Automated data integration, reconciliation, and self healing using machine learning

One or more computing devices comprising:

• processors;
• and
• memory;
• the one or more computing devices configured, via computer-executable instructions, to perform operations for automated data reconciliation and self-healing using machine learning, the operations comprising:
• receiving an indication of a number of reconciliation iterations to perform, wherein the number of reconciliation iterations is two or more, wherein the number of reconciliation iterations is generated by a machine learning model, and wherein the number of reconciliation iterations changes dynamically based on prior reconciliation results;
• for each reconciliation iteration of the number of reconciliation iterations:
• performing reconciliation comprising checking consistency of integrated data in a target system against source data in a source system;
• and
• when a reconciliation error is found, performing a self-healing operation to correct the integrated data in the target system;
• and
• outputting reconciliation results from performing the reconciliations over the number of reconciliation iterations, wherein outputting the reconciliation results comprises providing the reconciliation results to the machine learning model;
• wherein the machine learning model is retrained using the reconciliation results, and wherein the machine learning model dynamically adjusts a number of reconciliation iterations to perform for future reconciliation based on the reconciliation results, comprising increasing the number of reconciliation iterations to perform for future reconciliation when reconciliation errors are found during each reconciliation iteration;
• and
• wherein the machine learning model is trained to predict a number of reconciliation iterations that will be needed to ensure consistency of the integrated data in relation to the source data.

One or more computer-readable storage media storing computer-executable instructions for execution on one or more computing devices to perform operations for automated data reconciliation and self-healing using machine learning, the operations comprising:

• receiving an indication of a number of reconciliation iterations to perform, wherein the number of reconciliation iterations is two or more, wherein the number of reconciliation iterations is generated by a machine learning model, and wherein the number of reconciliation iterations changes dynamically based on prior reconciliation results;
• for each reconciliation iteration of the number of reconciliation iterations:
• performing reconciliation comprising checking consistency of integrated data in a target system against source data in a source system;
• and
• when a reconciliation error is found, performing a self-healing operation to correct the integrated data in the target system;
• and
• providing reconciliation results to the machine learning model, wherein the machine learning model is retrained using the reconciliation results, and wherein the machine learning model dynamically adjusts a number of reconciliation iterations to perform for future reconciliation based on the reconciliation results, comprising increasing the number of reconciliation iterations to perform for future reconciliation when reconciliation errors are found during each reconciliation iteration;
• wherein the machine learning model is trained to predict a number of reconciliation iterations that will be needed to ensure consistency of the integrated data in relation to the source data.