Data Quality Engineering: Enabling Success of Data Driven Solutions
The success enabler tools supporting business organizations within an enterprise are primarily driven by the underlying AI/ML powered solutions, data & analytics platforms, and enterprise applications suite. In modern data driven economy, the dependability (quality and timeliness) of data powering these business enablers, directly influences the success or failure of the business offering and adoption. The major factors for constrained data dependability can be attributed to limiting capabilities for validation of data availability, usability, reliability, relevance, and presentability. Given the data diversity, it is also challenging to keep up with validation, continued monitoring and remediation of related dense & subjective data quality rules for critical data elements. In this session, we will share our vision of well-established quality engineering and continuous monitoring methodology powered by low code, platform agnostic test automation frameworks that will help drive the data dependability upwards.
Dinesh Rajput is a Manager in KPMG’s Quality Engineering practice. He has over 15 years of experience delivering data quality and enrichment solutions within the data platform and analytics domain. Dinesh has spent last several years supporting and providing consultation to several communication service providers, addressing their traditional and emerging use cases. He specializes supporting the end-to-end life cycle of complex enterprise data solutions, delivering strategic cloud migration projects, integrating newly acquired systems, modernizing, and optimizing data solutions, ensuring dependable and trustworthy data quality, thereby enabling data platform users confidently support desired business needs.