Accelerating Enterprise Value: Hexaware’s AI-Driven Data Migration Services for CIOs
Introduction
For today’s CIOs, data is the lifeblood of innovation. AI and advanced analytics depend not only on having large volumes of data but well-governed, clean, accessible, and properly structured data. Many organizations, however, continue to operate with data trapped in silos, legacy systems, incompatible formats, and with low trust levels. This raises risk, slows time-to-value, and undermines AI and digital transformation initiatives.
That is where Hexaware Technologies’ Data Migration Services come in. By embedding AI, automation, and their platforms (notably Amaze® for Data & AI), Hexaware helps enterprises migrate, modernize, and make data ready for AI adoption—with minimal disruption and high business value.
In this blog, we’ll walk through:
- Why data migration remains a strategic priority for CIOs
- How AI enables faster, lower-risk, smarter migration
- Hexaware’s offerings, methodologies & platforms (e.g. Amaze®)
- Case studies & concrete business outcomes
- Key challenges & how to mitigate them
- Prompts / decision-points / governance that CIOs can use
- A suggested roadmap for CIOs to lead migration with AI reliably
Why Data Migration is Still a Strategic Priority
- AI readiness depends on data modernization: Without clean, well-integrated, accessible data, AI/ML efforts stall. Data must be consistent, adequately structured, reliable.
- Legacy systems hinder agility & innovation: Older platforms (on-prem, proprietary formats, batch ETL) can impose latency, cost, lack of scalability.
- Regulatory, compliance and cost pressures: As privacy, security, and compliance demands grow (GDPR, HIPAA, etc.), organizations need to move data to more secure, auditable environments. Migration can also enable cost savings (e.g. cloud vs data centre costs).
- Mergers, acquisitions, or reorganization: These often create multiple systems that must be unified. Hexaware has done multiple post-merger migrations. For example: in their case study for “data migration efficiency & cost optimization for a leading non-life insurer,” a client needed to “migrate three different policy administration systems to the new target system.” (Hexaware Technologies)
How AI & Automation Transform Data Migration
AI/automation can add value in many migration stages:
| Stage | Traditional challenges | Where AI/Automation Helps |
| Assessment / Discovery | Manual inventory of sources; poor visibility into data lineage and dependencies | AI tools can scan sources, automatically map schemas, detect dependencies, flag potential issues (e.g. anomalies, format mismatches) |
| Transformation & Cleansing | Manual rules, slow error detection, high effort in duplicate removal, format conversion, etc. | Machine learning / NLP & pattern matching to cleanse, dedupe, classify data; perhaps generative tools to translate business rules; use of AI to convert code (e.g. SAS to PySpark) automatically instead of hand rewriting (Hexaware Technologies) |
| Migration Execution / Orchestration | Time-consuming mapping, testing, validating; risk of downtime or data loss | Automation platforms/ pipelines (ETL/ELT), shift-workers, automated validation, continuous monitoring; tools like Amaze® that automate complex transformations and migration workflows (Hexaware Technologies) |
| Post-migration validation / Monitoring | Inconsistent data, missed edge cases, performance degradation | AI-driven testing, anomaly detection, performance monitoring; continual feedback loops |
Hexaware’s Amaze® for Data & AI is explicitly built to help with this. For example:
“Hexaware’s Amaze® for Data and AI accelerates AI adoption by enabling data estate modernization, automating complex transformations, and building future-ready …” (Hexaware Technologies)
And in the blog “The Role of AI in Automating SAS to PySpark Conversion: Accelerating Data Migration”, Hexaware shows how AI-driven automation can simplify code conversion and migration tasks, reducing time and cost. (Hexaware Technologies)
Hexaware’s Offerings & Methodologies
Here are the major components of Hexaware’s approach to Data Migration, especially with AI in the mix.
3.1 Amaze® Platform
- Amaze® for Data & AI: Modernizes data estates, automates transformations, supports future readiness. (Hexaware Technologies)
- Used in migrations of data pipelines/data store layers, etc. (e.g. Exadata → Azure SQL Data Warehouse migration accelerated by Amaze®) (Hexaware Technologies)
3.2 End-to-End Data Modernization & Migration Service
- Hexaware’s Data Modernization & Migration service offering emphasizes uncovering the right strategy & tools. (Hexaware Technologies)
- It includes strategies, data quality, governance, cloud migration, data lake / warehouse design, real-time ingest, etc.
