At the beginning of the project, we analyzed the company’s existing data handling process. The business was managing large volumes of information using Google Sheets, which caused system crashes, slow loading, and incorrect formula calculations. These issues affected daily operations and data reliability. Based on this, we defined the need for a proper database-based migration system.
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Smart Data Migration for Modern Businesses
Industry
Manufacturing
Region
USA
Project Size
Non-Disclosable
Overview
This project performs secure data migration for CT Sounds, an online car-audio product company. The system transfers product, customer, and order data from existing storage into a structured database without data loss. It includes data extraction, cleaning, validation, and loading processes. The migration improves data accuracy, inventory tracking, and order handling. After implementation, the company can manage operations efficiently and support future digital upgrades and automation.
Core Feature
Secure, scalable data migration with automated ETL, validation, batch processing, and backup recovery.
- Large-Scale Data Migration Support
- Data Integrity Verification
- Data Cleaning & Duplicate Removal
- Error Detection and Logging System
- Secure Data Handling
- Scalable Architecture for Future Expansion
Technologies
Built with Industry-Leading Tools
BigQuery
Google Sheets
SQL
GCS
AI/NLP
Outcomes
Successful migration of large business data without loss
Improved data accuracy and consistency
Reduced system crashes and manual errors
Organized and structured database management
Improved overall system performance
Easier future system upgrades and integrations
The Challenges
01
Spreadsheet Scalability Limitations
Google Sheets frequently crashed when handling large volumes of data, making it difficult to manage and process records efficiently.
Inaccurate Formula Results
Formulas were not producing accurate results on large datasets.
02
Fragmented Data Calculations
To calculate individual columns, separate sheets had to be created to prevent system crashes.
03
Lack of Structured Database
Customer data was scattered across spreadsheets due to the absence of a structured database system.
04
Solution by Datumquest
Implemented structured database storage to replace Google Sheets and handle large datasets reliably.
Designed an ETL pipeline to extract, clean, transform, and migrate business data safely.
Processed records in Partitions to improve performance and Reduce costing by only processing limited data.
Established backup and recovery system to prevent data loss and protect historical data.
Outcomes
Successful migration of large business data without loss
Improved data accuracy and consistency
Reduced system crashes and manual errors
Organized and structured database management
Improved overall system performance
Easier future system upgrades and integrations
Project Timeline
A structured 8-week journey from concept to deployment
Discovery
Research & Planning
2 weeks
Development
Core System Build
8 weeks
Integration
AI Model Training
4 weeks
Testing
QA & Optimization
3 weeks
Launch
Deployment & Rollout
1 week
Requirement Analysis
Key Activities
- Identified Google Sheets performance issues
- Observed data crashes and incorrect formula results
- Defined migration goals and system requirements
Data Analysis
We examined the collected dataset to identify data quality issues and prepared it for migration. After detection, incorrect and inconsistent records were corrected to ensure accurate and reliable data storage.
Key Activities
- Duplicate record detection and removal
- Handling missing or blank values
- Standardizing date, price, and product code formats
- Correcting incorrect entries and mismatched fields
Data Modelling
A structured database schema was designed with proper tables and relationships for storing business data efficiently. This replaced unorganized spreadsheet storage and improved data management.
Key Activities
- Created tables (products, customers, orders, inventory)
- Defined primary and foreign keys
- Planned relationships between records
- Improved data structure and scalability
ETL Development
We developed scripts to perform the ETL process, which extracted data from the source files, transformed it into the required format, and loaded it into the database. Automation reduced manual work and improved accuracy. The process ensured that the migrated data matched the required database structure.
Key Activities
- Developed migration scripts
- Applied transformations during migration
- Converted data formats
Data Validation, & Testing
After migration, the data was verified to ensure that all formulas were working correctly and records were transferred correctly and accurately. Backup mechanisms were implemented to protect the information from loss, and the system was tested to confirm fast retrieval and stable performance.
Key Activities
- Verified record counts and sample data accuracy
- Ensured zero data loss after migration
- Created backup copies for recovery
How Datumquest is Making an Impact
Michael Reynolds
Head of Customer Experience
Datumquest helped us transform thousands of call recordings into searchable business intelligence. Their AI-powered call analysis system made it incredibly easy to identify recurring customer issues, improve support quality, and reduce response times.”
Robert Kim
Director of Customer Success
The Smart Call Analysis & Search System developed by Datumquest significantly improved our operational visibility. Instead of manually listening to hours of call recordings, we can now instantly search conversations and uncover key insights within seconds.
James Carter
Product Operations Manager
What impressed us most was how seamlessly the system integrates AI, semantic search, and automation. Our managers can quickly detect patterns in customer complaints and proactively improve the service experience.
Datumquest specializes in AI, automation, data science, and analytics, helping businesses unlock data-driven insights and streamline operations.
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