Vijay Sariputta Richard

Vijay Sariputta Richard, also known professionally as Vijay Richard, is a former banking data and technology executive with nearly 25 years of experience across enterprise data transformation, data architecture, regulatory data, analytics, cloud, governance, data platforms and artificial intelligence.

His career has spanned large financial institutions, complex enterprise technology environments and, more recently, the creation of next-generation data and AI platform capabilities through Datahondo.

Vijay's work sits at the intersection of two disciplines that are increasingly converging inside global financial institutions: enterprise data leadership and the technology required to make data strategy operational at scale.

He brings experience across the full data lifecycle — from regulatory reporting, data integration and enterprise architecture to cloud transformation, data products, federated governance, real-time data, AI-ready infrastructure and the economics of modern data platforms.

Banking and Enterprise Data Leadership

A substantial part of Vijay's career has been spent solving data problems inside regulated banking environments.

His experience has included enterprise data warehousing, business intelligence, regulatory reporting, large-scale data engineering, cloud transformation and strategic data-platform development.

Earlier in his banking career, Vijay worked on the MAS 759/760 regulatory reporting programme covering unsecured-credit exposure. The programme involved complex integration and analysis across credit cards, loans and related products, reconciliation of measures against the general ledger, exposure analysis and regulatory reporting.

That experience established an important theme that has continued throughout his career: enterprise data cannot be separated from control, reconciliation, governance and accountability.

As banking data estates became larger and more distributed, his focus progressively moved from individual data solutions toward the architecture and operating models required to support data across an entire enterprise.

Transforming Data Platforms at Westpac

At Westpac Group in Australia, Vijay became closely involved in the evolution of the bank's strategic data-platform capabilities.

His work covered the journey from large-scale Big Data infrastructure toward cloud-native enterprise data platforms capable of supporting batch, real-time and streaming workloads.

He contributed to the development and scaling of Westpac's Big Data Platform and subsequently to ADAPT, a cloud Big Data platform built on Microsoft Azure.

The transformation included metadata-driven data engineering, real-time and streaming capabilities, platform automation, lineage, telemetry and approaches designed to enable enterprise data teams to operate at greater scale.

Vijay also worked on Westpac's implementation of data-mesh principles, including domain-oriented decentralised data ownership, data as a product, self-service data infrastructure and federated computational governance.

This experience shaped his view that modern data transformation is not simply a technology migration. Sustainable transformation requires architecture, governance, operating model, data ownership, platform engineering and business accountability to evolve together.

Westpac Technology Employee of the Year

In 2023, Vijay was named Technology Employee of the Year at the Westpac CEO Awards.

The award recognised his work in conceptualising, designing, building and deploying Westpac's strategic Big Data platform on Microsoft Cloud PaaS.

For Vijay, the significance of that work extended beyond moving technology to the cloud. It demonstrated how a major financial institution could rethink the foundations on which enterprise data products, analytics, governance and future AI capabilities would depend.

From Enterprise Operator to Founder and Product Builder

After almost seven years at Westpac, Vijay moved from operating and transforming enterprise data platforms inside a major bank to addressing many of the same structural problems from the outside.

Through Datahondo, he has been closely involved in creating EMDE — Evergreen Modern Data Ecosystem.

EMDE grew from a central question:

Why do enterprises repeatedly rebuild their data platforms every few years?

Large organisations commonly accumulate warehouses, lakes, lakehouses, streaming platforms, integration frameworks, governance systems and analytics engines over successive technology cycles. Each generation solves part of the problem while adding another layer to the enterprise estate.

EMDE represents an attempt to rethink that model around an evergreen, governed and interoperable data ecosystem rather than another isolated technology stack.

The work has extended Vijay's experience beyond enterprise architecture into product strategy, commercialisation, partnerships, cloud economics, customer engagement and the realities of turning architectural ideas into repeatable enterprise capabilities.

Data, Governance and AI

Vijay's current focus is increasingly centred on the convergence of Data, AI and enterprise platforms.

Generative and agentic AI are changing the requirements placed on enterprise data estates. AI systems need more than access to data. They require trusted context, metadata, lineage, security, governance, interoperability and mechanisms capable of turning intent into controlled production outcomes.

For highly regulated financial institutions, the challenge is therefore not simply to deploy more AI.

It is to create an environment in which AI can safely operate on trusted enterprise data while remaining explainable, governed, resilient and economically sustainable.

Vijay's perspective is informed by experience across both sides of this challenge: operating regulated banking data environments and building the underlying platforms intended to make data and AI capabilities easier to govern and scale.

The Evolving Chief Data & AI Officer Mandate

Vijay believes the role of the Chief Data Officer is entering a fundamental transition.

The traditional mandate centred heavily on governance, data quality, policy and regulatory control. Those responsibilities remain essential.

But the emergence of enterprise AI means that the next generation of Chief Data and AI leaders must increasingly understand the relationship between data strategy, governance, architecture, data products, cloud, platform economics, security, AI and business value.

The question facing global banks is no longer simply:

How should we govern our data?

It is increasingly:

How do we turn a complex global data estate into a trusted, governed and AI-ready strategic capability?

That is the problem space in which Vijay has spent much of his career.

His combination of banking experience, enterprise data architecture, cloud transformation, platform engineering, governance, AI and founder-level product experience gives him an unusual perspective on how financial institutions can approach the next generation of enterprise data transformation.

Current Work

Vijay is based in Singapore and works across banking, enterprise data, cloud and artificial intelligence through Datahondo and EMDE. His current areas of focus include:

• •enterprise Data and AI strategy;
• •modern data-platform architecture;
• •AI-ready data infrastructure;
• •data governance and federated operating models;
• •cloud and hybrid data platforms;
• •data products and interoperability;
• •platform simplification and economics;
• •enterprise AI and agentic systems;
• •security, resilience and operational controls; and
• •the evolving role of Chief Data and AI leadership in financial institutions.

Selected Perspectives

Vijay writes regularly about the evolution of enterprise data, AI and technology. Recent perspectives include:

The Data Platform Lie — Why successive generations of data platforms continue to recreate complexity.

Back Then We Built Warehouses. Now We Orchestrate Intelligence. — Reflecting on the evolution of enterprise data from traditional warehousing toward AI-era platforms.

Risk Accepting Vulnerable Software? — Examining how AI is changing software security and continuous platform assurance.

His wider writing explores data architecture, AI, privacy, cloud, cybersecurity, quantum technology and the relationship between technology and society.