Risk, controls, and data quality checks

Why this exists: Accounting data is only useful if you trust it. Track D teaches a light version of audit/control thinking for analysts.

Learning objectives

• Describe why controls and reconciliation matter for analytics.

• Run simple anomaly checks and interpret them carefully.

• Explain the difference between an error and a legitimate outlier.

Outline

Practical checks that scale

• Start from normalized/gl_journal.csv (and optionally normalized/chart_of_accounts.csv).

• Missing dates, negative amounts where unexpected, duplicated rows or duplicated transaction references (when present).

• Unusual spikes relative to typical ranges.

Sampling mindset

• You can’t check everything; choose samples based on risk and materiality.

• Document what you checked and what you didn’t.

When to stop and ask for accounting context

• A statistical red flag is not automatically fraud or error.

• Your next step is often: ask for invoices, contracts, or policy notes (e.g., revenue timing, refunds, capitalization).

Where this connects in the workbook

• Track D Outputs Guide (where checks appear in script outputs)

• Track D BYOD: Bring Your Own Data (validate step and why it exists)

Note

This page is intentionally an outline right now. Expand it incrementally as we refine Track D narrative.