1. Scope and plan
Define audit objectives, analytical questions, data requirements, stakeholders, and request scope.
Read Phase 1Internal Audit + Applied Data Analysis
Applying Data Analysis in Internal Audit
A practical methodology for turning audit objectives, risks, and controls into answerable analytical questions, reproducible tests, and communicable results.
Current status
This is an early-stage methodology guide. It currently provides audit analytics guidance, templates, one synthetic dataset, and one initial worked example. Planned additions include Python notebooks, reusable checks, and visualizations.
What problem this addresses
Internal audit analytics work often jumps too quickly from available data to scripts and dashboards. This project separates two decisions: first, whether the audit question is suitable for data analysis; second, whether the analytical output is strong enough to rely on as audit evidence.
2. Collect and curate
Validate completeness, clean data, document transformations, and preserve traceability.
Read Phase 23. Analyze
Run exploratory and targeted analysis while preserving assumptions, limitations, and decision points.
Read Phase 34. Communicate
Translate analytical results into evidence-led audit communication with clear limitations.
Read Phase 4Practical guidance
Templates and examples
Design stance
Analytical outputs are not automatically audit findings. Exploratory patterns should be treated as leads until they are connected to criteria, validated data, reproducible logic, and appropriate business context.