Patent ID: 11922357
Assignee: CHARTER COMMUNICATIONS OPERATING, LLC
Field: IT methods for management (Electrical engineering)
Classification: CPC G  H | IPC G  H

Claim 0:
1. A method for managing data quality anomalies in an enterprise system, the method comprising:
accepting, by one or more network computing device, connections through data ingestion circuitry to receive a plurality of data sets for analysis in the enterprise system;
generating, by the one or more network computing device, integrated data sets by executing natural language processing (NLP) tasks on at least one of ingested text and voice data in the data sets, wherein the NLP tasks implement one or more of statistical NLP and a Natural Language Toolkit (NLKT) library;
identifying, by one or more network computing device implementing a data quality management (DQM) framework, control points for monitoring the integrated data sets in the enterprise system;
tracking, by the one or more network computing device, data quality metrics using at least one threshold for each of the identified control points;
determining, by the one or more network computing device, whether a data quality anomaly is detected;
classifying, by the one or more network computing device, a review priority in response to determining that a data quality anomaly is detected;
analyzing, by the one or more network computing device, feedback for one of the identified control points associated with the detected data quality anomaly to identify additional problems impacting business; and
in response to identifying additional problems impacting business:
automatically adjusting, by the at least one network computing device, the at least one threshold for the one of the identified control points associated with the detected data quality anomaly to capture the identified additional problems, wherein the automatically adjusting is performed using machine learning; and
feeding, by the at least one network computing device, the adjusted at least one threshold back to track the data quality metrics for the one of the identified control points.