How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

In today’s rapidly evolving financial landscape, data is the bedrock of innovation, enhancing customer and employee experiences and securing a competitive edge. Recognizing this paradigm shift, ANZ Institutional Division has embarked on a transformative journey to redefine its approach to data management, utilization, and extracting significant business value from data insights.

Like many large financial institutions, ANZ Institutional Division operated with siloed data practices and centralized data management teams. As time went on, the limitations of this approach became apparent due to rising data complexity, larger volumes, and the growing demand for swift, business-driven insights. Consequently, the bank encountered several challenges and needed to take the following actions:

• Create business insights from untapped data potential, estimated to be approximately $150 million in the Institutional Division alone
• Improve operational efficiency by removing manual data handling, the use of spreadsheets, and duplicate data entries
• Increase agility by making data expertise more readily available, thereby improving time to market and overall customer experience
• Address data quality
• Standardize tooling and remove the Shadow IT culture, driving scalability, reducing risk, and minimizing overall operational inefficiencies

These challenges are not unique to ANZ Institutional Division. Globally, financial institutions have been experiencing similar issues, prompting a widespread reassessment of traditional data management approaches.

One major trend, embraced by many financial institutions, has been the adoption of the data mesh architecture and the shift towards treating data as a product. This paradigm, pioneered by thought leaders like Zhamak Dehghani, introduces a decentralized approach to data management that aligns closely with modern organizational structures and agile methodologies.

Some notable global examples of leading companies embracing and implementing this trend are JPMorgan Chase, Capital One, and Saxo Bank.

Inspired by these global trends and driven by its own unique challenges, ANZ’s Institutional Division decided to pivot from viewing data as a byproduct of projects to treating it as a valuable product in its own right.

This shift promises several business benefits:

• Empowered domain expertise – By decentralizing data ownership to domain-based teams, ANZ can use the deep business knowledge within each unit to create more relevant and valuable data products
• Increased agility – Domain teams can now respond more quickly to business needs, creating and iterating on data products without relying on a centralized bottleneck
• Improved data quality – With domain experts overseeing their own data, there’s a greater likelihood of catching and correcting quality issues at the source
• Scalability – The federated approach allows for greater scalability, enabling ANZ to handle increasing data volumes and complexity more effectively
• Innovation catalyst – By democratizing data access and empowering teams to create data products, ANZ is fostering a culture of innovation and data-driven decision-making across the organization

This transition is not just about technology; it represents a fundamental shift in how ANZ views and values its data assets. By treating data as a product, the bank is positioned to not only overcome current challenges, but to unlock new opportunities for growth, customer service, and competitive advantage.

This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division.

ANZ’s federated data strategy

In response to the challenges, ANZ Group formulated a data strategy that focuses on empowering employees to securely use data to improve the sustainability and financial well-being of their customers. At its core are the following pillars:

• Introducing new ways of working that focus on generating customer value first
• New technology platforms and tooling that allow the bank to collect, share, archive, and dispose data in a secure and controlled way
• Achieving consistency in how data is produced and consumed across the entire bank through data products and better-connected systems
• Supporting the bank’s risk and regulatory obligations by providing a secure and resilient data platform that provides fine-grained, controlled access to quality data products

ANZ has made the strategic decision to adopt an architectural and operational model aligned with the data mesh paradigm, which revolves around four key principles: domain ownership, data as a product, a self-serve data platform, and federated computational governance.

Domain ownership recognizes that the teams generating the data have the deepest understanding of it and are therefore best suited to manage, govern, and share it effectively. This principle makes sure data accountability remains close to the source, fostering higher data quality and relevance.

Treating data as a product instils a product-centric mindset, emphasizing that data must be secure, discoverable, understandable, interoperable, reusable, and managed throughout its lifecycle. This principle makes sure data consumers, both internal and external, derive consistent value from well-designed data products.

A self-serve data platform empowers domains to create, discover, and consume data products independently. It abstracts technical complexities and provides user-friendly tools, enabling a scalable, repeatable, and automated approach to producing high-quality data products.

Under the federated mesh architecture, each divisional mesh functions as a node within the broader enterprise data mesh, maintaining a degree of autonomy in managing its data products. To effectively coordinate these autonomous nodes and facilitate seamless integration, enterprise-wide standards, such as those related to data governance, interoperability, and security, are essential to maintain alignment and consistency across all nodes and domains and teams within.

With this approach, each node in ANZ maintains its divisional alignment and adherence to data risk and governance standards and policies to manage local data products and data assets. This enables global discoverability and collaboration without centralizing ownership or operations.

