Energy — Named Entity Extraction for Unstructured Data Enrichment

Overview

The primary purpose of this DAG is to automate the extraction of named entities from unstructured data sources, specifically targeting documents relevant to the energy sector. By utilizing advanced Natural Language Processing (NLP) techniques, the DAG processes various data inputs such as reports, emails, and other textual documents to identify and normalize entities like company names, locations, and energy types. The ingestion pipeline begins with the collection of unstructured data, followed

The primary purpose of this DAG is to automate the extraction of named entities from unstructured data sources, specifically targeting documents relevant to the energy sector. By utilizing advanced Natural Language Processing (NLP) techniques, the DAG processes various data inputs such as reports, emails, and other textual documents to identify and normalize entities like company names, locations, and energy types. The ingestion pipeline begins with the collection of unstructured data, followed by the application of NLP algorithms to extract relevant entities. These entities are then normalized to ensure consistency and accuracy before being integrated into the data warehouse for further analysis. Quality controls are implemented throughout the process, including monitoring the extraction rate and processing time as key performance indicators (KPIs). In the event of a failure, a recovery process is initiated to ensure data integrity and continuity. The outputs of this DAG include enriched datasets that can be utilized for enhanced fraud detection and anomaly analytics. By improving the quality and accessibility of data, this DAG delivers significant business value, enabling energy companies to make informed decisions and mitigate risks associated with fraudulent activities.

Part of the Fraud & Anomaly Analytics solution for the Energy industry.

Use cases

• Enhanced data quality for improved fraud detection accuracy
• Reduced manual effort in data processing and analysis
• Faster decision-making through timely data availability
• Increased operational efficiency in handling unstructured data
• Mitigation of risks associated with fraudulent activities