Abstract: The assessing of collaboration time includes the extraction of collaboration data from collaborators and storing the collaboration data as a dataset. Attributes for each of the collaborators is defined, and a group of collaborators is defined by filtering based on the attributes. For the dataset, collaboration time is assigned for each member of the group using the collaboration data. Data from certain activities by collaborators are mined as representative of the collaboration activities, and in combination with organizational structure data, time is allocated between people, teams, and companies for the purpose of assessing organizational productivity and effectiveness. No manual data gathering or imposition on collaborators to provided data is required. Real data for the collaborative activities are used, instead of self-reported data. This provides a more granular picture of how time is allocated to relationships and activities than could be gathered manually.

Abstract: The assessing of collaboration time includes the extraction of collaboration data from collaborators and storing the collaboration data as a dataset. Attributes for each of the collaborators is defined, and a group of collaborators is defined by filtering based on the attributes. For the dataset, collaboration time is assigned for each member of the group using the collaboration data. Data from certain activities by collaborators are mined as representative of the collaboration activities, and in combination with organizational structure data, time is allocated between people, teams, and companies for the purpose of assessing organizational productivity and effectiveness. No manual data gathering or imposition on collaborators to provided data is required. Real data for the collaborative activities are used, instead of self-reported data. This provides a more granular picture of how time is allocated to relationships and activities than could be gathered manually.

Abstract: In a method for deriving entities and metrics from collaboration data from a plurality of computing systems, collaboration data is extracted from sent mails and calendars at the plurality of computing systems of a plurality of collaborators. The collaboration data is linked to correspond to one or more entities based on an activity type and collaborator metadata of the collaboration data, linked to organizational metadata defining a structure of an organization, and linked to external entities metadata defining one or more entities outside of the organization. A library of metrics is created for the one or more entities, and the metrics are quantified. The metrics are then displayed on a display, and the metrics are analyzed metrics according to instructions received via the display.

Abstract: A data storage and analysis system for modeling complex systems and the outcome of decisions and potential alternatives. The system employs a hierarchical hybrid data model with high volumes of data organized in many dimensions, such as models of organization and business operations. A “wizard-based” data loader handles imperfect data and supports multiple servers and data sources. The structures that represent the hierarchical model for the data are defined and created as the backbone for the model using spreadsheets, multiple relational database tables, and other sources of data that may reside on one or more servers. The hierarchical hybrid data model that is built also supports the linking of many different sources of data to the model's hierarchies. Analytics generators called “microCubes™” generate answers to ‘questions’ based on the server model as they are requested.

Abstract: A data storage and analysis system for modeling complex systems and the outcome of decisions and potential alternatives. The system employs a hierarchical hybrid data model with high volumes of data organized in many dimensions, such as models of organization and business operations. A “wizard-based” data loader handles imperfect data and supports multiple servers and data sources. The structures that represent the hierarchical model for the data are defined and created as the backbone for the model using spreadsheets, multiple relational database tables, and other sources of data that may reside on one or more servers. The hierarchical hybrid data model that is built also supports the linking of many different sources of data to the model's hierarchies. Analytics generators called “microCubes™” generate answers to ‘questions’ based on the server model as they are requested.

Abstract: A data storage and analysis system for modeling complex systems and the outcome of decisions and potential alternatives. The system employs a hierarchical hybrid data model with high volumes of data organized in many dimensions, such as models of organization and business operations. A “wizard-based” data loader handles imperfect data and supports multiple servers and data sources. The structures that represent the hierarchical model for the data are defined and created as the backbone for the model using spreadsheets, multiple relational database tables, and other sources of data that may reside on one or more servers. The hierarchical hybrid data model that is built also supports the linking of many different sources of data to the model's hierarchies. Analytics generators called “microCubes™” generate answers to ‘questions’ based on the server model as they are requested.

Abstract: A data storage and analysis system for modeling complex systems and the outcome of decisions and potential alternatives. The system employs a hierarchical hybrid data model with high volumes of data organized in many dimensions, such as models of organization and business operations. A “wizard-based” data loader handles imperfect data and supports multiple servers and data sources. The structures that represent the hierarchical model for the data are defined and created as the backbone for the model using spreadsheets, multiple relational database tables, and other sources of data that may reside on one or more servers. The hierarchical hybrid data model that is built also supports the linking of many different sources of data to the model's hierarchies. Analytics generators called “microCubes™” generate answers to ‘questions’ based on the server model as they are requested.