Automated Data Quality Monitoring System
Data quality issues (duplicate records, missing fields, inconsistent formats) caused 20% of operational errors
Context
Multi-channel retailer managing product data, customer records, and order information across e-commerce, marketplaces, and B2B systems. Data entered manually by staff or imported from suppliers, leading to quality issues: duplicate product records, missing required fields, inconsistent formatting (product codes, addresses, phone numbers). Data quality problems caused operational errors: orders failed due to invalid product codes, shipping errors due to bad addresses, inventory discrepancies due to duplicate SKUs. Operations team spent 2 days monthly manually auditing data, fixing errors reactively. No systematic approach to data quality—problems discovered only after causing operational issues.
The Real Problem
Data quality issues were invisible until they caused operational problems—no proactive monitoring or validation. Manual data entry introduced errors: typos in product codes, missing fields, inconsistent formats. Data imports from suppliers had quality issues: different formats, missing required fields, duplicate records. No validation rules—system accepted any data format, errors only caught during order processing or shipping. Duplicate detection was manual—staff occasionally noticed duplicate products or customers, but no systematic process. Data cleanup was reactive—fixed problems after they occurred, but same issues recurred. Off-the-shelf data quality tools were enterprise-focused and expensive ($30K+ annually). No data quality metrics—couldn't measure improvement or identify problem areas. Staff lacked time for proactive data maintenance—focused on fixing immediate operational issues.