Automated Data Quality Dashboard to Detect Anomalies & Improve Reporting

Automated Data Quality Dashboard to Detect Anomalies & Improve ReportingAutomated Data Quality Dashboard to Detect Anomalies & Improve Reporting

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Darshan Bs

• Jun 7

A few months ago, I was manually comparing source and target tables to find data issues.

Today, I built a Data Quality Monitoring Dashboard that automatically detects anomalies, tracks validation metrics, and provides real-time visibility into data health.

Here's what the project looked like 👇

───────────────────── 🔷 The Problem ─────────────────────

Modern data pipelines process thousands of records every day.

But one missing value, duplicate record, or failed transformation can impact reports, dashboards, and business decisions.

Teams often discover these issues too late.

My goal was simple:

Build a centralized dashboard that continuously monitors data quality and highlights issues before they become business problems.

───────────────────── ⚙️ Step 1 — Data Validation Framework ─────────────────────

Created automated validation checks across multiple datasets.

The framework monitors:

→ Record count mismatches → Null value violations → Duplicate records → Data completeness → Business rule validation → Source-to-target reconciliation

Instead of manually checking tables, every validation runs automatically.

───────────────────── 📊 Step 2 — Monitoring Dashboard ─────────────────────

Built an interactive dashboard to visualize data quality metrics.

Key sections included:

🟢 Data Quality Score

📈 Trend Analysis

⚠️ Failed Validation Checks

🔍 Table-Level Health Monitoring

📋 Data Reconciliation Status

The dashboard enables teams to quickly identify issues and take corrective action.

───────────────────── 💡 Key Outcomes ─────────────────────

✅ Reduced manual validation effort

⚡ Faster identification of data issues

📉 Improved reporting accuracy

🔍 Increased visibility into pipeline health

🚀 Enhanced trust in business-critical data

─────────────────────

Tech Stack:

• SQL • BigQuery • ETL Pipelines • Data Validation • Data Quality Monitoring • Data Warehousing

Data quality isn't just about fixing errors.

It's about building confidence in every business decision powered by data.

#DataEngineering #DataQuality #BigQuery #SQL #ETL #DataWarehouse #Analytics #Dashboard #PortfolioProject #DataGovernance

Rehbaz Ali

• Jun 15

Analytics Dashboard — Data Visualization UI Dashboards fail when they show everything at once. Designed a KPI-first analytics dashboard with clean charts, dark mode support, and a layout that surfaces what matters in under 3 seconds.

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