Overview
A leading U.S. manufacturer and distributor of HVAC and boiler equipment needed to centralize and standardize their model number and product data.
Their product information was scattered across multiple manufacturer portals, third-party databases, and outdated archives making it difficult for teams to find reliable details for customer support and procurement.
Their product information was scattered across multiple manufacturer portals, third-party databases, and outdated archives making it difficult for teams to find reliable details for customer support and procurement.
- Business Challenge
- Our Solution
The client struggled with fragmented, inconsistent, and manually maintained data sources.
Key challenges included:
- Difficulty retrieving accurate model numbers and product specifications quickly
- Manual data entry errors and redundancy
- Delayed customer support due to unreliable product information
- Lack of a structured, searchable repository for technical data
Aquarient Technologies designed and deployed an automated DataOps pipeline to extract, clean, and organize product model data across multiple digital sources.
Solution Highlights:
- Web Scraping Automation: Extracted model numbers and product details from manufacturer and third-party sites such as Energy Star, ManualsLib, and Archive.org.
- Data Structuring in Excel: Standardized and categorized model data into searchable, well-structured sheets.
- Validation & Quality Checks: Applied automated validation logic to ensure accuracy and eliminate duplicates.
- Continuous Support: Implemented ongoing updates and validation cycles for data freshness.
Why It Matters
This DataOps-driven automation allowed the client to:
- Enhance customer service through instant data access
- Reduce human dependency in data collection and verification
- Build a scalable foundation for future analytics and automation initiatives
Results & Business Impact
| Outcome | Impact |
|---|---|
| 80% Faster Retrieval | Model numbers and details instantly accessible via centralized data sheets |
| Improved Accuracy | Automated validation eliminated manual entry errors |
| Centralized Repository | Single source of truth for all product and technical data |
| Streamlined Operations | Faster response times for customer support and procurement |
Technologies Used
- Python (Requests, BeautifulSoup, Pandas)
- Excel-based Data Structuring
- Automated Validation Frameworks
- Cloud-based Data Repository (Excel/SharePoint Integration)
