Ticket Data Matching System with Incremental Processing

Automates the processing of repeated ticket data by reusing historical results and reducing manual classification work.

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Project Overview

In multi-source support workflows, similar or identical records are processed repeatedly.

This leads to:

• duplicated manual work
• inconsistent classification results
• increasing effort as data volume grows

This project introduces a reusable data matching system that separates processing logic from data storage and enables scalable, incremental data processing.

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Problem

Data collected from multiple sources requires repeated manual checking and classification.

Existing workflows rely on:

• manual filtering
• repeated validation of known records
• reprocessing of already handled data

This results in inefficiency and inconsistency across teams.

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Design

The system is designed around three key decisions:

• Reusable historical dataset (JSON)
  Stores previously processed results to avoid reprocessing identical records

• Separation of concerns
  Distinguishes between data input (Google Sheets), processing (Python), and storage (JSON)

• Incremental processing approach
  Only new or unmatched records are processed in each run

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Impact

• reduces repeated manual classification effort
• ensures consistent results across repeated processing runs
• enables scalable processing as data volume increases
• supports reuse of validated historical data

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Example (Before → After)

Before

After

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Workflow (Core Logic)

New Data
   ↓
Check Against Historical Dataset
   ↓
Reuse Known Results
   ↓
Process Only New / Unmatched Records
   ↓
Update Dataset for Future Runs

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Input / Output

Input

• raw records from Google Sheets
• existing historical dataset (JSON)

Output

• structured and classified results in spreadsheet
• updated historical dataset for reuse

Results are grouped by classification categories to improve readability and highlight processing outcomes.

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Project Structure

Modules

• data_preparation
  transforms raw spreadsheet data into structured datasets

• data_matching
  compares incoming data with historical records and identifies matches

• integration
  connects Google Sheets with processing logic and manages data flow

src/
    data_preparation/
    data_matching/
    integration/

docs/
    architecture.md
    workflow.md
    code-structure.md

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Technical Focus

This project focuses on:

• structuring data processing workflows
• separating data input, processing, and storage
• improving maintainability through modular design
• reducing repeated work through incremental processing
• applying automation to support operational workflows

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Project Evolution

This project is part of an iterative development process:

• Initial stage: simple automation of manual data filtering
• Intermediate stage: structured data processing with rule-based logic
• Current stage: reusable data matching system with modular architecture

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Technologies

• Python
• JSON
• Google Sheets
• Google Drive
• Google Apps Script

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Notes

• designed as a lightweight automation solution for small-scale workflows
• suitable for environments without dedicated backend infrastructure
• can be extended to database-based systems for larger-scale applications

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Related Projects

This project is part of an iterative development process:

• Multi-Source Data Processing Automation
  Introduces structured intermediate datasets and modular processing for multi-source spreadsheet workflows

• Basic Data Processing Automation
  Initial automation of manual spreadsheet processing, focusing on filtering, transformation, and data structuring

This progression reflects the transition from simple automation scripts to a reusable data matching system with incremental processing.