A leading digital bank \u2014 new to market, with a large and rapidly evolving data estate \u2014 needed to build confidence in its data products from day one. Waiting until a data model was largely built before preparing test data was creating friction that a modern engineering organization couldn’t absorb.<\/p>\n
Working with a GenRocket channel partner, the bank adopted a Data-as-Code approach powered by GenRocket. Rather than treating synthetic data as a downstream testing task, the team used GenRocket’s JSON Schema and Web APIs to build governed, UI-visible synthetic data projects programmatically \u2014 alongside each data model. The result:<\/strong> synthetic data now lives inside the engineering lifecycle, not bolted on at the end.<\/p>\n The client is a leading digital bank standing up its technology estate at pace. As a new digital-first institution, confidence in its data products has to be built in from day one \u2014 there is no legacy safety net to fall back on. The estate is large, complex, and changing frequently, with data models evolving alongside the systems they power.<\/p>\n The engagement was delivered in partnership with a GenRocket delivery service provider on the account. GenRocket was already in use inside the organization, but initial adoption had been concentrated in several independent use cases. The opportunity: move beyond point solutions and position synthetic data generation across the full engineering lifecycle \u2014 starting with the bank’s Data Chapter, the team responsible for the data platform and data products.<\/p>\n Traditional test data preparation didn’t fit how this bank builds. The team was hitting the limits of a legacy pattern:<\/p>\n The team’s central question reframed the problem: could synthetic source data be built alongside each data model, so the same controlled inputs support development, QA, and automated validation from the very first commit?<\/p>\n Working with the GenRocket channel partner, the bank built a Data-as-Code approach on top of GenRocket’s Design-Driven Synthetic Data platform. The core idea: treat each synthetic data project as a versioned engineering asset that is created and provisioned programmatically \u2014 not set up by using a UI.<\/p>\n The Data as Code approach shifted synthetic data from a testing utility to a core engineering capability. Early outcomes include:<\/p>\n This engagement demonstrates a shift in how mature engineering organizations think about test data. Synthetic data is no longer a downstream QA task \u2014 it is an upstream engineering asset, versioned and provisioned like any other piece of infrastructure. For a digital bank operating in a fast-moving, regulated environment, that shift matters:<\/p>\n With the Data as Code foundation in place, the bank and delivery services partner are extending the pattern into new territory:<\/p>\n For this leading digital bank, synthetic data is no longer a testing artifact \u2014 it is code. Defined declaratively, provisioned programmatically, governed centrally, and reused across the full engineering lifecycle. In partnership with the GenRocket services delivery partner, GenRocket has moved from a helpful data solution to a foundational platform for how the bank builds and validates its data products.<\/p>\n The result is a modern engineering discipline: repeatable, governed, and built for a data estate that never stops changing. For any organization asking how synthetic data belongs inside \u2014 not alongside \u2014 the software lifecycle, this engagement is the answer.<\/p>\n
→ Test → Change<\/div>\n<\/p><\/div>\nContext & Overview<\/h2>\n
The Validation Challenge<\/h2>\n
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The GenRocket Solution: Data as Code<\/h2>\n
How It Works<\/h2>\n
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\n FROM<\/strong><\/th>\n TO \u2014 with Data as Code on GenRocket<\/strong><\/th>\n<\/tr>\n<\/thead>\n\n \n \nSynthetic data designed after the Model is created<\/td>\n Synthetic data built alongside every data model<\/td>\n<\/tr>\n \n \nProject configuration in the UI<\/td>\n Programmatic provisioning via JSON Schema and Web APIs<\/td>\n<\/tr>\n \n \nProject updates required whenever the data model changes<\/td>\n Regenerate the project from an updated schema<\/td>\n<\/tr>\n \n \nIndependent, use-case-specific adoption<\/td>\n Repeatable pattern across the Data Chapter and Enterprise<\/td>\n<\/tr>\n \n Testing treated as a separate stage<\/td>\n Synthetic data embedded across Dev, Build, Deploy, Test, Change<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Results & Benefits<\/h2>\n
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Why This Matters<\/h2>\n
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What’s Next<\/h2>\n
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Conclusion<\/h2>\n