Automated AI-Powered Data Quality Monitoring and Profiling System for Enhanced Data Integrity

The client faces significant challenges with inconsistent and inaccurate data originating from multiple sources, leading to unreliable analytical insights. Manual data profiling is time-consuming, error-prone, and hampers timely data ingestion, especially during data volume spikes. Delays and data quality issues increase operational costs and reduce confidence in data-driven decision-making.

A large-scale fintech firm handling diverse and sensitive data sources including customer transactions, market data, and internal operations, seeking to improve data quality and processing efficiency.

• Enhance data accuracy, consistency, and reliability through automated data profiling leveraging AI techniques.
• Streamline and optimize the data ingestion pipeline for faster processing from multiple data sources.
• Implement real-time data quality monitoring to promptly identify and resolve issues, ensuring high-quality data flow.
• Design a scalable system architecture capable of handling increasing data volumes without degradation of performance.
• Reduce operational costs by minimizing manual data profiling efforts and correcting data inaccuracies proactively.
• Improve confidence levels in data-driven insights, aiming for a target data quality rate of 95% and processing time reduction to approximately 8 hours per 1 terabyte dataset.

• Automated data collection, sorting, and categorization using machine learning algorithms such as classification and clustering.
• Recognition of data types, patterns, and anomalies without manual input.
• Real-time dashboards for continuous monitoring of data quality metrics and system performance.
• Automated alerting system to notify stakeholders immediately upon detection of data anomalies or quality issues.
• Configurable rules and filters for data validation, including preview and backtesting capabilities.
• Horizontal scalability to accommodate fluctuating data volumes seamlessly.

• Data sources like transactional databases, market data feeds, and operational systems for automated data ingestion.
• Existing analytics and reporting tools for visualization and insights dissemination.
• Notification systems for prompt alerts and issue escalation.

• Scalability to manage at least 30 terabytes of daily data processing, with performance unaffected during data volume surges.
• Reduction of data processing time from approximately 12 hours to around 8 hours per 1TB dataset.
• Real-time or near real-time data quality monitoring capabilities to detect issues within less than 1 hour.
• High availability, reliability, and security measures to protect sensitive data and ensure uninterrupted operations.

Implementing the automated AI-powered data profiling and quality monitoring system is expected to reduce data errors and inconsistencies by approximately 40%, achieving about 95% data quality rate. The data processing time will be decreased by 30%, enabling quicker data availability—down to approximately 8 hours per terabyte. Real-time monitoring will enable issue detection within an hour, significantly improving data reliability. The system’s scalability supports up to 30 terabytes of data daily, representing a 200% improvement in handling capacity, and overall confidence in data-driven decision-making could increase by 25%.