System and method for enriching and normalizing data

A method for enriching and normalizing data from a plurality of different types of data systems that are managed by different types of software applications, comprising

• extracting with a data extraction unit selected portions of data from a plurality of data sources to form extracted data, wherein the plurality of data sources provides data that is generated by a plurality of different types of data systems that are managed by different types of software applications,
• storing the extracted data in a data storage unit,
• enriching the extracted data with a data enrichment unit to form cleaned data that is stored in the data storage unit, wherein the enriching of the cleaned data includes
• cleaning the extracted unit with a data cleaning unit to form cleaned data, and
• inserting the cleaned data into a common data model to normalize the cleaned data, and
• applying one or more selected artificial intelligence and machine learning (AI/ML) techniques to selected portions of the cleaned data to form machine language data, wherein the one or more selected machine learning techniques is stored in a machine language module having a plurality of predefined machine learning units,
• wherein the cleaned data includes transaction data, product data, and user data, and wherein the machine language module further includes a prediction unit for processing the transaction data and the user data and generating a prediction based on an interest in one or more selected products of a selected user, wherein the prediction unit is configured to generate a first product interest score indicative of a first interest level in the product by the selected user, a second product interest score indicative of a second interest level in the product by the selected user, a community interest score associated with a community interest in the one or more selected products, a user feature score associated with one or more primary user features of the selected product, and a product feature score indicative of one or more primary features of the selected product, and to determine therefrom a final product score indicative of the user interest in the one or more selected products, and
• ranking the final product interest scores.