A computer implemented system for automating the generation of an analytic model includes a processor configured to process a plurality of data sets. Each data set includes values for a plurality of variables. A time-stamping module is configured to derive values for a plurality of elapsed-time variables for each data set, and the plurality of variables and plurality of elapsed-time variables are included in a plurality of model variables. A model generator is configured to create a plurality of comparison analytic models each based on a different subset of model variables. Each comparison analytic model is configured to operate on new data sets associated with current opportunities, and to output a likelihood of successfully closing each current opportunity. A model testing module is configured to select an operational analytic model from among the comparison analytic models based on a quality metric.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented system for automating the generation of an analytic model, said system comprising: a processor communicatively coupled to a database, the database configured to store a plurality of data sets, each of the data sets comprising values for a plurality of variables, said processor configured to process the data sets, said processor comprising: a time-stamping module configured to derive values for a plurality of elapsed-time variables for each data set, wherein each elapsed-time variable represents an elapsed time between a respective pair of dated events associated with the respective data set, the plurality of variables and the plurality of elapsed-time variables included in a plurality of model variables; a model generator configured to create a plurality of comparison analytic models, each comparison analytic model based on a different subset of the plurality of model variables, wherein each comparison analytic model is configured to operate on new data sets associated with current opportunities, and to output a representation of a likelihood of successfully closing each current opportunity; and a model testing module configured to select an operational analytic model from among the plurality of comparison analytic models based on a quality metric applied to each of the plurality of comparison analytic models. 2. The system according to claim 1, wherein: the model generator is further configured to create a respective plurality of comparison analytic models for each of a plurality of model types, each comparison analytic model within each model type based on a different subset of the plurality of model variables, and the model testing module is further configured to select a respective operational analytic model for each model type from among the plurality of comparison analytic models of the corresponding model type based on the quality metric applied to each of the plurality of comparison analytic models, said system further comprising a model selection module configured to select a production analytic model from among the operational analytic models of each model type based on the quality metric applied to the operational analytic models of each model type, wherein the system is configured to report the output representation of the production analytic model for each current opportunity. 3. The system according to claim 2, wherein the system is configured to apply the operational analytic models other than the selected production analytic model to the new data sets associated with the current opportunities on an ongoing basis, and to store the resulting output representations of each operational analytic model. 4. The system according to claim 3, wherein said system further comprises a model updating module configured to: receive updated data sets each indicating an actual result associated with the respective data set; and evaluate a predictive performance of the operational analytic model of each model type based on the updated data sets. 5. The system according to claim 4, wherein the model updating module is further configured to reselect the production analytic model from among the operational analytic models of each model type based on the predictive performance of the operational analytic models of each model type. 6. The system according to claim 4, wherein the model updating module is further configured to instruct the time-stamping module, the model generator, and the model testing module to regenerate the operational analytic model of at least one model type, based at least in part on the updated data sets, in response to the predictive performance of the operational analytic model of the at least one model type being less than a threshold value. 7. The system according to claim 6, wherein the model updating module is further configured to instruct the time-stamping module, the model generator, and the model testing module to regenerate the operational analytic model of the at least one model type in a stateful manner. 8. The system according to claim 6, wherein the model updating module is further configured to instruct the time-stamping module, the model generator, and the model testing module to regenerate the operational analytic model of the at least one model type in a stateless manner. 9. The system according to claim 1, wherein the model generator is further configured to create each comparison analytic model configured to output the representation of the likelihood of successfully closing each current opportunity as one of (i) a score indicating a relative likelihood that each current opportunity will be successfully closed, and (ii) a prioritized ranking of the current opportunities based on the respective likelihood that each current opportunity will be successfully closed. 