Process Overview

Key Areas Assessed

• Data acquisition & storage – relational databases, SQL, data warehouses, and star schemas.
• Data preparation & programming – Python structures, NumPy, pandas, and automation scripts.
• Statistical & mathematical foundations – probability rules, confidence intervals, and sampling concepts.
• Machine learning model development & evaluation – supervised/unsupervised learning, over‑fitting checks, bias‑variance trade‑off, and appropriate metrics.
• Business intelligence & visualization – Power BI, Tableau, chart selection, and insight relevance.

Audit Criteria

• Data integrity: validation, handling of missing values, and correct use of indexes.
• Model governance: proper training/test splits, avoidance of over‑fitting, and documented evaluation procedures.
• Documentation: clear definitions of grain, time windows, and metric calculations.
• Reproducibility: version‑controlled scripts, automated pipelines, and audit trails for transformations.
• Insight relevance: dashboards answer the right business question and avoid misleading visualizations.

Required Skills for Auditors

• Proficiency in SQL and relational database concepts.
• Familiarity with Python data‑science libraries (pandas, NumPy).
• Understanding of statistical inference, probability, and sampling error.
• Knowledge of machine‑learning workflows, evaluation metrics, and bias‑variance considerations.
• Ability to assess BI visualizations for clarity, correctness, and business impact.

Recommended Practices

• Implement star schemas to separate facts and dimensions for analytical consistency.
• Match chart types to the analytical question (comparison, composition, distribution, relationship, change over time).
• Maintain a documented data dictionary, model registry, and version‑controlled code base.
• Conduct regular peer reviews of model performance against defined business KPIs.
• Ensure comprehensive audit trails for all data transformations and model versioning.