Analytics & Insights

• Define descriptive measures and test them against source records and business rules
• Build and evaluate machine-learning models for specified segmentation or optimisation decisions
• Document self-service dataset owners, measure definitions, access rules and support procedures
• Trace and amend agreed workflows in Tableau, Power BI, Synapse, Python, R, Databricks, Spark, Power Query or DAX

• Configure Power BI semantic models, deployment pipelines, access rules and named dataset owners
• Compare Microsoft Fabric Lakehouse, Data Science and Real-Time Analytics workloads for an agreed preparation or modelling task
• Configure Azure Machine Learning, Azure AI services or Databricks MLflow for specified model training, evaluation and monitoring steps
• Test Azure Synapse, Delta Lake and serverless SQL datasets against agreed freshness, quality and query conditions

Fragmented reports, manual spreadsheets and conflicting KPIs make decisions difficult to explain. Rory traces disputed figures to their sources, defines calculation and ownership rules, and tests reports against agreed records and refresh conditions.

1. Define the decision: Record the question, user, source data, constraints and acceptance conditions.
2. Prototype and validate: Build the smallest useful report or model and test its calculations with responsible users.
3. Document operation: Configure agreed refresh and monitoring steps, record owners and limits, and test the handover.

Rory leads the analytics review and records source assumptions, model limits, measure owners and acceptance evidence. His public-sector background informs the attention given to auditability and handover. If an associate performs a defined engineering task, the role and output are agreed in advance.