Enterprise Data Quality & Governance Workflow (AI + Automation)
A smart data management pipeline that identifies errors, cleans datasets, fills missing values, and maintains data consistency across systems using ML.
🎯 Project Overview
Developed an AI-powered data governance workflow with automated profiling, cleaning, anomaly detection, and lineage tracking. Ensures consistent, validated, analytics-ready data across the organization. Reduced data errors by 80% and saved teams hours per day that were previously spent cleaning spreadsheets.
💼 Business Impact
- 80% reduction in data errors
- Hours saved daily - no more manual spreadsheet cleaning
- Prevented analytics failures and compliance risks
- Consistent data quality across all systems
- Automated compliance with data governance policies
🛠️ Technical Architecture
Workflow Components
1. Data Ingestion
Multi-source data collection: REST APIs, database connections (PostgreSQL, MySQL, MongoDB), S3/cloud storage, Excel/CSV file uploads. Scheduled and real-time ingestion pipelines.
2. Data Profiling
Automated analysis: completeness (missing values), uniqueness (duplicate detection), data types, value ranges, outlier detection, statistical summaries. Great Expectations for validation rules.
3. AI-Based Cleaning
Missing value prediction using ML models. Smart deduplication with fuzzy matching. Anomaly detection using isolation forests and autoencoders. Automatic data type correction.
4. Data Governance Layer
Schema validation against defined standards. PII (Personally Identifiable Information) detection and masking. Data quality scoring. Access control and audit logging for all data operations.
5. Automated Reports & Alerts
Daily data quality reports. Email alerts when quality thresholds are breached. Dashboard showing data health metrics. Trend analysis for data quality over time.
6. Data Catalog + Lineage Tracking
Centralized catalog of all data assets. Lineage tracking shows data flow from source to destination. Impact analysis: which downstream systems are affected by source changes.
Core Technologies
- Python - Data processing and ML pipeline
- Pandas - Data manipulation and cleaning
- Great Expectations - Data validation framework
- DBT - Data transformation and modeling
- Snowflake/BigQuery - Cloud data warehouses
- ML models - Anomaly detection and missing value imputation
🔧 Technical Challenges Solved
Challenge 1: Handling Large-Scale Data
Problem: Processing terabytes of data for profiling and cleaning is slow and expensive.
Solution: Distributed processing with Spark, incremental processing (only new/changed data), and sampling for profiling large datasets. Cloud data warehouses (Snowflake, BigQuery) for scalable compute.
Challenge 2: False Positive Anomalies
Problem: Anomaly detection flags too many false positives, overwhelming data teams.
Solution: Ensemble anomaly detection (combine multiple algorithms), confidence scoring, and learning from user feedback to reduce false positives over time.
Challenge 3: Data Lineage Complexity
Problem: Tracking data flow across hundreds of tables and systems is complex.
Solution: Automated lineage extraction from SQL queries, DBT models, and ETL logs. Graph database (Neo4j) for efficient lineage queries and visualization.
📊 Performance Metrics
💡 Key Features
- Automated profiling: Understand data without manual inspection
- AI-powered cleaning: Smart missing value imputation and deduplication
- Anomaly detection: Find data quality issues before they cause problems
- PII detection: Automatically identify and mask sensitive data
- Lineage tracking: See how data flows through your organization
🚀 Results
- ✅ 80% reduction in data errors across all systems
- ✅ Hours saved daily - eliminated manual data cleaning
- ✅ Zero analytics failures due to data quality issues
- ✅ 100% compliance with data governance policies
- ✅ Real-time monitoring catches issues before they impact business