Overview

LumeMart, a mid-sized retail company, was struggling with duplicate customer records, inconsistent product data, and fragmented analytics across multiple systems. Bacancy’s developers implemented structured data cleansing and deduplication using fuzzy matching and rule-based merges. This improved data accuracy, enhanced reporting reliability, and enabled the business to gain actionable insights for inventory, sales, and customer behavior.

Technical Stack

  • Python
  • Pandas
  • SQL
  • Power BI
  • AWS Lambda
  • AWS S3

    • Industry

    Retail

  • region

    • Region

    USA

  • project-size

    • Project Size

    Non-Disclosable

Highlights

Removed duplicate records and standardized customer and product data

Automated data cleansing using fuzzy matching and rule-based merges

Improved analytics accuracy and reporting reliability

Enabled actionable insights for sales, inventory, and customer behavior

Challenges & Solutions

Duplicate Customer and Product Records

  • Solution: Bacancy implemented structured data cleansing using fuzzy matching and rule-based merges. The system identified and merged duplicate records across multiple sources, ensuring a single, accurate version of each customer and product.

Inconsistent Data Across Systems

  • Solution: Data was standardized with defined rules and transformation logic, cleaning formatting inconsistencies, missing fields, and errors. This unified data supports accurate reporting and reduces manual reconciliation.

Slow and Error-Prone Analytics

  • Solution: Cleaned and deduplicated data was integrated into Power BI dashboards, enabling reliable, real-time analytics. Decision-makers can now trust insights for inventory planning, sales tracking, and customer behavior analysis.

Maintaining Data Quality Over Time

  • Solution: Bacancy implemented automated scripts and scheduled workflows in Python to continuously monitor incoming data, detect duplicates, and apply cleansing rules. This ensures ongoing data quality without heavy manual intervention.

Core Features

  • Structured data cleansing with fuzzy matching and rule-based merges
  • Deduplication across multiple retail data sources
  • Automated and repeatable workflows for ongoing data quality
  • Reliable, real-time analytics in Power BI
  • no.-of-resources

    • No. of Resources

    05

  • time-frame

    • Time Frame

    January 2025 - June 2025

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