By Niraj Kumar, CTO, Onix
The global data migration market is experiencing explosive growth, with forecasts pointing to an $11 billion valuation by 2025 – a surge largely attributed to the emergence of Generative AI. As organisations increasingly transition from traditional systems to cloud infrastructure, this growth trajectory aligns with broader digital transformation trends. While Gen AI first captured the public imagination in 2022, it has since evolved from a buzzword to delivering tangible business value. This evolution is particularly evident in addressing complex data management challenges, including data migration.
Transforming data migration through AI innovation
Traditional data migration projects have historically struggled, with 80% either failing to achieve objectives or exceeding budget and time constraints. AI technology offers a solution by automating code conversion and validation processes. Additionally, these AI-driven migration tools help organisations maintain strong data privacy compliance, particularly with regulations such as GDPR. The shift toward AI-powered data migration isn’t merely a technological upgrade; it’s becoming a strategic imperative for businesses aiming to optimise their data operations and remain competitive in an increasingly data-driven landscape.
Here are some key stages of data migration and how it is eased with AI and automation:
Assessment and planning
AI-powered assessment and planning tools analyse existing data landscapes to identify dependencies, complexities, lineage, and potential risks. This proactive approach ensures accurate scope definition, cost estimates, and migration strategy, significantly reducing unplanned delays and cost overruns. AI-generated insights provide actionable recommendations, allowing organisations to prioritise workloads effectively. Tools like Eagle specialise in comprehensive assessments of legacy data warehouses, also providing step-by-step planning for efficient migration and modernisation to the cloud platforms.
Data conversion and transformation:
Automated code conversion and transformation solutions leverage AI to convert workloads, codes, ETLs, etc. in the legacy database schemas, optimise query performance, and ensure seamless compatibility with modern cloud data warehouses. AI-powered tools eliminate redundant manual efforts, accelerating the transition to cloud-native architectures with minimal disruption. Solutions like Raven use AI to automate the conversion of complex code workloads from on-premises systems to cloud-native applications, enhancing efficiency and accuracy.
Data validation and reconciliation
Validation and reconciliation of data is critical in ensuring that the data is successfully migrated and is working in the new cloud environment. AI automates data validation and reconciliation by employing advanced algorithms to compare source and target environments at the cell level. This ensures accuracy, consistency, and completeness, reducing data mismatches and compliance risks. AI-powered automated validation eliminates the need for the army of QAs and expedites migration timelines by swiftly identifying discrepancies and enabling proactive resolution. Pelican provides real-time validation and reconciliation to maintain data integrity throughout the migration process and confidently decommission the legacy system.
Data security and AI enablement
Data security is of utmost importance during migration and modernisation. AI-powered synthetic data generation tools, such as Kingfisher, create realistic datasets for testing and AI model training, ensuring compliance and protecting sensitive data. This allows businesses to maintain data privacy while leveraging AI-driven analytics post-migration. Additionally, AI helps structure and optimise datasets for advanced analytics and machine learning applications.
How does GenAI for data migration benefit businesses?
Gen AI can speed up the migration process, enabling enterprises to unlock the advantages of cloud migration quickly. Here’s how:
Automation of manual workflows
The complexities of legacy system migration often involve time-consuming manual processes, including data analysis, infrastructure updates, and compatibility assessments. Gen AI streamlines these operations through automation, enabling teams to redirect their focus toward strategic initiatives and core business operations.
Advanced schema mapping intelligence
Leveraging sophisticated algorithms trained on database architectures, Gen AI excels at understanding complex data relationships and structures. This capability enables intelligent schema mapping and facilitates advanced data pre-joining operations, where multiple database tables seamlessly merge into unified structures within data warehouses. This functionality proves essential for modern database transformation initiatives.
Efficient code modernisation
Code refactoring, a critical component of data modernisation, requires precise translation to ensure platform compatibility. Gen AI’s deep understanding of source code architecture enables accurate conversion to target languages. Beyond basic translation, this technology optimises code performance specifically for cloud environments, enhancing overall system efficiency.
Financial impact and business value
McKinsey’s analysis highlights Gen AI’s significant impact on migration economics. The technology drives enhanced ROI through multiple channels like identifying business opportunities, reducing application remediation expenses, accelerating migration timelines, and enhancing cloud-based development productivity. This comprehensive approach not only reduces migration costs but also positions organisations to maximise their cloud investment returns through improved operational efficiency and innovation capabilities.
Conclusion
The trajectory of Generative AI in data migration points to an increasingly transformative impact on enterprise data management. Organisations that strategically integrate AI-powered migration solutions position themselves to maximise the value of their data assets and accelerate innovation initiatives. While this technological advancement offers compelling opportunities, success hinges on implementing responsible AI practices that prioritise strong data security and privacy frameworks. By balancing innovation with protection, organisations can leverage AI’s capabilities to build competitive advantage in an increasingly data-centric business landscape.
