The objective was to add more context to the existing data stored in a Salesforce instance by enhancing it with additional information queried from multiple external systems. The idea was to collect the maximum scope of information within Salesforce for further machine learning analysis without any major prefiltering. The APIs of external systems were mostly documented in RAML.
To enable the business in their exploration of contextual data from third party systems that could improve the prediction power of their custom machine learning model we decided to build an automated load tool that would enable the client to easily add new data feeds. Our aim was to simplify the whole data model creation and data load process and treat Salesforce Platform as a general purpose data store. Putting MuleSoft integration platform into action we created a universal mechanism that automatically generates a custom Salesforce data model on the basis of RAML specification of the API and then populates it in a full snapshot or incremental fashion.
Our proprietary API connectivity tool enabled us to turn Salesforce into a general purpose data store and to quickly populate it with contextual data from various third party data sources. The automated process of Salesforce schema creation and data loading dramatically improved the business ability to try and explore new data sources for their predictive machine learning models.