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Web Data Automation Case Study
A healthcare listing website based in the US
The client wanted to grow their business by expanding into new geographies and capturing more market share. They needed to find reliable and updated data on the healthcare market, the competitors, and the potential customers in different regions.
They needed a solution that could help them gather and analyze data from various sources and provide them with actionable insights for their expansion plan.
Developing an adaptable web crawler that can effectively navigate and extract data from diverse website structures can be a complex task. Websites undergo regular updates, including changes in structure, content, and data sources. Ensuring the web crawler remains up-to-date and can adapt to these changes requires ongoing maintenance and monitoring.
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In order to address the challenges and meet our client’s needs, we successfully implemented a robust data aggregation solution by leveraging
advanced automation techniques. Here’s our approach:
● Site Analysis and Navigation: Using advanced tools, we analyze competitor websites, evaluating data complexity. Our algorithms categorize
websites based on structure, content, and accessibility. We automate website navigation and generate comprehensive mapping documentation.
● Web Crawler Development, Testing, and Deployment: We specialize in developing adaptable web crawlers to handle diverse websites. Our team conducts automated testing, validating data and promptly addressing issues. The web crawler is deployed on a reliable server or cloud infrastructure for real-time data collection. Parallel crawlers scrape data from multiple competitor websites.
● Data Integration and Cleansing: We implement automated workflows to seamlessly integrate and cleanse collected data. Output is delivered monthly and the project is completed within 2-3 months.
● Quality Checks: Our robust two-tier data quality assurance process ensures accuracy and compliance. Our dedicated Process Excellence
Group (PEG) uses automated algorithms to meticulously assess data accuracy. Non-compliant batches that meet the Service Level Agreement
(SLA) are delivered. Automated rework rectifies defects before resubmitting batches for quality assurance.
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