into modern solutions.

Why Legacy System Modernization Matters

The demand for legacy data system modernization, part of legacy application modernization, is growing fast as businesses struggle with outdated technology. Legacy systems hinder innovation and create inefficiencies that disrupt critical business processes vital for business growth. Existing legacy systems often lack the scalability and flexibility needed to meet the demands of today's dynamic challenges.

Historically, businesses have used the lift-and-shift approach — a simple migration of old systems to modern infrastructure. However, this approach often fails to tackle the core challenges of inefficiency, scalability and integration with new technologies such as artificial intelligence (AI). Holistic modernization has become the cornerstone of digital transformation, empowering organizations through data democratization, cost efficiency and AI-driven innovations. It ensures businesses transform legacy systems into reliable, modernized systems designed for the future.

Common Risks of Legacy Systems and Solutions to Address Them

Legacy systems pose significant challenges like hindering innovation, driving up costs and limiting business growth. Businesses often grapple with fragmented data, limited accessibility, high maintenance costs and challenges in adopting modern technologies like Generative AI (GenAI). These obstacles hinder efficiency, innovation and growth. These issues not only disrupt operations but also hold back organizations from staying competitive in a fast-evolving market.

Addressing these challenges requires tailored modernization strategies. Below, we outline the primary risks of legacy data systems and offer actionable solutions to resolve them.

1. Data Fragmentation

Data fragmentation is a major obstacle to modernization efforts. Legacy systems often distribute data across multiple platforms, locations and systems, making it difficult for organizations to gain a unified and accurate view of their operations. This scattered data prevents seamless integration, reduces transparency and complicates decision-making processes. Legacy applications or systems rely on outdated legacy code and architectures, further increasing the complexity of consolidating and modernizing data.

The main causes of data fragmentation include:

• Scattered Data: Data resides across various storage systems, services and applications, making it hard to consolidate into one view.

• Disparate Systems: Many systems are standalone and use different technology, creating barriers to integration.

• Multiple Data Owners: When data is owned by different stakeholders or departments, it's hard to coordinate and align.

• Lack of Unified Structure: Without a standard framework, aggregating and modernizing data becomes more complex and inefficient.

Solution:

• Conduct an Accurate Migration Assessment: Perform an automated assessment of legacy systems to evaluate and consolidate data inventory, complexity and dependencies, creating a roadmap for seamless integration.

• Map Data to Business Needs: Create a migration strategy that prioritizes consolidation into a unified, centralized data structure to better support organizational objectives.

• Establish Clear Data Governance: Set clear roles for data ownership and implement strong governance practices to improve transparency, responsibility, data accuracy and tracking.

2. Lack of Data Democratization

The democratization of data drives modern businesses, where efficiency and innovation thrive. Giving teams access to data becomes a key driver of growth and faster decision-making.

However, legacy systems hold us back by creating barriers to access, coordination and integration:

• Centralized Ownership: Data ownership is centralized in one team, causing inefficiencies and a long process of changes.​

• Slow Data Analytics: Analytics updates are too slow, delaying insights and decisions.

• Limited Accessibility: Centralized control creates obstacles, limiting data owners from acting independently.

• Variety of Tools and Approaches: The use of diverse tools for data integration and curation often leads to challenges in system integration and higher maintenance costs.

Solution:

• Build Decentralized Data Marketplaces and Self-Service Tools: Give business users easy access to data with self-service tools, so they don't have to depend on IT teams and work faster.

• Develop a Common Language for Data Products: Use the same terms and frameworks for all data to make communication easier and help everyone access it more smoothly.

• Implement Real-Time Data Tools for Faster Access: Use tools like Apache Kafka or AWS Kinesis to deliver data instantly, helping teams make faster decisions and gain quick insights.

• Build a Data-Driven Culture: Encourage openness and teamwork, using intuitive user interfaces that make data easier to access and view as a key resource to help teams innovate and make better decisions.

3. High Costs and Inefficient Scaling

Legacy systems drain resources and limit scalability, making maintenance costly and preventing businesses from investing in core operations or driving innovation.

