Data platform modernisation has become a strategic necessity as enterprises seek greater agility, scalability, and the ability to derive real-time insights from data.
Yet, for many organisations, modernisation initiatives remain slow, complex, and risk-laden. Decades of accumulated technical debt, undocumented business logic, tightly coupled reporting layers, and large volumes of historical data make traditional migration approaches costly and disruptive, impacting modernisation initiatives.
An artificial intelligence (AI)-first data modernisation solution addresses these challenges by embedding intelligence and automation in the entire modernisation lifecycle. By combining automated discovery, generative artificial intelligence (GenAI)-assisted code transformation, scalable data migration, and AI-driven validation, organisations can modernise legacy data platforms faster. Our experience suggests that programmes executed using this approach demonstrate up to 70% faster modernisation outcomes, enabling organisations to realise cloud value sooner and with lower risk.
Automation and GenAI enable faster execution while preserving business logic and data trust, delivering up to 70% faster modernisation.
A typical data platform modernisation programme involves analysing the existing legacy environment, defining a migration strategy, converting schemas, code, and workloads to modern platforms, migrating historical data, and validating the migrated assets. Here is an approach that can accelerate the modernisation plan:
Assess |
Convert |
Migrate |
Validate |
Comprehensive analysis of existing landscape to achieve
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Rewrite, re-architect and optimise existing schema, DML (data manipulation language) scripts, ETL (extract, transform, load) workloads and BI reports to be compatible with new platform.
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Safely and efficiently migrate data from the legacy platform to the new modernised environment, ensuring data integrity and consistency.
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Validation of migrated data, code, workloads and reports at scale.
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Onboarding the legacy data Warehouse on to cloud requires a massive up-front investment of time and resources. The key considerations for such initiatives are data model changes, downstream changes, timeline for the change, and enterprise priority. Organisations choose from lift and drop, re-design, hybrid approaches based on their specific context and enterprise data strategy.
The key challenges in such migrations include:
Without intelligence and automation, migrations become lengthy, expensive, and disruptive, reinforcing scepticism around modernisation efforts rather than enabling transformation.
Automation and AI fundamentally transform how modernisation programmes are executed. Instead of fragmented, manual activities, an AI-first solution embeds intelligence across five critical stages of the migration lifecycle, turning modernisation into a scalable, repeatable enterprise capability.
AI‑first data platform modernization: Acceleration framework
Acceleration area |
What the solution addresses |
How innovation is applied |
Outcome delivered |
Legacy platform analysis and assessment |
Understanding schemas, objects, dependencies, and data lineage |
AI‑driven discovery, lineage mapping, GenAI‑based code explainability |
Faster planning and reduced reliance on legacy expertise |
Schema, SQL, and access model migration |
Migration of database objects, DMLs, roles, and privileges |
GenAI‑assisted code conversion and automated security mapping |
Higher conversion accuracy with reduced manual effort |
Workload and BI modernisation |
Migration or re‑pointing of ETL workloads and BI reports |
Automated workload translation and report alignment |
Minimal disruption to business users |
Historical data migration |
Large‑scale movement of legacy data to modern platforms |
Elastic, AIoptimised data migration pipelines |
Faster execution and optimised cost at scale |
Testing and validation |
Ensuring accuracy of data, code, and reports |
Automated test generation and AI‑driven reconciliation |
Increased trust and lower cutover risk |
By applying intelligence and automation in discovery, migration, and validation, we can deliver measurable enterprise benefits:
Acceleration: Data migration, Schema and SQL code conversion, data validation
Problem statement
The current Oracle Exadata warehouse was nearing licence expiry and had multiple limitations like scalability problems, data in silos, and lack of support for advanced analytics and data science solutions.
Solution Implemented
The migration utilised the organisation’s Snowflake data lake infrastructure to move the enterprise data warehouse (EDW) from on-premises Oracle to Snowflake. Automation tools accelerated the processes of data migration, database object conversion, and validation. With robust connectors and schema conversion features, along with native load capabilities, approximately 0.5 trillion records were migrated without any data loss. The accelerators enabled the customer to transfer and validate data and schema on Snowflake in repeatable, structured phases, ensuring the migration was both predictable and thoroughly validated by business. Reporting tools such as Informatica, BO Universe, and Tableau workbooks were reconfigured to integrate seamlessly with Snowflake.
Value delivered