US 12,423,281 B2
System and method for determining data quality during data processing
Vivekanand Apuri, Telangana (IN); and Naresh Dolani, Maharashtra (IN)
Assigned to BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed by BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed on Sep. 14, 2023, as Appl. No. 18/368,334.
Prior Publication US 2025/0094394 A1, Mar. 20, 2025
Int. Cl. G06F 16/00 (2019.01); G06F 16/215 (2019.01); G06F 16/2457 (2019.01)
CPC G06F 16/215 (2019.01) [G06F 16/24578 (2019.01)] 17 Claims
OG exemplary drawing
 
1. A system for determining data quality during data processing, the system comprising:
at least one non-transitory storage device containing instructions; and
at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device, upon execution of the instructions, is configured to:
receive data quality rule information relating to a plurality of data quality rules executed on a network, wherein the data quality rule information comprises at least one data quality metric for each of the plurality of data quality rules, and wherein the plurality of data quality rules comprises at least a first data quality rule and a second data quality rule, wherein the plurality of data quality rules comprises determining data conflicts from different sources and data uniqueness;
compare one or more of the at least one data quality metric for each of the plurality of data quality rules, wherein comparing the one or more of the at least one data quality metric comprises comparing one or more common data quality metrics, wherein the at least one data quality metric comprises processing power utilized during execution, data quality rule domain, and data quality rule execution during a set interval, wherein the one or more common data quality metrics comprise a data quality metric type shared by at least a plurality of the plurality of data quality rules, wherein the one or more common data quality metrics comprise a weighting based on a predetermined hierarchy of data quality metrics and a weight input from an end-point device;
determine a data quality rule ranking for each of the plurality of data quality rules based on the comparison of one or more of the at least one data quality metric using a learn to rank machine learning model;
update the data quality rule ranking based on a predefined update interval and a query received from an end-point device associated with the network;
receive one or more processes to be executed on the network; and
determine one or more executed data quality rules of the plurality of data quality rules to be executed on data associated with the one or more processes based on the data quality rule ranking.