Method and system for auto generating automotive data quality marker

A method facilitating auto generating automotive data quality marker, said method comprising:

• receiving by a data quality module coupled to a processor, a set of data packets from a plurality of distributed storage systems associated with a plurality of automotive data sources, wherein the set of data packets is received at a specific frequency and in specific quantity/numbers;
• extracting, by an ML engine coupled to the processor, a first set of attributes from the set of data packets received from a plurality of first computing devices associated with the plurality of automotive data sources, wherein the first set of attributes pertain to automotive numeric data set;
• extracting, by the ML engine coupled to the processor, a second set of attributes from the set of data packets received from the first computing devices associated with the plurality of automotive data sources, wherein the second set of attributes pertain to automotive image data set;
• and
• based on the first and the second set of attributes extracted, determining, by the ML engine, an average score pertaining to quality of data in the set of data packets received, wherein the average score comprises the automotive data quality marker (ADQM);
• and
• classifying, based on the ADQM, the set of data packets related to the one or more automobiles among one or more pre-defined categories for quality marking.

A non-transitory computer readable medium comprising machine executable instructions that are executable by a processor to:

• receive by a data quality module coupled to the processor, a set of data packets from a plurality of distributed storage systems associated with a plurality of automotive data sources, wherein the set of data packets is received at a specific frequency and in specific quantity/numbers;
• extract by an ML engine coupled to the processor, a first set of attributes from the set of data packets received from a plurality of first computing devices associated with the plurality of automotive data sources, wherein the first set of attributes pertain to automotive numeric data set;
• extract, by the ML engine coupled to the processor, a second set of attributes from the set of data packets received from the first computing devices associated with the plurality of automotive data sources, wherein the second set of attributes pertain to automotive image data set;
• and
• based on the first and the second set of attributes extracted, determine, by the ML engine, an average score pertaining to quality of data in the set of data packets received, wherein the average score comprises the automotive data quality marker (ADQM);
• classify, based on the ADQM, the set of data packets related to the one or more automobiles among one or more pre-defined categories for quality marking.