BI Modernization that Scales and Stays Inclusive

GenAI-Powered Strategy to Apache Superset Migration with iAURA™ BI Migrate

Thousands of Reports Turned Legacy System into Modernization Bottleneck

The client is a leading global payments technology corporation that enables secure electronic fund transfers via credit, debit and prepaid cards. Operating across more than 200 countries, it must uphold stringent privacy, data protection and accessibility standards so reporting stays compliant and usable for every audience.

Its enterprise reporting ecosystem, built on a 35-year-old business intelligence (BI) platform, had expanded into hundreds of thousands of reports. Over time, redundancy and obsolescence created duplication, confusion and inconsistent outputs. The aging platform limited agility, while maintaining licenses and customizations increased operating cost, thus accelerating the move toward open-source options.

Accessibility raised the stakes further. Under the American Disabilities Act (ADA), reports must comply with WCAG standards to support users with visual impairments or color blindness. Gaps in compliance raised regulatory and reputational risk, while manual migration was infeasible at this scale. Business users still needed faster access to modernized, visually enriched reporting, but rationalization and compliance were slowing decision-making.

Forrester’s recent Tech Pulse Survey found that only 21% of US IT decision-makers report no significant technical debt, while 49% face moderate levels and 30% report high or critical debt—one reason modernization bottlenecks persist when legacy complexity is left unaddressed.

When the reporting estate is this large, migrating everything is the costliest default.

So the question became precise: discover, rationalize, migrate and validate at scale—while making accessibility provable.

Program, Not One-Off Project

Persistent partnered with the client to modernize its BI landscape using iAURA BI Migrate, a GenAI-powered accelerator, purpose-built to automate report discovery, rationalization, migration and validation at scale, while embedding accessibility compliance into every stage of transformation.

The engagement began with a detailed discovery of the strategy ecosystem, followed by iterative automation cycles that combined GenAI-driven intelligence, metadata analytics and validation frameworks. The approach was designed not only to accelerate migration but also to improve governance, user experience and audit readiness.

Speed matters only when parity and audit-readiness keep up.

Before any code moved, the estate had to be seen clearly enough to shrink—because scale is easier to migrate once you stop migrating noise.

Four Motions That Make Scale Possible

1. Automated metadata discovery: iAURA’s GenAI agents scanned thousands of strategy reports to extract metadata, including datasets, metrics, visual components, hierarchies and dependencies. Reports were categorized by business domain, usage frequency and data lineage and surfaced through an inventory dashboard highlighting report complexity, metadata objects, dependencies and redundancy patterns—accelerating decisions on what to retain, migrate or retire.
2. AI-driven rationalization and consolidation: Migrating every report “as is” would have been prohibitively expensive and unnecessary. Persistent used a machine learning–based clustering algorithm to detect patterns across report structures and usage. Overlapping KPIs, charts and filters were used to identify report clusters. Usage statistics flagged stale reports; those not accessed for a defined 12–24 months were suggested for decommissioning. Heat maps and similarity matrices helped reduce the active report set by nearly 20–30% before migration even began. This rationalization phase minimized downstream effort and cost while reinforcing governance discipline.
   

   Modernization only scales when you reduce what you migrate.

3. GenAI-based bulk migration and automated validation: Once the streamlined inventory was finalized, iAURA executed automated code translation and report migration to Apache Superset, leveraging GenAI models fine-tuned for BI metadata transformation. The platform mapped visualizations, calculated fields and datasets from strategy to superset equivalents—maintaining logical integrity and look-and-feel. GenAI models dynamically generated superset configuration scripts, eliminating manual rework. Post-migration, an AI-based validation layer compared migrated reports with the originals, checking KPIs, chart consistency and query logic to ensure one-to-one parity. This two-tier automation pipeline reduced migration time from weeks to days.
4. Accessibility enablement by design: Persistent embedded inclusivity into the BI experience by addressing ADA and WCAG compliance from the ground up. Reports were redesigned with high-contrast color palettes to support users with visual impairments and color blindness. Screen-reader compatible layouts and ARIA labels were added to charts, filters and data tables, enabling narration of key insights. iAURA’s accessibility module simulated end-user interactions and validated compliance using automated accessibility testing tools. Deque’s Automated Accessibility Coverage Report found that, across 2,000+ audits, 57.38% of accessibility issues were identified using automated tests and the top five WCAG success criteria accounted for over 78% of total issues—reinforcing why automated validation matters at scale.

Accessibility is not a design touch-up; it is a validation discipline.

With discovery, rationalization, migration and compliance joined into one flow, impact could be measured cleanly by complexity.

Outcomes by Complexity, With Compliance Built In

Automation is only credible when validation keeps pace—especially when “simple” and “complex” reports coexist in the same estate.

Business impact delivered:

• Up to 60% faster migration for simple reports
• 35–40% acceleration for moderately complex reports
• 20% acceleration for complex reports
• Achieved ADA/WCAG compliance, mitigating regulatory exposure
• Established a repeatable, scalable framework for future BI modernization across the enterprise

When results hold across complexity, modernization stops being a risk-managed exception.

With outcomes proven and a framework in place, the next question becomes who can sustain the program—not just complete a project.

Why Persistent

Persistent brought the blend this transformation demanded: GenAI innovation, modernization engineering and compliance-first delivery.

• AI and data expertise: Deep experience in modern data platforms, BI modernization and regulated financial environments
• Innovation leadership: A patented GenAI-based BI migration and validation solution in iAURA BI Migrate
• Accessibility focus: Compliance treated as a core design principle rather than an afterthought
• Scalable, secure and future-ready: A modular framework that extends to thousands of reports and integrates across open-source and commercial BI ecosystems

Standardization is how scale stays trustworthy.

The first wave validated the approach; the longer-term value is a repeatable engine for every next wave.

Next Step, Without Guesswork

This engagement shows what changes when BI modernization is treated as an engineered system: discover what exists, reduce what does not matter, migrate in bulk with parity and validate accessibility as part of the pipeline. The result is faster time-to-value and an analytics environment that serves every user at enterprise scale, with regulatory alignment and insight-driven decision-making.

Explore a first-cut report estate assessment. Validate a superset migration path with WCAG built in.
Talk to Persistent.