Case Study

Data Landscape Modernisation and Microsoft Fabric Transition

Improving operational stability, strengthening data governance and defining a practical modernisation pathway within constrained funding.

Client

Small Australian Government Agency.

Program Context

Enterprise data warehouse uplift and future platform readiness.

Service Area

Data, Analytics and Artificial Intelligence.

Engagement Type

Key personnel supplied for data architecture, database administration, data management, reporting platform support and modernisation advisory.

The Challenge

A small Australian Government agency needed to uplift its data management maturity while maintaining the stability, security and performance of its existing Azure-hosted Enterprise Data Warehouse.

The agency had limited funding and needed practical, value-for-money support rather than a large transformation program. It required dependable business-as-usual SQL operations, improved documentation, stronger operational controls and clearer visibility of its data estate.

At the same time, the agency needed to prepare for future reporting, analytics and integration needs, including alignment with an upcoming platform transition. The challenge was to stabilise the current environment while establishing a credible pathway towards a more modern data architecture.

Pinaka’s Role

Pinaka supplied experienced data and platform personnel to support the agency’s data operations, governance uplift and modernisation planning.

The supplied personnel contributed to a baseline assessment of the Enterprise Data Warehouse configuration, performance, capacity and usage. They supported operational hardening activities across backup and restore checks, security review, health checks, performance monitoring and incident response.

They also helped improve documentation and handover readiness through configuration records, maintenance procedures, troubleshooting guides and a structured documentation repository. To support future readiness, the personnel contributed to Microsoft Fabric Lakehouse proof-of-concept work, including ingestion, transformation, semantic model and Power BI refresh patterns.

Capabilities Demonstrated

  1. Data Architecture
  2. Data Governance
  3. Data Engineering
  4. Database Administration
  5. Azure SQL | SQL Server
  6. Microsoft Fabric
  7. Lakehouse Architecture
  8. Power BI
  9. Reporting
  10. Data Catalogue
  11. Platform Modernisation
  12. AI-Ready Data Foundations

Outcomes Supported

Improved operational resilience

Improved operational resilience through proactive monitoring and repeatable database administration practices.

Reduced operational risk

Reduced operational risk through clearer ownership, stronger recoverability controls and handover-ready documentation.

Improved transparency

Improved transparency of the data estate through audit, catalogue and rationalisation activities.

standardised runbooks

Supported standardised runbooks for maintenance, backup and restore, monitoring and incident response.

database incident investigation

Assisted database incident investigation and remediation to reduce disruption to business applications and reporting.

Microsoft Fabric Lakehouse

Validated a future-state Microsoft Fabric Lakehouse pathway to support reporting, analytics and platform integration.

value-for-money decisions

Helped the agency make better value-for-money decisions through Azure SQL and Microsoft Fabric licensing assessment.

meaningful progress

Demonstrated how a smaller agency with limited funding can still make meaningful progress on data governance and modernisation.

Why it matters

This engagement demonstrates Pinaka’s ability to provide practical, senior data personnel who can help smaller government agencies stabilise existing data environments while planning for future analytics and AI-readiness. It shows that data modernisation does not always require a large-scale transformation program; with the right people, agencies can improve resilience, governance, documentation and future platform options within realistic funding constraints.

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