Abstract: A system may store data from data channels in a first data storage, where the data from each data channel corresponds to a channel-specific structure. The system may transform the data from a channel-specific structure into a common structure to obtain transformed data and store the transformed data in a second data storage. The system may store, in a data model, a channel-specific session and one or more channel-specific threads that are in association with the transformed data. Further, a channel-specific session may correspond to metadata representing a grouping of channel-specific threads. The system may generate, in accordance with a stored configuration and via machine learning models, insights on the transformed data stored and store the insights in the data model, where an insight may be associated with the channel-specific session and the channel-specific threads of the transformed data. The system may then execute actions based on the insight generation.