Regulatory Applications SQL Analytics Database

Project Overview

This project builds a PostgreSQL analytics database using a synthetic regulatory applications dataset.

It extends the earlier Python regulatory analytics project by moving the same operational data into a relational database and writing reusable SQL queries and reporting views.

The goal is to demonstrate SQL, database design, reporting logic, views, documentation, and Git-based project development.

Business Problem

Regulatory and approval-based organisations need reliable reporting on application volumes, SLA breaches, processing delays, department workload, and workflow bottlenecks.

This project answers questions such as:

• How many applications were processed?

• What percentage breached SLA?

• Which departments have the highest breach rates?

• Which application types are most at risk?

• Do missing documents affect processing performance?

• Do priority levels reduce delays?

• How does SLA performance change over time?

Tools Used

• PostgreSQL

• pgAdmin

• SQL

• Git and GitHub

Project Structure

regulatory_applications_sql

├── data

│   └── applications.csv

├── schema

│   ├── 001_create_database.sql

│   └── 002_create_applications_table.sql

├── queries

│   ├── 001_verify_import.sql

│   ├── 002_sla_overview.sql

│   ├── 003_sla_by_department.sql

│   ├── 004_sla_by_application_type.sql

│   ├── 005_missing_documents_impact.sql

│   ├── 006_priority_impact.sql

│   └── 007_monthly_sla_trends.sql

├── views

│   ├── 001_create_sla_summary_view.sql

│   ├── 002_create_department_sla_view.sql

│   ├── 003_create_application_type_sla_view.sql

│   └── 004_create_monthly_sla_trends_view.sql

├── docs

├── reports

├── README.md

├── CHANGELOG.md

└── .gitignore