Challenges
The client is a leading asset management company that requires large volumes of financial data to power their AI platform, providing data-driven insights to customers, identifying investment opportunities, and managing risk. While their in-house team can scrape traditional sources for web data, they lack the required expertise when it comes to collecting data from obscure, hard copy formats such as monthly financial reports.
PDF files pose a different set of challenges altogether, which makes them particularly difficult to scrape. For example, PDF files are not designed for easy parsing. Unlike web pages (which are essentially structured lines of HTML codes), PDFs are essentially images that contain text. This adds an extra layer of complexity for data scraping. Additionally, PDF files can have varying layouts, making it difficult to extract data consistently. A PDF file might have multiple columns, images, tables, or headers and footers, which makes extracting accurate data even more challenging.
Furthermore, some PDF files may have password protection or other security measures in place to prevent scraping. These security measures can make it even more challenging to scrape data from the PDF file. Finally, some PDF files can be very large, making it time-consuming to extract data from them, which can be especially challenging if the data you want to extract is located in a specific section of the PDF file.
Given these challenges, the client turned to Grepsr for help. As data extraction veterans, Grepsr’s expertise more than makes up for where the client lacks.