As a result, governance resides with the data products themselves, making sure standards and policies, such as access control, data quality, and compliance, are enforced where the data lives. In this regard, the enterprise data product catalog acts as a federated portal, facilitating cross-domain access and interoperability while maintaining alignment with governance principles. This model balances node or domain-level autonomy with enterprise-level oversight, creating a scalable and consistent framework across ANZ.

Within the ANZ enterprise data mesh strategy, aligning data mesh nodes with the ANZ Group’s divisional structure provides optimal alignment between data mesh principles and organizational structure, as shown in the following diagram.

Central to the success of this strategy is its support for each division’s autonomy and freedom to choose their own domain structure, which is closely aligned to their business needs. Divisions decide how many domains to have within their node; some may have one, others many. These nodes can implement analytical platforms like data lake houses, data warehouses, or data marts, all united by producing data products. Nodes and domains serve business needs and are not technology mandated.

Under the federated computational governance model, the ANZ Group strategy defines guardrails that treat a node as a logical data container suitable for the following:

• Ingestion and metadata management
• Creating source-aligned data products complying with ANZ’s Data Product Specification (DPS)
• Integrating source-aligned data products from other nodes
• Producing consumer-aligned data products for specific business purposes
• Publishing conforming data products to ANZ’s Data Product Catalog (DPC)

Following on from this strategy is organizing its domain structure to provide autonomy to various functional teams while preserving the core values of data mesh. The following diagram depicts an example of the possible structure.

For instance, Domain A will have the flexibility to create data products that can be published to the divisional catalog, while also maintaining the autonomy to develop data products that are exclusively accessible to teams within the domain. These products will not be available to others until they are deemed ready for broader enterprise use.

This strategy supports each division’s autonomy to implement their own data catalogs and decide which data products to publish to the group-level catalog. This flexibility extends to divisional domains, which can choose which data products to publish to the divisional catalog or keep visible only to domain consumers.

Institutional Data & AI Platform architecture

The Institutional Division has implemented a self-service data platform to enable the domain teams to build and manage data products autonomously. The Institutional Data & AI platform adopts a federated approach to data while centralizing the metadata to facilitate simpler discovery and sharing of data products. The following diagram illustrates the building blocks of the Institutional Data & AI Platform.

The building blocks are as follows:

1. Foundational Data & AI Platform capabilities – A dedicated data platform team provides domain-agnostic tools, systems, and capabilities to enable autonomous data product development across domains. This self-serve infrastructure allows domain teams to manage the full data lifecycle without relying on a centralized data team. Key capabilities include data storage, data onboarding and transformation, and data utilities that facilitate data sharing with interoperability between domains. These capabilities abstract the technical complexities associated with data management infrastructure, allowing domain experts to focus on creating valuable data products rather than infrastructure management.
2. Domain-owned data assets – The domain-oriented data ownership approach distributes responsibility for data across the business units within the Institutional Division. Domain teams are responsible for developing, deploying, and managing their own analytical data products alongside operational data services. Data contracts authored by data product owners automate data product creation and provide a standard to access data products. By treating the data as a product, the outcome is a reusable asset that outlives a project and meets the needs of the enterprise consumer. Consumer feedback and demand drives creation and maintenance of the data product.
3. Division-level metadata management and data governance – A centrally hosted service provides domain teams with the capability to publish their data products along with relevant metadata, like business definitions and lineage. Some of the key features implemented are:
   1. Metadata management that centralizes metadata and presents it within the context of data products, such as data quality scores and data product lineage.
   2. A data portal for consumers to discover data products and access associated metadata.
   3. Subscription workflows that simplify access management to the data products.
   4. Computational governance that enforces divisional and enterprise data policies and standards, such as data classification and business data models for aligning terminology.

The following diagram is a high-level example of the technical architecture approach towards the Institutional Data & AI Platform. The solution uses a building block approach, on a cloud-centered platform comprised of AWS services, with partner solutions and open standards like OpenLineage and Apache Iceberg.

Let’s look at the key services that enable the federated platform to operate at scale:

• Data storage and processing:
  • Apache Iceberg on Amazon Simple Storage Service (Amazon S3) offers an optimized way to store data assets and products and promotes interoperability across other services
  • Amazon Redshift allows domain teams to create and manage fit-for-purpose data marts
  • AWS Lambda and AWS Glue are used for data onboarding and processing, and data utilities created in Python and PySpark promote reusability and quality across the data processing pipelines
  • dbt simplifies data transformation rules and allows sub-domain data analysts to build modeling logic as SQL statements
  • Amazon Managed Workflows for Apache Airflow (Amazon MWAA) enables efficient management of workflows and data pipeline orchestration using out-of-the-box integrations with AWS services
• Metadata management and data governance:
  • To maintain data reliability and accuracy, a robust data quality framework using Soda core is used that automates data quality using checks defined in a data contract
  • Amazon DataZone enables data product cataloging, discovery, metadata management, and implementing computational governance
  • OpenLineage simplifies harvesting and collection of data and process-level lineage, which are then published to Amazon DataZone
  • AWS Lake Formation, combined with AWS Glue Data Catalog, provides data governance and access management to data products that reside within sub-domains
• Analytics:
  • Tableau offers capabilities for sub-domains with data visualization and business intelligence capabilities
• Observability and security:
  • Observability needs of the platform are built into all the processes using monitoring, with logging functionality provided by Amazon CloudWatch and AWS CloudTrail
  • AWS Secrets Manager makes sure secrets are stored and made available for data pipelines to access services in a secure manner

