Often, existing product data can differ from the format an eCommerce website requires it to be uploaded in. The destination template might require a format that differs from a company’s catalog format.
For instance, the dimensions of a product might be mentioned in cms, but the ecommerce site might require it in inches. Further complications might include Length, Width and Height in 3 separate columns vs. single column with a separator such as 'X'. Other specifications, such as color, weight, and more, might also differ in format. Colors like ‘obsidian’, or ‘charcoal’ might have to be mapped to a standard color, such as Black. In other cases, the data might need to be formatted differently in terms of how it's arranged– instead of the title having the unit, for example Rating (in Volts), the destination template might require each value to be accompanied with the unit, e.g., Rating: 220 V. To convert all these into the required formats on a large scale is a mammoth task that, if handled manually, can take up valuable time, and lead to errors.
Automated mapping can be quite useful in such a situation. Models trained on product data use semantic context to accurately perform data mapping, classification, and normalization. dataX Bridge can ingest data from multiple sources and seamlessly convert to a destination standard or template.
Our models are not dependent on complete templates to map or normalize data efficiently and accurately. They perform accurate functions even with a sample output file. Once data is organized into a standardized structure, it can be easily analyzed by other digital applications as well, making it ready for further automation.