Artificial intelligence system for data quality resolution

A method for validating and resolving data quality, comprising:

• importing, by a data quality system and from a data source, an incoming data set for data quality analysis;
• obtaining, by the data quality system, data quality validation information from one or more data repositories that form a data quality knowledge base,
• wherein the data quality validation information comprises documented rules that define requirements and an expected format for the incoming data set, historical data sets that have been evaluated for data quality issues, and historical data quality results related to the historical data sets;
• using, by the data quality system, one or more machine learning models to evaluate whether the incoming data set has one or more anomalies based on the data quality validation information;
• generating, by the data quality system, data quality results for the incoming data set based on an output from the one or more machine learning models,
• wherein generating the data quality results for the incoming data set includes using the one or more machine learning models to identify one or more fixes to be applied to the incoming data set based on the data quality results for the incoming data set including an indication that the incoming data set has one or more anomalies, and
• wherein the data quality results for the incoming data set are associated with a confidence level that indicates an accuracy of the data quality results for the incoming data set;
• and
• updating, by the data quality system and based on the confidence level, the one or more data repositories that form the data quality knowledge base in accordance with the data quality results for the incoming data set.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a data quality system, cause the data quality system to:
• import, from a data source, an incoming data set for data quality analysis;
• obtain data quality validation information from one or more data repositories that form a data quality knowledge base,
• wherein the data quality validation information includes documented rules that define requirements and an expected format for the incoming data set, historical data sets that have been evaluated for data quality issues, and historical data quality results related to the historical data sets;
• use one or more machine learning models to evaluate whether the incoming data set has one or more anomalies based on the data quality validation information;
• generate data quality results for the incoming data set based on an output from the one or more machine learning models,
• wherein the data quality results for the incoming data set include an indication that the incoming data set has been validated based on one or more of the output from the one or more machine learning models or a user of a client device indicating that the incoming data set does not contain any anomalies, and
• wherein the data quality results for the incoming data set are associated with a confidence level that indicates an accuracy of the data quality results for the incoming data set;
• and
• update the one or more data repositories that form the data quality knowledge base in accordance with the data quality results for the incoming data set.