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Smart Data for AI: Building Agile, AI-ready Ecosystems
Highlights
- Data platform modernisation has become a strategic imperative for enterprises looking for agility, scalability, and the ability to derive real-time insights.
- Modernisation initiatives, however, are hampered by accumulated technical debt, undocumented business logic, tightly coupled reporting layers, and large volumes of historical data.
- An artificial intelligence (AI)-first data modernisation solution addresses these challenges by embedding intelligence and automation in the entire modernisation lifecycle.
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The modernisation imperative
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.
What it involves
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
|
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.
|
Safely and efficiently migrate data from the legacy platform to the new modernised environment, ensuring data integrity and consistency.
|
Validation of migrated data, code, workloads and reports at scale.
|
Why traditional migrations fall short
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:
- Deciphering the deeply embedded, poorly documented legacy system
- Translating, re-engineering the complex business logic embedded in legacy SQL/ETL jobs
- Ensuring accurate migration, pointing of BI reports to the new platform
- Ensuring accurate, complete, and secure migration of vast data sets without business disruption
- Bridging the skills gap between legacy system expertise and modern cloud data technologies
- Validating at scale of migrated data, code, and reports
Without intelligence and automation, migrations become lengthy, expensive, and disruptive, reinforcing scepticism around modernisation efforts rather than enabling transformation.
Accelerating migration programmes through innovation
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 |
End‑to‑end solution benefits
By applying intelligence and automation in discovery, migration, and validation, we can deliver measurable enterprise benefits:
- Faster modernisation timelines through automation and parallel execution
- Reduced migration risk by preserving business logic and reporting behaviour
- Scalable validation to ensure data quality, integrity, and consistency
- Lower dependency on scarce legacy skills
- A future‑ready data platform that supports advanced analytics and AI
Case study: Migration of Oracle Warehouse to Snowflake for a leading media firm
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
- Enabled early and timely exit from Oracle-based EDW, which saved significant licensing cost
- No functional change and, hence, zero impact to business users during cutover
- Project implementation completed in about 12 months against 18 months laid out in the original implementation timeline
- Automation and migration accelerators facilitated rapid implementation, with automated conversion covering about 25,000 database objects (reducing effort by 75%)
- Historical data migration of nearly 200 TB (reducing effort by 40%)
- Automated data validation (reducing effort by 50%).