• Saransh Arora
• Sunil Raj Thota

Since the early days of computing, data quality issues have been prevalent, leading to the development of various domain-specific techniques to assess and improve data quality, particularly in relational databases and data warehouses. However, with the emergence of big data analytics and the resurgence of machine learning, there is a need to reevaluate the suitability of traditional relational database-centric approaches to address data quality in these new contexts. In this paper, explore the nature of data quality issues in the realm of big data and machine learning. This paper discusses different facets of data quality and proposes a data governance-driven framework for managing the data quality lifecycle in this evolving landscape. Additionally, we outline an approach to implementing this framework effectively. We also present a summary of the available tools for managing data quality and explore potential future developments in this area. Our focus is on automated data quality assessment and enhancement, particularly in software-as-a Service (SaaS)-based data applications, where ensuring data quality is crucial for reliable insights and decision-making.

"Automated Data Quality Assessment and Enhancement for SaaS based data applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.i207-i218, June-2024, Available :http://www.jetir.org/papers/JETIR2406822.pdf

"Automated Data Quality Assessment and Enhancement for SaaS based data applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppi207-i218, June-2024, Available at : http://www.jetir.org/papers/JETIR2406822.pdf