Enhancing Enterprise Data Quality and Governance with IBM Knowledge Catalog
A large financial services enterprise in Australia was struggling to govern and operationalize data across hundreds of internal applications, legacy systems, and analytics platforms. As data volume surged, teams lacked clarity on where data originated, who owned it, and whether it could be safely used for business insights or AI workloads.
By implementing IBM Watson Knowledge Catalog (WKC) with Nexright, the organization gained a unified data governance layer, automated data classification, and governed self-service access—enabling analysts, data scientists, and compliance teams to trust and confidently use data at scale.
Business challenge
The organization’s rapid expansion led to fragmented data ownership, inconsistent definitions, and unclear lineage across mission-critical systems. Multiple teams produced and consumed data with little visibility into quality or risk, resulting in duplicated datasets, inaccurate reporting, and compliance exposure.
Key Challenges:
- No centralized inventory of enterprise data assets, making discovery time-consuming and error-prone.
- Inconsistent data definitions across departments, leading to conflicting analytics outputs.
- Limited visibility into data lineage, increasing regulatory and audit complexity.
- Manually intensive data governance workflows with no automation for classification or quality checks.
- Inability to scale AI and analytics initiatives due to uncertainty around data trust and usage restrictions.
The enterprise needed a single, governed data catalog that could automate classification, improve collaboration, and ensure that every data asset met regulatory, quality, and security requirements.
Solution
Partnering with Nexright, the organization deployed IBM Watson Knowledge Catalog as the foundation of its enterprise data governance and metadata management strategy.
Nexright designed and implemented a governed catalog model tailored to the customer’s business domain, regulatory requirements, and data landscape. This included role-based access, automated quality enforcement, and machine-learning-driven metadata enrichment.
Solution Highlights:
- Centralized Metadata Hub
Consolidated thousands of data assets into a single governed catalog with consistent business terms, policies, and ownership models. - Automated Data Classification
Leveraged AI-driven tagging to detect PII, financial identifiers, and sensitive categories automatically—reducing manual effort and audit risk. - Policy Enforcement & Access Governance
Implemented approval workflows, data masking rules, and usage policies aligned to APRA, PCI-DSS, and internal risk controls. - End-to-End Lineage Visualization
Mapped data flow from source systems to dashboards and models, improving traceability for audits, impact analysis, and engineering teams. - Self-Service Data Discovery
Enabled analysts and data scientists to independently find, understand, and request access to trusted data.
This holistic approach ensured that the catalog not only organized metadata but operationalized governance across all data-consuming teams.
Solution components
- IBM Watson Knowledge Catalog
- IBM Cloud Pak for Data
- IBM Watson Knowledge Studio (optional tagging enhancements)
Unified Data Governance
Provided a standardized framework for business terms, classifications, ownership, and lifecycle management—eliminating ambiguity and ensuring consistent interpretation.
Automated Metadata Enrichment
AI-powered discovery and classification reduced manual tagging time and instantly identified sensitive data across cloud and on-prem environments.
Policy-Driven Access Controls
Fine-grained entitlements with automated approvals improved compliance and ensured only authorized users consumed sensitive data.
Result
- 65% reduction in data discovery time, enabling analysts to find trusted data in minutes instead of days.
- 40% improvement in audit readiness, supported by clear lineage, cataloged controls, and automated policy enforcement.
- Significant reduction in duplicated datasets, lowering storage costs and eliminating inconsistent reporting.
- Faster AI/ML project delivery, as data scientists gained immediate access to governed, high-quality datasets.
- Enhanced consumer-data protection posture, reducing regulatory exposure across financial compliance frameworks.
Before this implementation, data was everywhere and nowhere. With Nexright and IBM Watson Knowledge Catalog, we finally have a single place where governance, lineage, and trust come together. Our teams can now use data with confidence—and deliver insights at a fraction of the previous time.
— Chief Data Officer, Financial Services Enterprise