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Development of an AI-Driven Data Quality Monitoring and Profiling Platform

acropolium

Financial services

Information technology

Business services

Challenges in Data Quality and Manual Processing

The client faced significant data quality issues due to inconsistent and inaccurate data from multiple sources, leading to unreliable analytical insights. Manual data profiling methods were time-consuming, error-prone, and caused bottlenecks during peak data inflows, increasing operational costs and delaying decision-making.

About the Client

A leading fintech company specializing in financial, investment, and data management solutions for entrepreneurs, handling large volumes of customer transactions, market data, and internal operations.

Key Project Goals

Enhance data accuracy, consistency, and reliability using AI-driven automation.
Streamline data ingestion pipelines for diverse data sources.
Implement real-time quality monitoring to detect and resolve issues proactively.
Ensure horizontal scalability to handle fluctuating data volumes efficiently.
Reduce manual intervention and associated data management costs.

Core System Functionalities

Automated data classification and anomaly detection using ML algorithms.
Real-time dashboards for data quality monitoring and alerts.
Customizable rules for data validation and backtesting.
Horizontal scalability to manage increasing data volumes.
Integration with BI tools for actionable insights.

Technology Stack

Apache Spark

Apache NiFi

AWS

DBSCAN

SVM

Machine Learning

System Integrations

Existing data warehouses
BI platforms (e.g., Tableau, Power BI)
Cloud infrastructure (AWS)

Non-Functional Requirements

Scalability to handle 30+ TB/day data throughput.
Real-time processing latency under 1 hour for issue detection.
High availability and fault tolerance for continuous monitoring.
Data security and compliance for sensitive financial information.

Expected Business Impact

The solution is projected to reduce data errors by 40%, cut processing time by 30% (from 12 to 8 hours/TB), and enable seamless handling of 30+ TB/day. Real-time monitoring will improve decision-making confidence to 95%, with a 200% gain in scalability and significant cost savings from automation.