Retail — E-Commerce Data Normalization Pipeline

Overview

The primary purpose of this DAG is to normalize ingested data from various sources, ensuring high quality and compliance with predefined standards. In the retail industry, where data integrity is crucial for decision-making, this pipeline plays a vital role in transforming raw data into a structured format suitable for analysis. The main data sources include diverse corpora such as product descriptions, customer reviews, and sales data. The ingestion pipeline begins with data extraction from the

The primary purpose of this DAG is to normalize ingested data from various sources, ensuring high quality and compliance with predefined standards. In the retail industry, where data integrity is crucial for decision-making, this pipeline plays a vital role in transforming raw data into a structured format suitable for analysis. The main data sources include diverse corpora such as product descriptions, customer reviews, and sales data. The ingestion pipeline begins with data extraction from these sources, followed by a series of processing steps designed to cleanse and standardize the data. During the processing phase, quality control measures are applied, including validation checks and adherence to formatting rules, to ensure that the data meets the required criteria. Outputs from this DAG consist of normalized datasets that are ready for further analysis and integration into knowledge portals and ontologies. Key performance indicators (KPIs) for monitoring the effectiveness of this pipeline include the compliance rate of the processed data and the average processing time, with alerts generated for any instances of non-compliance. By ensuring high-quality data, this DAG provides significant business value, enabling retailers to make informed decisions based on accurate insights.

Part of the Knowledge Portal & Ontologies solution for the Retail industry.

Use cases

• Improved decision-making through high-quality data insights
• Enhanced customer experience with accurate product information
• Increased operational efficiency by automating data normalization
• Reduced risk of errors in data-driven strategies
• Stronger compliance with industry standards and regulations