Method and system for providing data quality as a service

Abstract

An enterprise level Application Programming Interface (API) driven service to integrate and automate data quality processes within organizations is provided. The service has capabilities to ingest data from heterogenous data sources and provide data quality metrics irrespective of data domain type. A subscribing application can write Data Quality (DQ) checks as code using templates of a customized rule engine and generate metrics based on pre-defined schedules. Analytics and visualization and reporting capabilities of the service empower stakeholders such as data managers, application developers, and risk assessors to obtain actionable insights into data quality. Stakeholders can use service-generated DQ measures, such as accuracy, completeness, and timeliness, to identify data quality concerns and proactively implement remediation actions to reduce data use risk. Artificial Intelligence (AI)/Machine Learning (ML) modeling techniques are used to analyze data quality, identify data anomalies, measure data quality metrics, and generate an overall data quality score.