3.3 Best Practice Processes & Strategy Components
From Hexaware’s blog “Your Guide to Data Modernization: Understanding the Most Critical Step in Data & AI Strategy” (Aug 2025) (Hexaware Technologies):
- Clear definition of business objectives
- Evaluation of current data state (sources, quality, infrastructure)
- Aligning strategy with business drivers (customer experience, revenue growth, operational efficiency)
- Choosing right technology & tools
- Strong governance, Data Quality, Compliance
3.4 Specialized Migrations & Use Cases
Some of the concrete migrations Hexaware has done:
- Healthcare Payer Data Migration: For a US-based Medicaid MCO, migration of data infrastructure to Azure Cloud to achieve cost savings and efficiency. (Hexaware Technologies)
- Oracle Database Migration to AWS Cloud: Mapping on-prem Oracle to AWS, using best practices to ensure minimal disruption. (Hexaware Technologies)
- VMware to AWS Migration for Healthcare Informatics: Ensuring data infrastructure supports AI models in healthcare. (Hexaware Technologies)
- Case Study: Exadata to Azure SQL DW Migration: Data pipelines and data store layers accelerated by Amaze®. (Hexaware Technologies)
Case Studies & Business Outcomes
Here are several cases with measurable business outcomes, showing the value for CIOs & enterprise leadership.
| Client/Case | Challenge | Solution Highlights / AI / Automation Elements | Outcome / Business Value |
| US-based Medicaid MCO | Legacy data infrastructure, cost inefficiencies, scalability issues | Migration to Azure Cloud; Hexaware leveraged cloud, optimized pipelines, improved efficiency. (Hexaware Technologies) | Cost savings, faster data access, improved analytics / reporting capability. (Exact figures not always public but described as “achieving cost savings and efficiency.”) (Hexaware Technologies) |
| Exadata → Azure SQL DW Migration | Large, complex data pipelines; need to move from Oracle-based Exadata to cloud data warehouse | Use of Amaze® to automate transformations and accelerate migration of pipelines/data store. (Hexaware Technologies) | Accelerated migration; reduced migration time; minimized risk. |
| Healthcare Informatics (VMware to AWS) | Data infrastructure not optimized to support AI models; legacy virtualization dependencies; compliance and security concerns | Migration from VMware to AWS; ensuring AI-supportive infrastructure; leveraging cloud scalability. (Hexaware Technologies) | Ability to run AI models more efficiently; better data availability; possibly lower costs (cloud ops) and higher compliance. |
| SAS to PySpark code conversion | Many enterprises have SAS scripts / legacy ETL, want to shift to modern big data stacks (e.g. PySpark) | Hexaware’s AI-driven automation to convert SAS to PySpark, reducing manual effort. (Hexaware Technologies) | Faster migrations, reduced cost, fewer errors; helps with scalability and adoption of big data tools. |
Moreover, a notable outcome in a publishing client case: “90% improvement in processing time through automated data processing” and “800,000 records efficiently cleansed.” (Hexaware Technologies)
Key Challenges & Mitigation Strategies
Even with powerful platforms and AI, data migration projects face risks. Here are common challenges and how Hexaware (and CIOs) can deal with them.
| Challenge | Business Risk | Mitigation / Best Practice |
| Data quality issues (incomplete, duplicate, inconsistent) | Leads to bad analytics, poor AI model performance, lack of trust | Use automated data profiling & cleansing, rule-based & ML-based data validation; define quality metrics early; involve business stakeholders in defining correctness. |
| Downtime / business disruption | Loss of revenue, customer dissatisfaction, regulatory exposure | Use incremental migration (trickle migration) vs “big bang” where possible; ensure fallback & rollback plans; test extensively; schedule during low usage windows. |
| Schema & compatibility issues | Loss of relationships, data integrity; business logic broken | Automated dependency mapping, transformation tools; robust testing; AI tools to recognize mismatches; maintain lineage. |
| Security / compliance | Risk of data breach, non-compliance, lost trust | Encryption, masking, auditing; governance and stewardship; secure cloud architecture. |
| Cost overruns / underestimating complexity | Budget blowouts; delayed timelines | Rigorous assessment & planning; prototyping; using platforms like Amaze® to automate and reduce manual effort; frequent checkpoints. |
Hexaware’s blog “Agility and Predictability for Data Migration Success” emphasizes that data migration is often “over-simplified and under-budgeted,” which results in surprise costs or delays. (Hexaware Technologies)
Prompts / Decision-Points / Governance for CIOs
To ensure successful execution and maximum business value, CIOs should own or heavily influence these:
- Define clear business objectives