Several factors amplify the high costs and inefficiencies of legacy systems:

• High Cost of Maintenance: Maintaining multiple systems for data management is costly, as clients must pay for each resource separately.​

• Expensive scaling: Clients often incur expenses for underutilized resources due to inefficient scaling practices.​

• Lack of Optimization: Legacy applications, reports and pipelines require more resources, leading to extra expenses.

Solution:

• Cut Costs with TCO (Total Cost of Ownership) Optimization: Combine and phase out outdated systems while removing unnecessary processes, duplicate data and unused components to lower expenses.

• Improve Efficiency with Automation: Use automation tools to handle repetitive tasks, reduce mistakes and save time and resources.

• Automate Legacy System Conversion: Use advanced tools to automate the migration of ETL code, reports and database logic from legacy systems, reducing errors caused by manually cleaning scattered data.

• Adopt Flexible Modern Solutions: Move to cloud-based systems that let businesses scale up or down as needed, so they only pay for what they use, avoiding unnecessary upfront costs.

4. Lack of GenAI Integration

The absence of GenAI integration poses significant challenges for businesses, impacting their ability to stay competitive, drive growth and adapt to modern technological demands.

The challenges due to a lack of GenAI adoption include:

• Missed Competitive Advantage: Businesses must embrace GenAI to stay competitive, as neglecting its innovative potential could leave them trailing behind competitors who are effectively leveraging it**.**

• Restricted Growth and Market Opportunities: Without GenAI, organizations face limitations in scaling operations and tapping into new market opportunities, leading to a potential loss of market position.

• Integration Barriers from Legacy Systems: Outdated infrastructure and legacy systems often limit the seamless integration of GenAI into business processes, preventing organizations from fully realizing their potential benefits.

Solution:

• Modernize Infrastructure for AI: Create flexible, cloud-based systems that support AI and GenAI, making it easy to handle unstructured data and deliver smarter predictions.

  Integrate AI Seamlessly: Update existing applications to work smoothly with the GenAI API without disrupting current workflows or operations.

  Improve Data Quality and Governance: Prepare data to reduce errors, ensuring clean and accurate information for AI training and better decisions.

  Strengthen Security and Compliance: Enhance cybersecurity and follow regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) to protect AI systems from risks and ensure data privacy.

migVisor: A Framework for Accelerated Modernization

One proven way to accelerate legacy system modernization is by using the migVisor Data Modernization Framework. Designed to fast-track projects, migVisor helps businesses achieve modernization milestones in record time, transitioning to production in just three months.

Tailored Migration Strategies for Legacy System Modernization

EPAM provides a comprehensive migration approach by analyzing the complexity of systems, applications and workloads to define the most effective strategy. By evaluating components like data ingestion (Upstream), processing (Data Warehousing) and reporting (Downstream), EPAM breaks migration into manageable phases that address both technical and business needs. This ensures organizations can seamlessly transition from legacy systems to modern, scalable and cloud infrastructures.

To suit varying levels of complexity, EPAM offers three core migration strategies:

• Lift-and-Shift: For low-complexity scenarios, this strategy transfers systems "as-is," preserving functionality with minimal changes, while being the fastest and most cost-effective approach, though it retains legacy inefficiencies.

• Re-Platform: A medium-complexity approach, this strategy updates schemas, SQL logic and workflows to improve performance and scalability. It also aims to balance modernization and effort without requiring a full system redesign.

• Re-Architect: The most advanced strategy involves completely redesigning systems and application logic to fully use cloud-native capabilities, making it ideal for businesses seeking maximum scalability, innovation and long-term return on investment (ROI).

Step-by-Step Legacy System Modernization Process

EPAM uses a structured, phased approach, combining automation tools and technical expertise to ensure a seamless transition. Below is the step-by-step process EPAM follows to successfully modernize legacy systems.

1. Learn: Conduct a Comprehensive Inventory Assessment

EPAM uses automated tools to profile databases, reports and pipelines, identifying system complexity and dependencies to create a clear migration scope. This step significantly reduces assessment time and cost, delivering results within weeks.

