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Letting Data Speak, AI Act!
Case Study
Scaling Municipal Business Intelligence: Automating Business Record Processing for Civic Engagement Platform
About the Client
Client
A U.S.-based SaaS platform serving the civic engagement and economic development sector. The client provides centralized data management, outreach tools, and performance tracking solutions to help municipalities engage with local businesses.
Challenge
The client faced mounting challenges in maintaining clean business data as their operations scaled across multiple regions. Their existing workflows were plagued by inconsistencies, manual interventions, and fragmented data formats from various systems and third-party providers. Traditional manual approaches had failed to keep pace with hundreds of thousands of records requiring processing and deduplication. The challenge was further complicated by the need to establish optimal threshold settings for fuzzy matching algorithms when comparing new businesses against existing database entries, as improper thresholds resulted in either false duplicates or missed matches. Without a reliable automated solution capable of handling large-scale data processing and intelligent matching, the client risked compromising their core value proposition of providing municipalities with accurate business intelligence for local economic development initiatives.
Key Results
Reduced duplicate records by 90%, dramatically improving data reliability and decision-making capabilities
Increased field-level data completeness by over 70%, particularly for critical missing addresses and contact information
Achieved 90-95% match accuracy through optimized fuzzy matching thresholds and custom matching logic
Automated the full refresh pipeline, cutting manual workload.
Enabled scalable and reproducible data processing for hundreds of thousands of business records
Solution
JashDS implemented a comprehensive multi-phase automated data pipeline solution that transformed the client's business data infrastructure. The solution began with building end-to-end pipelines in Python, incorporating custom matching logic and transformation scripts specifically designed for business record processing.
The core deduplication engine utilized advanced fuzzy matching libraries including RapidFuzz and FuzzyWuzzy and Machine Learning techniques like Locality Sensitive Hashing to process data in chunks for large datasets to consolidate duplicate entries based on business name, location, and contact details. A fully automated data refresh process was created that seamlessly integrated third-party enrichment tools like Data Axle and Outscraper to enhance data completeness.
The technical architecture leveraged AWS S3 for cost-effective storage and versioning of intermediate and final datasets, while Pandas and Excel were employed for sophisticated data wrangling, quality checks, and intermediate validation steps. Comprehensive observability was enabled through AWS CloudWatch for log monitoring and proactive issue detection.
The methodology encompassed seven key phases: raw data ingestion from multiple sources, preprocessing with address parsing and contact normalization, fuzzy logic deduplication, database matching against the master business database, automated record updates with full change tracking, scheduled enrichment and refresh cycles, and comprehensive logging and reporting for transparency and downstream quality assurance.
Technologies Used
Python (pandas, RapidFuzz, FuzzyWuzzy)
AWS S3 (Object Storage)
AWS CloudWatch (Monitoring and Logging)
Machine learning (Locality Sensitive Hashing)
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