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Azure DevOps Governance Analytics
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Project Overview
This showcase demonstrates the end-to-end delivery of an Azure DevOps governance analytics solution in Power BI, designed to identify overdue approval requests, assess requirements health and provide executive visibility of material governance exceptions.
The project is presented as a practical case study demonstrating how business analysis and data analytics were combined across requirements definition, source assessment, data preparation, semantic modelling, DAX development, report design, validation and delivery governance to solve a defined business problem.
Solution Preview
Click the dashboard showcase to explore the project from Business Problem & Requirements.
The completed Power BI solution provides executive governance visibility, approval monitoring, actionable overdue exceptions and requirements-health analysis.
The four business-facing dashboards provide complementary views of the same governed Azure DevOps requirement population — from executive summary through analytical monitoring to actionable detail.
Explore the Project
Follow the project from business problem definition through data preparation, modelling, report development and validation.
- Business Problem & Requirements
- Solution Architecture
- Data Preparation
- Semantic Model
- Power BI Dashboards & User Experience
- Validation & Testing
- Requirements & Delivery Traceability
- Outcome & Lessons Learned
What This Project Demonstrates
- Business problem definition and requirements engineering
- Requirements traceability from business need through implementation and validation
- Azure DevOps data-source assessment and reporting design
- Power Query data preparation using current and historical work-item data
- REST API enrichment for requirements-completeness analysis
- Semantic-model design for current-state, historical and issue-level reporting
- DAX implementation of approval-governance and requirements-health business rules
- Executive, analytical and operational Power BI dashboard design
- KPI, filter and summary-to-detail reconciliation
- Controlled testing, defect resolution and Performance Analyzer validation
- Privacy-conscious portfolio publication using synthetic and anonymised evidence
- End-to-end analytics delivery combining Business Analyst and Data Analyst responsibilities
Tools and Technologies
| Tool / Technology | Use in the Project |
|---|---|
| Power BI Desktop | Data modelling, DAX development, dashboard creation, interaction design and validation |
| Power Query (M) | Data cleansing, transformation, current-state preparation and approval-history processing |
| DAX | Governance KPIs, approval-age calculations, requirements-health classification and executive reporting measures |
| Azure DevOps Analytics Views | Historical work-item and approval-governance reporting data |
| Azure DevOps REST API | Current work-item enrichment for Description and Acceptance Criteria completeness |
| WIQL | Identification of the current Azure DevOps work-item population used for targeted API retrieval |
| Azure DevOps Boards | Controlled source data, work-item relationships and approval-governance test scenarios |
| Power BI Performance Analyzer | Report performance testing and optimisation evidence |
| GitHub | Source control, project documentation and portfolio repository |
| GitHub Pages | Public presentation of the completed project case study |
| Markdown | Controlled project documentation and public-facing portfolio content |
| VS Code | Local documentation and repository maintenance |
Portfolio Evidence
The case study includes selected evidence from the completed project across the full analytics delivery lifecycle:
- Business problem definition and requirements
- Requirements traceability
- Azure DevOps source assessment and solution architecture
- Power Query data preparation
- Semantic-model design
- DAX measures and business logic
- Executive, analytical and operational dashboard design
- Source-to-report and business-rule validation
- Filter, interaction and reconciliation testing
- Performance Analyzer results
- Delivery documentation and governance controls
- Final solution outcomes and lessons learned
Public-facing evidence uses synthetic, anonymised or sanitised data. Working Power BI files, credentials and private implementation artefacts are retained outside the public portfolio.
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