E.g. Are you migrating to reduce cost, enable AI/analytics, improve customer experience, meet compliance? Quantify KPIs: cost savings, time-to-insight, error reduction, speed. - Current state assessment
Inventory data sources, volumes, formats, pipelines. Understand legacy tech stack, existing dependencies. What tools or code (SAS, etc.) will need conversion? - Evaluate readiness for AI
Do you have data quality, governance, unified schema, privacy & security? Are there data silos? What is data lineage? Is data accessible in real or near real time? - Choose the migration approach
- Big-bang vs incremental/trickle
- Lift & shift vs replatform vs refactor vs rewrite
- On-prem → cloud vs hybrid vs multi-cloud
- Select platforms & tools
Leverage platforms like Amaze®; consider cloud providers (AWS, Azure, GCP). Consider AI tools for transformation, testing, code conversion. - Governance & data management
Data governance, stewardship, ownership defined; policies for quality, privacy, retention; auditing and lineage. - Risk management & validation
Include extensive testing, pilot migrations, rollback strategies; monitoring tools; stakeholder involvement. - Cost & ROI monitoring
Track actual vs projected costs: migration cost, cloud hosting, operational savings, business agility gains. - Change management / Stakeholder communication
Impact on operations, user training, process changes. Communicate transparently with business units.
Suggested CIO Roadmap for AI-Enabled Data Migration
Below is a suggested multi-phase roadmap that CIOs can follow, incorporating Hexaware’s capabilities and AI-automation, to ensure business value and a reliable migration.
| Phase | Key Activities | Tools / Hexaware Offerings | Expected Outcomes |
| Phase 0: Initiation & Discovery | Stakeholder alignment; define objectives; current state audit (sources, quality, volumes, costs); risk assessment | Hexaware Data Modernization & Migration service; Amaze® assessment tools; AI-driven profiling tools | Clear business case, prioritized migration workstreams, estimation of effort & cost |
| Phase 1: Strategy & Planning | Decide architecture (cloud / hybrid), approach (big bang / incremental); plan compliance, data governance; select platforms and tools; pilot design | Amaze® for Data & AI strategy; Hexaware’s BI/AI strategy services; governance framework setup (Hexaware Technologies) | Detailed migration roadmap, pilot projects, defined KPIs and resourcing |
| Phase 2: Pilot & Proof of Concept | Run pilot migration on small datasets; test transformation; test AI/ML model performance; validate monitoring & rollback | AI-automated transformations (e.g. SAS to PySpark), Amaze-enabled pipelines, cloud testing environments | Learning, refining estimates, risk identification |
| Phase 3: Full Migration Execution | Migrate data, run continuous validation, ensure business continuity, monitor performance; manage dependencies; communication with business | Use Amaze® for automation; cloud‐provider tools; Hexaware’s experience in migrations like Exadata → Azure, Oracle → AWS, etc. (Hexaware Technologies) | Minimal downtime, risk mitigated, cost within budget, data accessible for AI & analytics |
| Phase 4: Post-Migration & Optimization | Decommission legacy systems; monitor data quality; iterate; optimize cost (storage, compute); operationalize AI & analytics; continuous improvement | Ongoing data governance; tools for monitoring/anomaly detection; managed services; cloud optimization | Improved agility, reduced operating cost, trusted data, scalable AI capabilities |
| Phase 5: Scaling / Innovation | Use the migrated data environment to build new AI-enabled business applications; enhance customer experience; drive new revenue streams. | Use Hexaware’s AI & Analytics offerings; Generative AI, predictive analytics, real-time insights; leveraging unified & trustworthy data | Competitive differentiation; faster innovation; measurable business metrics (e.g. increased revenue, faster decisions) |
Measuring Business Value: What CIOs Should Expect
To justify investment and secure stakeholder support, CIOs should track and communicate tangible business value. Some metrics and value levers:
- Cost savings
Lower infrastructure costs (cloud vs data center), reduced maintenance of legacy systems, fewer manual interventions, lower error correction overhead. - Time to Insight / Decision Making
Reduced latency from data ingestion to usable analytics/AI models; real-time or near-real-time dashboards; faster reporting cycles. - Operational Efficiency
Fewer manual processes, automated code transformation, pipelines that run with minimal human supervision; better error detection early. - Improved Data Quality and Compliance
Lower error rates; adherence to regulatory standards; auditability; lineage. - Business Agility / Innovation
Ability to launch new products/services using AI; adapt to market shifts; integrate M&A quickly; scale operations. - Risk Reduction
Less downtime, less data loss, reduced security vulnerabilities, improved disaster recovery.
