Abstract: Arrangements for controlling data relations and data redundancies are presented. In some aspects, data may be received from a plurality of sources. The data may then be analyzed to determine a score or value associated with the data. Machine learning may be used to analyze the data and/or determine the score associated with the data. In some examples, a type of data may be identified. Based on the type of data, and the data, one or more connections to other data or types of data may be identified. In some examples, the data, as well as any identified connections, any determined score, and the like may be stored in a data container associated with the identified type of data. Stored data may be analyzed to determine whether one or more redundancies exist. If one or more redundancies are identified, one or more data redundancy actions may be identified and executed.

Abstract: Arrangements for forecasting data surges are presented. In some aspects, data may be received from, for instance, a computing system internal to an enterprise organization. In some examples, contextual data may be received. The contextual data may be analyzed, with the received data, to identify a score for the received data. Machine learning may be used to identify or determine the score. In some examples, additional data may be received via a plurality of data streams. The additional data may be analyzed to identify topics or trends in data. The topics or trends may be used to identify potential data surges. Machine learning may be used to analyze the data and forecast a potential data surge. In response to forecasting a potential data surge, one or more computing and/or data storage resources may be identified, configured, and deployed to accommodate the forecast potential data surge.