Abstract: A method is provided for analyzing energy or environmental factors relative to energy. One or more processors of a data management system receives a plurality of signals relating to a plurality of streams of data. The signals directed to at least one of: energy physical assets, energy related environment factors, energy availability, energy usage and energy source. A data transformer is used to transform the data to item and attribute characterizations. The data transformer uses a processor to organize the first, second and third streams of data according to their associated items and attributes. One or more analytic engines are used to receive the items with their attributes from the data management system and provide multi-dimensional analysis relative to a scale of study for at least one business intelligence, where multi-dimensional analysis is defined as analysis that compares, calculates, correlates, or operates on items and/or attributes from at least two distinct dimensions.

Abstract: A system provides quantification and mapping of cultural characteristics. An analytic engine collects and organizes cultural attribute information by binary score and by cultural tendency. A cultural score engine coupled to the analytic engine receives cultural attribute information by category and scores the cultural attribute data for each cultural attribute to create scored data. A map engine receives the scored data and maps the cultural attribute data to a graphic output. An assessment engine quantifies areas, organization priorities, or cultural tendencies associated with each graphic output. A comparison engine compares a plurality of graphic outputs in a single comparison graphic output of cultural tendencies.

Abstract: A method is for analyzing business intelligence applied to at least one of physical assets and environment factors. A computing system is used that includes one or more processors of a data management system for at least one stream of data selected from at least one of physical assets and environment factors. A data transformer is used to transform data from the at least one stream of data to their item and attribute characterizations. One or more analytic engines are used to receive the items with their attributes from the data management system and provide multi-dimensional analysis relative to a scale of study for at least one physical asset and environmental factor. Multi-dimensional analysis is defined as analysis that compares, calculates, correlates, or operates on items and/or attributes from at least two distinct dimensions.

Abstract: A method is provided for quantification and mapping of cultural characteristics. Using an analytic engine, cultural attribute information is collected and organized by assigning binary scores to cultural tendencies. Using a cultural score engine coupled to the analytic engine, cultural attribute data is scored for each cultural attribute category to create scored data. Scored data is mapped to a graphic output using a map engine. Quantification of areas, organization priorities, or cultural tendencies associated with each graphic output is performed with an assessment engine. Comparing multiple cultures or scenarios affecting cultural attributes is executed by a comparison engine that compares multiple cultural graphic outputs in a single comparison graphic output of cultural tendencies.

Abstract: A system is provided for qualifying and analyzing data for at least one business intelligence. A platform receives source data. A data management system transforms raw data and stores it. An analytic engine is included. In operation the data management system receives first, second, and third streams of source data. The first stream is client source data, the second stream is public source data and the third stream is acquired by the data management system. The data management system organizes the first, second and third streams of data into items and their attributes. The analytic engine receives the items with their attributes from the data management system and applies logic to provide multi-dimensional analysis relative to a scale for at least one business intelligence.

Abstract: A method is provided for qualifying and analyzing business intelligence. At a first part of a data management system receives first, second and third streams of data. The first stream is client provided source data, the second is public source data and the third is data management system internal data previously collected and managed source data in the data management system. The three streams of data are organized into items and their attributes at the data management system. The source data is transformed at a data warehouse where it becomes normalized. Logic is applied to provide multi-dimensional analysis of transformed source data relative to a scale for at least one business intelligence. The data warehouse includes updated data from the multi-dimensional analysis. A user interface communicates with the data management system to create statistical information that illustrates impact over time and value.

Abstract: A method is provided for qualifying and analyzing business intelligence. At a first part of a data management system receives first, second and third streams of data. The first stream is client provided source data, the second is public source data and the third is data management system internal data previously collected and managed source data in the data management system. The three streams of data are organized into items and their attributes at the data management system. The source data is transformed at a data warehouse where it becomes normalized. Logic is applied to provide multi-dimensional analysis of transformed source data relative to a scale for at least one business intelligence. The data warehouse includes updated data from the multi-dimensional analysis. A user interface communicates with the data management system to create statistical information that illustrates impact over time and value.

Abstract: A method is provided for qualifying and analyzing business intelligence. At a first part of a data management system receives first, second and third streams of data. The first stream is client provided source data, the second is public source data and the third is data management system internal data previously collected and managed source data in the data management system. The three streams of data are organized into items and their attributes at the data management system. The source data is transformed at a data warehouse where it becomes normalized. Logic is applied to provide multi-dimensional analysis of transformed source data relative to a scale for at least one business intelligence. The data warehouse includes updated data from the multi-dimensional analysis. A user interface communicates with the data management system to create statistical information that illustrates impact over time and value.