Case in point: Publishing client achieved 90% improvement in processing time and was able to cleanse 800,000 records with minimal manual intervention. (Hexaware Technologies)
“Prompts” / Leading Questions CIOs Should Ask Vendors / Internal Teams
Here are sample prompts (questions) CIOs can pose, whether to Hexaware or to internal teams, to ensure AI-driven data migration delivers desired outcomes:
- What is your data discovery & lineage capability? Can you automatically map source systems, dependencies, schema differences?
- How do you handle code conversion and transformation? Do you use automation (AI/ML) vs manual rewrite (e.g. SAS → PySpark)?
- What validation, testing, rollback, and fallback mechanisms are in place to ensure zero or minimal data loss or business disruption?
- How is security, privacy, and compliance being handled (e.g. data masking, encryption, audits)?
- What metrics & KPIs will we track? (e.g. migration time, cost variance, data quality, system performance, user satisfaction)
- What is the migration approach: incremental/trickle vs big bang? On-prem to cloud/hybrid? Replatform/refactor etc?
- How do we ensure the migrated data environment is AI-ready: e.g. data quality, governance, unified schema, ability to support real-time/streaming or batch pipelines?
- What toolset or platforms are used? Does the vendor have proprietary platforms (like Amaze®) that help automate tasks and reduce risk/time?
- How will you manage change management with users / business units dependent on the data being migrated?
- What is the post-migration plan for optimization (cost, operations), decommissioning of old systems, ongoing maintenance & monitoring?
Taking Action: What CIOs Should Do Next
To move from planning to action, here are steps a CIO should take in their organization:
- Build a cross-functional migration task force: Include data engineers, security/compliance, business domain leads, AI/analytics teams.
- Pilot project: Choose a non-critical but representative dataset to test approach & validate AI tools.
- Invest in platforms & automation: Assess Amaze® or similar tools; allocate budget.
- Governance and stewardship: Define roles for data ownership, data quality champions.
- Change management & communication plan: Align stakeholders, users, train teams.
- Monitor & iterate: After migration, collect feedback, track KPIs, optimize.
Why Hexaware is a Strong Partner for CIOs
Putting together the evidence:
- They have delivered multiple complex migrations—cloud migration, code conversion, platform shifts (e.g. Oracle → AWS, Exadata → Azure).
- Their Amaze® platform is explicitly designed for data estate modernization and automating complex transformations. (Hexaware Technologies)
- They publish thought leadership (blogs, whitepapers) showing awareness of business drivers, governance, AI readiness.
- They provide a full lifecycle service: from strategy through planning to execution and post-migration optimization.
Conclusion
For CIOs in 2025 and beyond, data migration is no longer just a back-office project—it’s a strategic enabler of AI, business agility, compliance, and competitive differentiation. With the right strategy, the right platforms (e.g. Hexaware’s Amaze®), and rigorous governance, organizations can realize savings, reduce risk, accelerate time to insight, and build robust data foundations for innovation.
If you’re considering a migration, here’s a call to action:
- Audit your data estate now: Where are the major pain points? What legacy systems are holding you back?
- Define what “AI-readiness” concretely means for your organization.
- Choose partners/vendors who bring both technical expertise and business value orientation, with platforms and automation.
- Begin with pilots, track metrics, scale confidently.
References
Below are some useful Hexaware resources cited in this blog:
- Amaze® for Data & AI overview – Hexaware (Hexaware Technologies)
- Your Guide to Data Modernization: Understanding the Most Critical Step in Data & AI Strategy (Blog, Aug 13, 2025) (Hexaware Technologies)
- Case Study: Healthcare Payer Data Migration Solution for US-based Medicaid MCO (Hexaware Technologies)
- Case Study: Exadata to Azure SQL DW Migration (Hexaware Technologies)
- Blog: The Role of AI in Automating SAS to PySpark Conversion (Hexaware Technologies)
- Glossary: What Is Data Migration? (Hexaware) (Hexaware Technologies)
- Blog: Agility and Predictability for Data Migration Success (Hexaware Technologies)
- Case Study: Oracle Database Migration to AWS Cloud (Hexaware Technologies)
VMware to AWS Migration for Healthcare Informatics (Hexaware Technologies)
