The Client
One of Australia’s four major banking groups, this organization operates a complex, multi-divisional technology environment spanning retail banking, institutional markets, and wealth management. As the bank accelerated its migration toward a hybrid-cloud architecture, maintaining a consistent and governed enterprise data vocabulary across legacy and modern platforms became both a regulatory necessity and an operational imperative.
The Challenge
As the bank expanded its data platform footprint, a growing ‘Governance Gap’ between divisional systems and enterprise standards began to threaten reporting accuracy and regulatory standing:
- Fragmented Truth: Business glossaries diverged across divisions, producing inconsistent data definitions that eroded trust in enterprise-level reporting and analytics.
- Restricted Visibility: Regulatory constraints limited direct access to source data, making centralized quality assessments across Snowflake, Teradata, and Databricks environments nearly impossible.
- Reactive Monitoring: Data quality issues were routinely identified by the consuming team rather than the producing team, creating high operational risk and delayed remediation.
- Manual Bottlenecks: Data teams relied on manual processes to replicate metadata between platforms, introducing errors and consuming significant engineering bandwidth.
The Impact
- Automated Synchronization across Snowflake, Teradata, and Databricks.
- Near-Real-Time Data quality tracking across enterprise platforms.
- Unified Governance language ensuring divisional alignment with enterprise standards.
The Solution
Hoonartek deployed the OneGov Platform – a centralized, metadata-driven architecture designed to synchronize governance definitions continuously across the bank’s multi-platform landscape. An automated framework was established to propagate enterprise Data Governance metadata to Snowflake, Teradata, and Databricks environments in near real-time, eliminating manual replication and the inconsistencies it introduced.
A near-real-time data quality tracking framework was developed using Google Pub/Sub and Ab Initio, ingesting quality results from external sources into a central DQ dashboard accessible to governance and operations teams. Semantic discovery logic was implemented to map technical physical names to enterprise business terms, ensuring every division operates from a unified data vocabulary aligned with enterprise standards.
Key Benefits
- Unified Data Language: Every division now operates from a consistent set of business definitions, eliminating reporting discrepancies caused by divergent glossaries.
- Reduced Manual Effort: Automated metadata propagation eliminated the engineering overhead of manual replication across platforms.
- Proactive Alerting: Threshold-based DQI scoring enables quality breaches to be resolved before they impact downstream reports.
- Lowered Infrastructure Costs: Consolidation of divisional governance platforms into a single scalable PaaS model reduced overall infrastructure overhead.
- Audit Readiness: Standardized definitions and synchronized metadata across platforms reduce regulatory risk and strengthen audit trails.