📊 Global Survey Data Quality & Reporting System (Excel-Based)

📌 Project Overview

This project demonstrates an end-to-end Excel-based reporting system designed to handle raw global survey data, apply data quality controls, perform validated analysis, and deliver executive-ready insights.

The solution follows enterprise analytics best practices, including:

• Raw data preservation
• Centralized validation rules
• Data quality tracking and logging
• Audit-friendly reporting
• Interactive dashboarding

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🧠 Business Problem

Global survey data often comes from multiple regions and sources, leading to:

• Inconsistent formatting
• Missing or invalid values
• Unreliable metrics if data quality is not controlled

Leadership requires:

• Trustworthy metrics
• Transparent data quality handling
• Simple, interactive reporting views

This project solves that by introducing a structured validation and reporting workflow.

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🏗️ Solution Architecture

Raw_Data
   ↓
Data_Validation (Control Tables)
   ↓
Clean_Data (Validation Flags & Standardization)
   ↓
Data_Quality_Checks & Data_Quality_Log
   ↓
Pivot Analysis
   ↓
Executive Summary & Interactive Dashboard

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📁 Workbook Structure

Sheet Name	Purpose
01_Raw_Data	Original survey data (unchanged, audit-safe)
02_Data_Validation	Master lists and validation rules
03_Clean_Data	Validation flags, standardized fields, record status
04_Data_Quality_Checks	Summary metrics for data quality
05_Pivot_Analysis	Analytical pivot tables
06_Executive_Summary	One-page leadership reporting view
07_Data_Quality_Log	Logged data issues, impact, and resolutions
08_Dashboard	Interactive Excel dashboard with slicers

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🧪 Data Validation & Quality Controls

Validation rules were applied using centralized control tables:

• Region validation
• Quarter validation
• Response value range (1–5)
• Record-level status flag (Clean / Issue)

Raw data was never overwritten. All issues were flagged, measured, and documented.

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📝 Data Quality Logging

A dedicated Data Quality Log was created to track:

• Issue type
• Affected quarter
• Business impact
• Resolution actions

This ensures traceability, transparency, and audit readiness.

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📈 Analysis Performed

Analysis was conducted only on validated (Clean) records, including:

• Average response score by region
• Compliance risk distribution (High vs Low Risk)
• Quarter-wise response trends (single-year dataset)

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📊 Executive Summary & Dashboard

Executive Summary

• Key KPIs at the top
• Clear regional and risk visuals
• Explicit data quality disclosure

Dashboard

• Interactive slicers (Region, Quarter)
• Limited visuals for clarity (3 charts max)
• KPI-driven design

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🔑 Key Metrics Included

• Total survey responses
• Average response score
• % High-risk responses
• % data quality issues identified

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🛠️ Tools & Skills Used

• Microsoft Excel
• Data validation & control tables
• Pivot tables & pivot charts
• KPI design
• Dashboard design
• Data quality management
• Business reporting & documentation

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🎯 Key Learnings

• Importance of preserving raw data
• Value of explicit data quality reporting
• How to design leadership-friendly dashboards
• Applying real-world analytics governance practices

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🚀 Future Enhancements

• Multi-year data for YoY trend analysis
• Power BI version of the dashboard
• Automated ingestion using SQL
• Advanced data standardization mappings

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📌 Author

Lakshmi Lahari Aspiring Data Analyst | Excel • SQL • Power BI • Data Quality & Reporting

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