Leveraging Data Science to Build Intelligent Document Processing Pipelines

Document processing is a core part of automation in today’s industries, including finance, healthcare, manufacturing, public policy, and many more. These industries generate and handle a massive amount of information in the form of documents, making document processing a critical aspect of their operations. However, despite the potential for automation, manual document processing remains common in many industries. This manual process can be tedious, error-prone, and time-consuming, leading to inefficiencies and delays in critical business processes. In fact, according to recent studies, over 80% of business information is still trapped in unstructured documents, making manual document processing a necessary but challenging task for many organizations. Therefore, leveraging data science techniques to automate document processing has become increasingly essential for organizations to improve their efficiency, accuracy, and productivity.

In this workshop, I’ll walk through how to leverage data science to build intelligent document processing pipelines that automate document classification, extraction, and analysis. We will also discuss the challenges of document processing, such as document variability, noise, and different formats, and how to overcome them with advanced data science techniques.