High Tech — Multi-Source Knowledge Content Extraction Pipeline

Overview

The purpose of this DAG is to facilitate the extraction of valuable content from diverse document sources such as Microsoft 365, Google Drive, and Confluence. It initiates a structured data ingestion pipeline that begins with the retrieval of documents from these platforms. Once the documents are ingested, they undergo a comprehensive analysis to extract key information, which is then normalized to ensure consistency across different formats. Quality control measures are implemented throughout t

The purpose of this DAG is to facilitate the extraction of valuable content from diverse document sources such as Microsoft 365, Google Drive, and Confluence. It initiates a structured data ingestion pipeline that begins with the retrieval of documents from these platforms. Once the documents are ingested, they undergo a comprehensive analysis to extract key information, which is then normalized to ensure consistency across different formats. Quality control measures are implemented throughout the process to validate the integrity and accuracy of the data extracted. This includes automated checks for completeness and relevance, ensuring that only high-quality information is cataloged. The final outputs of this DAG are published to the KM2 knowledge portal, which enhances the searchability and accessibility of knowledge within the organization. Key performance indicators (KPIs) such as extraction accuracy, processing time, and user engagement metrics are monitored to assess the effectiveness of the pipeline. By streamlining content extraction and improving data quality, this solution delivers significant business value, enabling teams to leverage knowledge more effectively and make informed decisions in the high-tech industry.

Part of the Data & Model Catalog solution for the High Tech industry.

Use cases

• Enhanced knowledge accessibility for informed decision-making
• Improved data quality through rigorous quality controls
• Streamlined content extraction process saves time
• Increased user engagement with searchable knowledge base
• Supports innovation by providing relevant insights quickly