2. Plan: Design a Modernization Roadmap

EPAM develops a detailed roadmap outlining delivery waves, resource allocation and key milestones based on business priorities and technical requirements. This strategy ensures efficient execution tailored to the client's goals.

3. Validate: Test and Prepare for Execution

EPAM ensures the migration approach works by piloting key components, resolving dependencies and adjusting the roadmap for scalability and accuracy. This final step prepares the solution for seamless implementation.

4. Execute: Scale Implementation Across Systems

EPAM streamlines migration execution through automated tools and advanced processes, ensuring efficient and accurate conversion across schemas, historical data, ETL pipelines, applications and reports. Key steps include setting up migration environments and converting legacy systems with tools like migVisor Code Converter.

Additionally, the process involves integrating data sources, validating historical records and reconciling discrepancies using migVisor Reconciler.

5. Cut-Over and Support: Ensuring a Seamless Transition

In the final stage, EPAM ensures a smooth transition to production through strategic cut-over planning, rigorous post-migration testing and user training. Using tools like migVisor Reconciler, automated processes validate data quality, schema migration and report accuracy, while readiness checklists ensure the system meets predefined criteria. The go-live phase deploys the solution into the production environment, followed by post-cutover testing to ensure acceptance by users and IT teams.

6. Continuous Improvement: Optimizing Systems and Processes Post-Migration

EPAM's support team provides ongoing assistance after going live, addressing performance optimization, user onboarding and issue resolution. Regular checkpoints and transparent reporting ensure effective knowledge transfer to client IT teams and long-term system adoption, guaranteeing business continuity and operational success.

Let's move on to the use case and see how EPAM used the migVisor Suite to help a client modernize its legacy data analytics infrastructure.

Enterprise-Level Modernization for Legacy Data Analytics: Case Study

A global food and beverage leader partnered with EPAM to modernize its decade-old data analytics ecosystem, migrating from on-premise platforms to a scalable, cloud infrastructure. The goal was to improve efficiency, reduce costs and enable self-service analytics.

Challenges Faced

The client's legacy systems presented the following critical issues:

• Outdated architecture, limiting scalability and efficiency based on an on-premises technical stack. Over 60,000 data transformation jobs significantly increase the complexity of support and modernization costs.

• With 37,000 reports and workflows coupled with a lack of data ownership, the challenge of harmonizing and unifying the BI layer across 16 geographical locations becomes even more complex.

• Dependency on DB2 infrastructure, limiting cloud integration and adding a performance tuning challenge as the existing on-premises solution is limited in scalability.

• Fragmented systems, data redundancy and inconsistency are slowing modernization and preventing the building of a comprehensive data catalog with consistency and a single source of truth.

• High operational costs are caused by redundant processes and duplicate reports.

• Sophisticated and non-systematic data governance reduces flexibility and efficiency in report modifications.

Proposed Solution

EPAM implemented a structured approach to address these challenges:

1. Conducted Technology Assessment: Evaluated ~1,600 reports, ~670 ETL workflows and ~1,200 database objects to assess migration complexity in 6 weeks using automated tools.

2. Established Prioritization: Defined client-specific migration priorities and identified dependencies.

3. Designed Migration Strategy: Offered two options — lift-and-shift for full migration or modernization focused on self-service analytics.

Execution Strategy

• Analytics Migration to Power BI: Consolidated ~1,600 legacy reports into 20 data models and 40 dashboards, executed in 3 agile waves.

• DB2 to Snowflake Migration: Converted schema and SQL code in stored procedures and views, removed obsolete components, eliminated duplicate products and improved data governance.

• Hub & Spoke Architecture: Built a scalable system that unified governance while enabling customized data solutions for regional teams.

Results Achieved

• Achieved a 78% reduction in migration effort through automation and optimization.

• Migrated key data, enhancing scalability and efficiency with Snowflake infrastructure.

• Enabled decentralized self-service analytics with modern Power BI dashboards.

• Improved data governance with standardized frameworks ensuring ownership and security compliance.

• Automated workflows to remove redundancies and accelerate operations.