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Overview
📖 AWS re:Invent 2025 - Utilizing AWS GenAI to deliver enterprise data modernization in CommBank-AIM416
In this video, Commonwealth Bank of Australia's Terri Sutherland and HCLTech's Puneet Makhija discuss the largest data migration in the southern hemisphere. CBA migrated 61,000 on-premise pipelines and 10 petabytes of data to AWS in nine months, moving their entire data engineering and AI workforce to the cloud. The bank established CommBank.data, a data mesh ecosystem serving 40 lines of business with a producer-consumer model and unified data marketplace. HCLTech deployed over 250 AWS-certified engineers using the Dreyfus Competency Framework and full cycle engineering approach. They leveraged AI and generative AI for code transformation and testing, achieving 100% data reconciliation across 229,000 tests. The partnership between CBA, AWS, and HCLTech followed the "One Team One Dream" approach, utilizing steel threads methodology and outcome-based delivery to successfully complete this pathbreaking platform modernization.
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Main Part
Introduction: CBA's Position as Australia's Largest Bank and Strategic Partnerships
Good evening all. I'm Srini Seshadri, global head of Financial Services for HCLTech. AWS has been a great partner for us, and we've recently signed an FS-specific SCA. We've also partnered with Commonwealth Bank of Australia for almost two decades, and we are extremely thrilled to have Terri Sutherland, who is the general manager for data platforms and operations. She oversaw what is probably the largest data migration in the southern hemisphere, and we look forward to hearing insights from her on this pathbreaking platform as well as other insights on data modernization. Terri Sutherland, please.
Thank you, Srini. Hello, everyone. I'm Terri Sutherland, general manager of the Enterprise Data Platforms at the Commonwealth Bank of Australia, and I'm excited to be here with you today to talk about our enterprise data platform modernization and our strategic partnership with HCL and how we achieved the largest and fastest migration in the southern hemisphere. But first, let me tell you about the bank itself. Though many of you may never have heard of the Commonwealth Bank of Australia, or CBA as we call it, we are Australia's largest bank. Australia's population is 27 million, and today CBA services 17.5 million customers, which means one in three Australians and one in four businesses bank with us. Overall, 50% of Australia's transactions go through CBA, and on the global stage, we are the 13th largest bank by market value. I'm very excited to say that we've just been named the top four bank globally for AI maturity.
CBA's Data Strategy: Three Core Pillars and the CommBank.data Ecosystem
I'd like to talk to you briefly about our data strategy and then how the data platform modernization fits in. At the heart of our transformation are three core pillars: people, safeguards, and technology. On people, we recognize that a centralized data team couldn't keep pace with our business demand to scale on AI. So we embedded data engineers and data scientists directly into the lines of business, bringing data closer to the people who use it and closer to the people we serve, our customers. As a bank, safeguards are non-negotiable, so we embedded governance and risk controls at every stage of the data and AI lifecycle, ensuring safety by design.
And lastly on technology, we needed to connect decades of rich data spread across hundreds of source systems to empower our federated data teams on the latest tooling. To achieve this, we established a data mesh ecosystem on AWS cloud that empowers our federated teams to operate independently, move data, and use data seamlessly, all the while enforcing strict governance. We call this ecosystem CommBank.data. Today, it empowers 40 lines of business with the freedom to produce and use data as needed within a trusted and controlled framework. To enable this, we decentralized and adopted a clear producer-consumer model. Each business unit now builds, owns, and manages its data as a product with defined roles and responsibilities. We've also introduced self-service data sharing through a unified data marketplace, a single pane of glass where users can discover, request, and consume data across the entire AWS ecosystem.
The Southern Hemisphere's Largest Data Migration: 61,000 Pipelines in Nine Months
However, data has gravity, and where the data lives is where every data scientist and data engineer in the bank will work. Historically, CBA held years of rich data on-premise platforms that lacked interoperability and could not scale for AI. So we made a bold move. We migrated 61,000 on-premise pipelines to our AWS mesh ecosystem, equivalent to 10 petabytes of data. The migration took nine months with 100% of data pipelines tested three times, that's 229,000 tests. In doing so, we moved our entire data engineering and AI workforce to AWS cloud. So how did we do such a large migration so quickly? This is where our partnership with AWS and HCL comes in.
Early last year, we kicked off a series of workshops with AWS and HCL to test our most complex data pipelines and AI use cases to see if the migration to AWS native technologies was possible. We call this approach steel threads. Unlike a proof of concept or an MVP, steel threads prove the technology, but they also productionalize the outcome. Working closely with HCL, we built AI and generative AI that transform code, check for errors, and then tested the output, reconciling the tables exactly to our on-premise platform 100%. Every single row, column, and number needed to be accounted for.
Throughout the nine month migration, CBA, AWS, and HCL worked closely as a unified tight knit team. We adopted this approach to stop any potential misalignment, often associated with big programs of work and ambitious deadlines. We prioritized establishing a shared purpose and operating cadence, ensuring each member of the team felt a real sense of ownership toward achieving our common goal. And now, with our AWS mesh ecosystem in place and evolving our on-premise migration to that ecosystem complete, we are ready to scale on CBA's ambitious AI-powered, data driven future.
HCL Tech's Engineering Excellence: Critical Success Factors and AI-Driven Delivery Model
Thank you, Terri for sharing the insights. I'm Puneet Makhija, delivery partner at HCL Tech, leading high performing teams delivering solutions at CBA. Let me dive into some of the critical success factors for this migration. Best in class and fit for purpose engineering practices. At HCL Tech, engineering is at the core of everything we do. Industry is seeing a convergence of data, AI, and engineering, and we leverage best practices across these paradigms for this program.
We began early, strategically sourcing and deploying talent in phases to access the ideal talent pool. The data and AI lab created an environment that encouraged learning and innovation, helping teams to adapt to the program requirements. We improved test and migration quality in each sprint by using metrics and tracking progress. The delivery construct for this program was truly outcome-based.
We organized the teams according to the Dreyfus Competency Framework, which helped us create a cognitively diverse team structure. Connect pods led enablement, scale migration, and automation and acceleration spots across the program. Elite AI engineers were embedded in the teams to foster creative tension and consistently identify ways to accelerate. We ramped up our engineering team early, peaking at over 250 AWS-certified data engineers. We began upskilling and certifications early, so our team was ready and effective from the start of the migration.
We shifted from specialized roles to full cycle engineering, aligned to CBA's model to deliver solutions effectively. In short, we used three transformation levers: AWS certification for engineers, focused on full cycle engineering and AI-driven automation. Developing automation accelerators and parallel testing enabled us to successfully migrate 61,000 pipelines and over 10 petabytes of data to AWS in less than nine months with precision and meeting quality objectives.
An important takeaway from this migration is that we are committed to incorporating AI accelerators for all future transitions and legacy migration initiatives. We are working towards bringing the agent-led delivery cycle, ALDC, to life in the future. As an organization, we have embraced a future-fit delivery model as the minimum standard for any search initiative. A significant factor in our success has been the strategic partnership with AWS and strong collaboration from the top down. The mantra, One Team One Dream, united CBA, AWS, and HCL Tech, leading to the program's success. This achievement marks the first of its kind, and we look forward to future collaborations.
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