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Development of Statistical Anomaly Detection System for Survey Data Quality Assurance

stratoflow.com

Information technology

Business services

Education

Problem Statement

Inconsistent data quality from surveys administered by non-specialist interviewers introduces risks of human error and unconscious bias, threatening the validity of research outcomes and organizational credibility.

About the Client

A research organization specializing in data-driven studies across multiple sectors, requiring high-integrity data collection from non-specialist field personnel

Project Objectives

Implement automated data quality verification mechanisms
Reduce human-induced data anomalies by 70%
Enable real-time anomaly detection and correction workflows
Maintain compatibility with existing survey infrastructure

Functional Requirements

Automated data aggregation from multiple survey platforms
Statistical anomaly detection algorithms (e.g., outlier analysis, pattern recognition)
Interactive anomaly visualization dashboard
Integration with existing survey management system
Automated alert system for suspicious data patterns

Technology Preferences

Apache Spark

Hadoop ecosystem

Python (Pandas, NumPy)

SQL/NoSQL databases

Cloud-native architecture (AWS/Azure)

Integration Requirements

Legacy survey data collection platform
Cloud storage infrastructure
Research team collaboration tools

Non-Functional Requirements

Horizontal scalability for large datasets
Real-time processing capabilities
Role-based access control (RBAC)
99.9% system uptime SLA
Cross-platform UI compatibility

Estimated Business Impact

Implementation will enhance data reliability by 60%, reduce manual quality control efforts by 45%, and strengthen research credibility through systematic bias mitigation, directly supporting evidence-based decision-making and organizational reputation.