A few years ago, data and analytics modernization was largely about replacing aging systems and moving data to the cloud. That definition no longer goes far enough. Many enterprises have completed major migration programs, yet still struggle to obtain a clean, trusted answer from the data estate they created.
The label has stayed, although the job has grown. Once data reaches a new platform, teams still need to find it, understand the rules applied to it, and trace it back to source. Modernization has delivered when analytics and AI can draw on that data without another round of manual checks.
That is the tension captured in the ISG Provider Lens® Advanced Analytics and AI Services – Large and Midsize 2025 U.S. Quadrant Report.
Modernization as the Operating Layer, Not a One-time Migration
The data foundation has to keep working after the migration team leaves. New sources are added, definitions change, and fresh use cases put pressure on decisions made months earlier. Lakehouse patterns and platform-led architectures feature across the market, while metadata-driven ingestion is central to many modernization programs. Providers differ in how well they manage reliability, governance, lineage, and cost around those foundations.
DataOps, semantic layers, observability, and lineage are increasingly part of the initial design, with some providers also building cost controls into the modernization process. Each addresses a familiar problem. A broken feed should be found before it reaches a report, a changed figure should be traceable, and two business teams should not spend weeks debating which definition applies. The data still needs specialist care, but using it should not depend on finding a specialist every time.
Reliability is only part of the work. The data also has to fit the industry and the decisions it supports. A modernization program can end with a stronger platform and little change for the business. Providers reduce that risk when industry data models, semantic structures, and consumption layers are included from the start.
The 12 Data and Analytics Modernization Services Providers
The report identifies 12 data modernization providers, listed alphabetically, as leaders for their ability to deliver rapid, cost-effective data modernization services through agile frameworks, accelerators, strong governance, and industry-focused solutions.
| Provider |
Modernization Focus |
| Apexon |
Schema conversion, pipeline validation, lineage-first documentation, BI refresh orchestration, access controls, and service windows |
| Brillio |
Integrated ingestion, quality, observability, governance, and lineage across multicloud environments |
| EXL |
Multiagent data engineering, metadata-driven lineage, Code Harbor, Data Harbor, and governed catalog integration |
| HARMAN |
Standardized DataOps accelerators, multienvironment orchestration, migration, quality, and lineage |
| Hexaware Technologies |
Amaze® for Data and AI, metadata-driven ingestion, lineage automation, multicloud orchestration, observability, and cost governance |
| HTC Global Services |
Metadata-driven pipelines, restartability, audit dashboards, multiregion visibility, and governance alignment |
| Innova Solutions |
Automation accelerators, multicloud migration frameworks, pipeline quality, and operational control |
| Mphasis |
Snowflake and AWS modernization, phased road maps, ETL retrofit, incremental synchronization, and the Cognitive Mesh ecosystem |
| Persistent Systems |
Modular pipelines, agentic lineage, embedded observability, and structured and unstructured data |
| Unisys |
Tenant-hosted architectures, semantic layer integration, hybrid governance, observability, and applied AI |
| UST |
Analytics orchestration, telemetry governance, memory-aware agent integration, and infrastructure-rich verticals |
| Virtusa |
Helio Data Studio, dataset validation, orchestration, controlled preprocessing, label quality evaluation, and prompt conditioning |
The table points to different areas of emphasis. Some providers concentrate on migration engineering and orchestration. Others place more weight on lineage, semantic layers, observability, or preparing data for AI. A contained warehouse migration will place different demands on a provider than a multiregion estate spanning legacy platforms, several clouds, and regulated data. The choice depends on the estate, the industry, and the problems that remain after migration.
Why Midsize Modernization Programs Win, and Where They Strain
Cost is only part of the appeal of a midsize provider. Many come in with migration frameworks, reusable ingestion components, and industry data models already prepared, so the client can begin with its own estate and priorities instead of spending the first few months designing the basic method. This can ease the load on an internal data team that is small or already occupied with running the current environment.
That speed is valuable. It helps clients make progress before a modernization program becomes too large to manage. In a well-scoped program, a midsize provider can often stay closer to the business team, understand the domain faster, and keep delivery moving.
It is increasingly difficult to maintain the same delivery model once the program crosses the enterprise. For example, in a program, metadata may be owned by finance in one case and sales in another. Governance may change from region to region. Observability must cover legacy and cloud platforms together. One domain may be handled well by a lean team. However, a broader coordination and clearer authority is required to achieve the same standard across multiple functions and several regions.
A sound data modernization strategy must extend governance, lineage, and observability across domains without losing the speed and cost efficiency that distinguish midsize providers.
The Data Modernization Challenges Facing Enterprises
Enterprise data modernization programs commonly run into four related problems.
| Challenge |
What It Looks Like |
| Application complexity |
Close to 2,000 applications across a large enterprise, often without consistent data architecture standards |
| Difficulty proving value |
Modernized infrastructure without a clear link to business outcomes or a defined path for consuming the data |
| Uneven adoption |
Differences in data literacy, stewardship, and the use of common definitions across business units |
| Gaps across mixed estates |
Inconsistent lineage and observability across legacy, cloud, hybrid, and multicloud environments |
The Hexaware Approach
Hexaware’s Leader profile centers on several of these priorities, particularly automation, lineage, governance, and operational assurance.
Hexaware treats data and analytics modernization as a governed, automation-led discipline anchored in its metadata-driven Amaze® for Data and AI—a unified data modernization platform that brings together engineering, observability, and governance functions so that ingestion, validation, and transformation pipelines are metadata-defined. Governance is built into the operating process, allowing regulated enterprises to trace data from source to consumption without depending on manual intervention at every stage.
Amaze® for Data and AI supports modernization across Azure, AWS, Google Cloud, Databricks, and Snowflake. Its automated assessment and migration capabilities help enterprises re-platform on-premises data landscapes within five to seven months, 40% to 60% faster than traditional approaches.
Governed data management is anchored in Microsoft Purview-based lineage and cataloging, so classification, traceability, and compliance are part of the build rather than added later. FinOps insight is included within modernization pipelines, giving teams visibility into cost alongside data quality.
For instance, Hexaware supported the strategy, architecture, and migration of a U.S. airline’s data warehouse to AWS, creating an environment that could expand with its analytics requirements. The engagement shows why modernization planning has to extend beyond the first migration. The estate also needs to accommodate new sources, reporting requirements, and later AI use cases.
How to Choose the Right Data and Analytics Modernization Service Provider
A quadrant position helps narrow the field. The closer questions concern what happens during delivery and after the first data domain has moved.
Providers should be able to move data across legacy and cloud platforms, recover failed pipelines, and apply governance that works for business teams. Experience in similar regulatory environments also counts, while client references should show outcomes such as faster reporting, reduced reconciliation, or quicker delivery of new analytics use cases.
These midsize Leaders stand out for faster delivery, reusable accelerators, and industry knowledge. Larger and more complex programs will ask more of their governance, observability, and ability to connect modernization with business value.
Explore Hexaware’s data and analytics modernization services.
