Data is the backbone of modernization but legacy data is often
unstructured, incompatible and polluted. We help you migrate with surgical
precision, using rule-based automation and strong governance.
Data Landscape Study
We analyze volumes, quality, dependencies and governance maturity. We identify potential roadblocks and co-create a strategy with your teams.
Scope & Governance Definition
Define data scope, transformation rules, acceptance criteria and rollback plans. No room for surprises.
Schema Mapping & Transformation
We align old schemas with new data models, bridging structural gaps with business logic-driven transformations.
Data Profiling & Cleansing
We use profiling rules to detect anomalies and redundancies. Cleansing is automated where possible, with business validation in the loop.
Pilot → Full Migration
Start with a 2% pilot migration to validate the approach. Once verified, we scale up with rollback, reconciliation and UAT checkpoints at every stage.
Whether it’s monolithic mainframes or fragmented financial databases, we help you transition with zero loss, total traceability and future-ready architecture.
Mainframe to Relational Database Migration
Re-engineered legacy COBOL-based systems into a scalable, relational model, with full business logic preserved.
Hierarchical to Normalized Data Structures
Restructured deeply nested legacy data into optimized, query-ready formats for real-time access and analytics.
Multiple Desktop Databases → Unified Cloud Platform
Consolidated disconnected on-prem databases into a secure, scalable cloud-native architecture, enabling cross-system visibility.
Siloed Financial Systems to a Centralized Data Lake
Integrated fragmented financial databases into a governed data lake, enabling faster reporting, richer insights and compliance-ready audit trails.
The most advanced application cannot deliver value on corrupt data. Our migration strategy is tested, validated and built for scale without compromising on compliance, consistency or uptime.
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