Process Overview

Audit Scope

• Data Collection: Enrollment, attendance, grades, and extracurricular records.
• Data Storage: Relational databases, data warehouses, and backup procedures.
• Data Reporting: Dashboards, reports, and predictive models used by school leadership.

Key Audit Areas

• Business Intelligence: Use of Power BI/Tableau dashboards to visualise performance metrics.
• Relational Databases & SQL: Schema design, indexing, and query correctness for accurate data retrieval.
• Machine Learning & Predictive Analytics: Application of models to forecast enrollment trends or student risk.
• Statistical Foundations: Proper use of descriptive statistics, confidence intervals, and hypothesis testing in reporting.
• Python Automation: Scripts for data cleaning, transformation, and scheduled report generation.

Methodology

• Review of documented policies, data models, and dashboard specifications.
• Sampling of raw data and transformed outputs to verify accuracy and consistency.
• Interviews with teachers, IT staff, and administrators to understand workflow and controls.
• Technical testing of SQL queries, BI visualisations, and Python automation scripts.

Findings Summary

• Data pipelines generally maintain integrity, but several dashboards lack clear grain definitions.
• Some SQL queries omit necessary JOIN conditions, risking incomplete reports.
• Predictive models are not consistently validated against bias‑variance considerations.
• Automation scripts handle missing values inconsistently, leading to occasional data loss.

Recommendations

• Define and document the grain, time window, and metric definitions for all dashboards.
• Implement standardized SQL review checklists to ensure proper joins and aggregations.
• Establish a model validation framework that includes cross‑validation and error‑cost analysis.
• Refine Python scripts to enforce uniform missing‑value handling and include logging for audit trails.
• Conduct regular training for staff on best practices in BI, SQL, statistics, and automation.