US 12,248,447 B2
Data quality evaluation system
Kasey Eunice, Santa Rosa, CA (US); and Roman Kisin, San Jose, CA (US)
Assigned to CollectiveHealth, Inc., San Mateo, CA (US)
Filed by CollectiveHealth, Inc., San Mateo, CA (US)
Filed on Dec. 8, 2022, as Appl. No. 18/077,867.
Prior Publication US 2024/0193137 A1, Jun. 13, 2024
Int. Cl. G06F 7/00 (2006.01); G06F 16/215 (2019.01); G06Q 40/08 (2012.01)
CPC G06F 16/215 (2019.01) [G06Q 40/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
determining, by one or more processors, and using a data quality evaluation system, an expected amount of data received or processed by a data processing system, wherein:
the data processing system generates output based at least in part on instances of the data received from one or more sources, and
the data quality evaluation system determines the expected amount of the data based at least in part on validation data received from one or more validation sources different from the one or more sources;
determining, by the one or more processors, and using the data quality evaluation system, that an actual amount of the data received or processed by the data processing system differs from the expected amount by more than a threshold degree;
detecting, by the one or more processors, using the data quality evaluation system, and based on determining that the actual amount differs from the expected amount by more than the threshold degree, a data quality issue associated with the data received or processed by the data processing system, wherein the data quality issue impacts quality of the output generated by the data processing system;
generating, by the one or more processors, and using the data quality evaluation system, data quality results associated with the data quality issue; and
generating, by the one or more processors, and using the data quality evaluation system based at least in part on the data quality results, at least one of a data quality scorecard or an anomaly notification.