A commercial process with a dependent variable can be associated with a set of independent variables. The commercial process can continuously provide data collection opportunities. An intervention is designed using a model to predict the dependent outcome. The actual outcome of the intervention can be determined within the window of utility for these data. One objective is to improve intervention outcomes with prediction. Purely random outcomes (no model prediction) and outcomes resulting from the intervention (model operations) are aggregated into separate files—a sequence of control model data files and a sequence of model data files of operational data. These model data files and control model data files are used to analyze model performance and to react automatically when identified conditions warrant.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An operational intelligence platform, comprising: a first expert system configured to use a model in connection with providing analytics data to one or more interested parties; and a second expert system configured to periodically test and validate the model based on changes in one or more independent variables and their relation to one or more dependent variables. 2. The platform of claim 1, wherein the second expert system is configured to obtain an independent source of random data to account for a modeler effect during the testing and validation of the model. 3. The platform of claim 1, wherein the second expert system is configured to test and validate the model at least on a daily basis. 4. The platform of claim 1, wherein the second expert system applies predictive models to ongoing commerce that is occurring in the first expert system. 5. The platform of claim 4, wherein the first expert system generates and provides scoring code to the second expert system while the second expert system is used to manage a Goodness-of-Fit (GOF) for models built in the first expert system. 6. The platform of claim 5, wherein the second expert system provides a model data file to the first expert system, wherein the model data file is used to recalibrate or rebuild the first expert system based on how close the scoring code is to predicted values of constancy. 7. A method, comprising: deploying a first expert system to use a model in connection with providing analytics data to one or more interested parties; and deploying a second expert system to periodically test and validate the model based on changes in one or more independent variables and their relation to one or more dependent variables. 8. The method of claim 7, wherein the second expert system is configured to obtain an independent source of random data to account for a modeler effect during the testing and validation of the model. 9. The method of claim 7, wherein the second expert system is configured to test and validate the model at least on a daily basis. 10. The method of claim 7, wherein the second expert system applies predictive models to ongoing commerce that is occurring in the first expert system. 11. The method of claim 10, wherein the first expert system generates and provides scoring code to the second expert system while the second expert system is used to manage a Goodness-of-Fit (GOF) for models built in the first expert system. 12. The method of claim 11, wherein the second expert system provides a model data file to the first expert system, wherein the model data file is used to recalibrate or rebuild the first expert system based on how close the scoring code is to predicted values of constancy. 13. A learning system, comprising: an expert system that periodically tests and validate a model used by another expert system in connection with providing analytics data to one or more interested parties, wherein the expert system periodically tests and validates the model based on changes in one or more independent variables and their relation to one or more dependent variables. 14. The learning system of claim 13, wherein the second expert system is configured to obtain an independent source of random data to account for a modeler effect during the testing and validation of the model. 15. The learning system of claim 13, wherein the second expert system is configured to test and validate the model at least on a daily basis. 16. The learning system of claim 13, wherein the second expert system applies predictive models to ongoing commerce that is occurring in the first expert system. 17. The learning system of claim 16, wherein the first expert system generates and provides scoring code to the second expert system while the second expert system is used to manage a Goodness-of-Fit (GOF) for models built in the first expert system. 18. The learning system of claim 17, wherein the second expert system provides a model data file to the first expert system, wherein the model data file is used to recalibrate or rebuild the first expert system based on how close the scoring code is to predicted values of constancy.