10. The system according to claim 1, further comprising a converter configured to add at least one additional variable to the plurality of model variables, the converter further configured to derive values for the at least one additional variable by converting unstructured data associated with at least one data set into structured data associated with the respective data set. 11. A method for automating generation of an analytic model, said method comprising the following computer-implemented steps: accessing a plurality of data sets, each of the data sets including values for a plurality of variables; deriving values for a plurality of elapsed-time variables for each data set, wherein each elapsed-time variable represents an elapsed time between a respective pair of dated events associated with the respective data set, the plurality of variables and the plurality of elapsed-time variables included in a plurality of model variables; creating a plurality of comparison analytic models, each comparison analytic model based on a different subset of the plurality of model variables, wherein each comparison analytic model is configured to operate on new data sets associated with current opportunities, and to output a representation of a likelihood of successfully closing each current opportunity; calculating a quality metric for each of the plurality of comparison analytic models; and selecting an operational analytic model from among the plurality of comparison analytic models based on the quality metric. 12. The method according to claim 11, further comprising: creating a respective plurality of comparison analytic models for each of a plurality of model types, each comparison analytic model within each model type based on a different subset of the plurality of model variables; calculating the quality metric for each of the plurality of comparison analytic models of each model type; selecting, for each model type, an operational analytic model from among the plurality of comparison analytic models of the model type based on the quality metric; selecting a production analytic model from among the operational analytic models of each model type based on the quality metric; and reporting the output representation of the production analytic model for each current opportunity. 13. The method according to claim 12, further comprising: applying the operational analytic models other than the selected production analytic model to the new data sets associated with the current opportunities on an ongoing basis; storing the resulting output representations of each operational analytic model. 14. The method according to claim 13, further comprising: receiving updated data sets each indicating an actual result associated with the respective data set; and evaluating a predictive performance of the operational analytic model of each model type based on the updated data sets. 15. The method according to claim 14, further comprising reselecting the production analytic model from among the operational analytic models of each model type based on the predictive performance of the operational analytic models of each model type. 16. The method according to claim 14, further comprising regenerating the operational analytic model of at least one model type, based at least in part on the updated data sets, in response to the predictive performance of the operational analytic model of the at least one model type being less than a threshold value. 17. The method according to claim 16, wherein regenerating the operational analytic model of the at least one model type comprises regenerating the operational analytic model of the at least one model type in a stateful manner. 18. The method according to claim 16, wherein regenerating the operational analytic model of the at least one model type comprises regenerating the operational analytic model of the at least one model type in a stateless manner. 19. The method according to claim 11, wherein creating the plurality of comparison analytic models further comprises creating each comparison analytic model configured to output the representation of the likelihood of successfully closing each current opportunity as one of (i) a score indicating a relative likelihood that each current opportunity will be successfully closed, and (ii) a prioritized ranking of the current opportunities based on the respective likelihood that each current opportunity will be successfully closed. 20. The method according to claim 11, further comprising: adding at least one additional variable to the plurality of model variables; and deriving values for the at least one additional variable by converting unstructured data associated with at least one data set into structured data associated with the respective data set. 21. A computer-readable medium having computer-executable instructions for automating generation of an analytic model, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to: access a plurality of data sets, each of the data sets including values for a plurality of variables; derive values for a plurality of elapsed-time variables for each data set, wherein each elapsed-time variable represents an elapsed time between a respective pair of dated events associated with the respective data set, the plurality of variables and the plurality of elapsed-time variables included in a plurality of model variables; create a plurality of comparison analytic models, each comparison analytic model based on a different subset of the plurality of model variables, wherein each comparison analytic model is configured to operate on new data sets associated with current opportunities, and to output a representation of a likelihood of successfully closing each current opportunity; calculate a quality metric for each of the plurality of comparison analytic models; and select an operational analytic model from among the plurality of comparison analytic models based on the quality metric. 22. The computer-readable medium according to claim 21, wherein the computer-executable instructions further cause the at least one processor to: create a respective plurality of comparison analytic models for each of a plurality of model types, each comparison analytic model within each model type based on a different subset of the plurality of model variables; calculate the quality metric for each of the plurality of comparison analytic models of each model type; select, for each model type, an operational analytic model from among the plurality of comparison analytic models of the model type based on the quality metric; select a production analytic model from among the operational analytic models of each model type based on the quality metric; and report the output representation of the production analytic model for each current opportunity. 23. The computer-readable medium according to claim 21, wherein the computer-executable instructions further cause the at least one processor to: apply the operational analytic models other than the selected production analytic model to the new data sets associated with the current opportunities on an ongoing basis; and store the resulting output representations of each operational analytic model.