1. A method for managing data using one or more processors, included in one or more network computers, to execute a modeling platform server that performs actions, comprising: employing a source data server to provide a principal data set and another data set, wherein the principal data set includes a plurality of principal objects and the other data set includes a plurality of other objects; instantiating a first engine to perform actions including: associating each of the plurality of principal objects with one or more other objects that are selected from the plurality of other objects, wherein the one or more other objects are selected as potential matches to one or more of the principal objects; selecting one or more principal objects of the plurality of principal objects based on one or more heuristics and the one or more selected principal objects association with selected other objects; and labeling one or more of the selected other objects as either a match or a non-match of their associated principal object; and instantiating a second engine to perform actions, including: training a ranker to identify a selected other object that is top-ranked in similarity to its association with the one or more principal objects based on the labeling of the one or more selected other objects; and employing the trained ranker to indicate a match among the plurality of principal objects and the plurality of other objects; and instantiating a third engine to selectively filter the plurality of other objects to rank each matched other object higher than other objects associated with a same principal object, wherein geolocation information from a GPS device is employed to localize a display of the ranked other objects including one or more of time zone or language, and wherein a hardware security module is used to provide tamper resistant safeguarding of cryptographic information.
1. A method for managing data using one or more processors, included in one or more network computers, to execute a modeling platform server that performs actions, comprising: employing a source data server to provide a principal data set and another data set, wherein the principal data set includes a plurality of principal objects and the other data set includes a plurality of other objects; instantiating a first engine to perform actions including: associating each of the plurality of principal objects with one or more other objects that are selected from the plurality of other objects, wherein the one or more other objects are selected as potential matches to one or more of the principal objects; selecting one or more principal objects of the plurality of principal objects based on one or more heuristics and the one or more selected principal objects association with selected other objects; and labeling one or more of the selected other objects as either a match or a non-match of their associated principal object; and instantiating a second engine to perform actions, including: training a ranker to identify a selected other object that is top-ranked in similarity to its association with the one or more principal objects based on the labeling of the one or more selected other objects; and employing the trained ranker to indicate a match among the plurality of principal objects and the plurality of other objects; and instantiating a third engine to selectively filter the plurality of other objects to rank each matched other object higher than other objects associated with a same principal object, wherein geolocation information from a GPS device is employed to localize a display of the ranked other objects including one or more of time zone or language, and wherein a hardware security module is used to provide tamper resistant safeguarding of cryptographic information. 4. The method of claim 1 , further comprising: grouping the other objects in the other data set with potential duplicate objects in the other data set; labeling one or more duplicate objects included in the other data set as matches or non-matches; training another ranker to rank potential duplicate objects based on their similarity to their associated principal duplicate object; employing the other ranker to rank potential duplicate objects based on their similarity to their associated principal duplicate object, wherein the top-ranked potential duplicate object is indicated as a duplicate object; training another secondary classifier to discover duplicate objects that are falsely indicated by the other ranker; discovering duplicate model objects in the other data set based on the other ranker and the other secondary classifier; and removing the duplicate model objects from the plurality of other objects.