19. A non-transitory Computer-readable media having computer-executable instructions embodied thereon that when executed provide a method for discovering and validating latent relationships in data, the method comprising: determining a use-case context; determining a plurality of variables associated with the use case; based on the determined context and plurality of variables, generating a decision-tree classifier; receiving a plurality of documents from two or more record-keeping systems; wherein each document contains one or more document variables of the determined plurality of variables, and wherein the received plurality of documents comprises a set of documents; for each document in the set, applying the decision tree classifier to transform a value associated with each variable contained in the document into a categorical datatype; based on the set of documents, generating a set of textmatricies; applying latent semantic analysis to the set of textmatrices to determine a latent semantic space; specifying a threshold of similarity; for a first-document variable, from a first document, associated with the latent semantic space: determining a measure of similarity to a second-document variable, from a second document, associated with the latent semantic space; performing a comparison of the measure similarity to the threshold; and based on the comparison, determining that the measure similarity satisfies the threshold, associating the first-document variable with the second-document variable, and designating the association as a synonymy, wherein the threshold is satisfied if the measure of similarity is greater than the threshold.
19. A non-transitory Computer-readable media having computer-executable instructions embodied thereon that when executed provide a method for discovering and validating latent relationships in data, the method comprising: determining a use-case context; determining a plurality of variables associated with the use case; based on the determined context and plurality of variables, generating a decision-tree classifier; receiving a plurality of documents from two or more record-keeping systems; wherein each document contains one or more document variables of the determined plurality of variables, and wherein the received plurality of documents comprises a set of documents; for each document in the set, applying the decision tree classifier to transform a value associated with each variable contained in the document into a categorical datatype; based on the set of documents, generating a set of textmatricies; applying latent semantic analysis to the set of textmatrices to determine a latent semantic space; specifying a threshold of similarity; for a first-document variable, from a first document, associated with the latent semantic space: determining a measure of similarity to a second-document variable, from a second document, associated with the latent semantic space; performing a comparison of the measure similarity to the threshold; and based on the comparison, determining that the measure similarity satisfies the threshold, associating the first-document variable with the second-document variable, and designating the association as a synonymy, wherein the threshold is satisfied if the measure of similarity is greater than the threshold. 20. The computer-readable media of claim 19 , wherein the measure of similarity is determined using Salton's cosine.