The technical implementation actualizes the data product strategy at ANZ Institutional Division. Amazon DataZone plays an essential role in facilitating data product management for the domain teams. The service addresses several critical aspects of the Institutional Division’s data product strategy, including:

• Data cataloging and metadata management – Amazon DataZone provides comprehensive data cataloging and metadata management capabilities
• Data governance and compliance – Effective data governance is essential for scaling data products
• Self-service capabilities – Amazon DataZone empowers domain teams with self-service capabilities, enabling them to create, manage, and deploy data products independently
• Integration and interoperability – One of the challenges in scaling data products is providing seamless integration across various data sources and systems
• Collaboration and sharing – Amazon DataZone provides a platform for sharing data and metadata across teams and domains

Institutional Division’s delivery model to achieve scale

The Institutional Division has successfully used the federated architecture, and key to this delivery model is the implementation of Foundational Data & AI Platform capabilities that serve all domains within the division. This model promotes self-service and accelerates the delivery of subsequent initiatives by using the capabilities built for previous use cases.

To evaluate the success of the delivery model, ANZ has implemented key metrics, such as cost transparency and domain adoption, to guide the data mesh governance team in refining the delivery approach. For instance, one enhancement involves integrating cross-functional squads to support data literacy.

The key to scaling the Institutional Division operating model are the following considerations:

• Data as a product approach – Use techniques like event storming and domain-driven design to capture business events and their meanings.
• Education and enablement – Conduct learning interventions to upskill teams on understanding and using the data as a product approach.
• Iterative data platform delivery – Work backward from business initiative to iteratively deliver self-service data platform infrastructure capabilities.
• Managing demand efficiently – Implement a feedback mechanism to manage demand on data products. Track and manage data debt using standard data contract specifications. Most importantly, adopt governance and standards to make sure data products are built and maintained with a long-term perspective, minimizing technical debt.

“The Institutional Data & Analytics Platform (IDAP) has allowed the Institutional team to establish a base foundation to allow various teams to aggregate and consume the wealth of data across the division. This self-service platform enables business leaders to both create and consume reusable data products, unlocking value across this division. It’s also an excellent proof point for our broader data mesh architecture, allowing us to connect this divisional data to broader enterprise data stores—further positioning us to put the customer at the center of everything we do.”

– Tim Hogarth, CTO ANZ

“AWS believes that democratizing data, while not compromising on security and fine-grained access, is a key component of any future-proof, scalable data platform, so we are pleased to be enabling ANZ bank’s IDAP metadata management and data governance capabilities through Amazon DataZone. This allows the diverse business functions at ANZ the autonomy to self-serve on their data needs with built-in governance.”

– Shikha Verma, Head of Product, Amazon DataZone

Conclusion

ANZ’s journey to move towards a data product approach has improved the organization’s approach to manage data and reduce data silos, and has positioned it to become a data-driven, customer-centric organization. By combining federated platform practices and adopting AWS services and open standards, ANZ Institutional Division is achieving its objectives in decentralization with a scalable data platform that enables its domain teams to make informed decisions, drive innovation, and maintain a competitive edge.

Special thanks: This implementation success is a result of close collaboration between ANZ Institutional Division, AWS ProServe, and the AWS account team. We want to thank ANZ Institutional Executives and the Leadership Team for the strong sponsorship and direction.

About the Authors

Leo Ramsamy is a Platform Architect specializing in data and analytics for ANZ’s Institutional division. He focuses on modern data practices, including Data Mesh architecture, data governance, quality management, and observability. His work aligns data strategies with business goals, improving accessibility and enabling better decision-making across ANZ.

Srinivasan Kuppusamy is a Senior Cloud Architect – Data at AWS ProServe, where he helps customers solve their business problems using the power of AWS Cloud technology. His areas of interests are data and analytics, data governance, and AI/ML.

Rada Stanic is a Chief Technologist at Amazon Web Services, where she helps ANZ customers across different segments solve their business problems using AWS Cloud technologies. Her special areas of interest are data analytics, machine learning/AI, and application modernization.