Data Governance and Quality Management in Data Engineering |
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| © 2023 by IJCTT Journal | ||
| Volume-71 Issue-11 |
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| Year of Publication : 2023 | ||
| Authors : Alekhya Achanta, Roja Boina | ||
| DOI : 10.14445/22312803/IJCTT-V71I11P106 | ||
How to Cite?
Alekhya Achanta, Roja Boina, "Data Governance and Quality Management in Data Engineering," International Journal of Computer Trends and Technology, vol. 71, no. 11, pp. 40-45, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I11P106
Abstract
Data has become one of the most valuable assets for organizations today. With the exponential growth in data, effectively governing and managing its quality is critical for gaining business insights and maintaining regulatory compliance. This paper examines the importance of data governance and quality management in data engineering. It outlines the fundamental principles, processes, and best practices for implementing robust data governance frameworks and quality management programs. The roles of key stakeholders, such as data owners, stewards, and engineers, are discussed. It also explores the challenges, such as inadequate data quality culture and lack of executive support. The focus is on new technologies, such as machine learning and automation, which can potentially improve data governance and quality. The paper concludes by emphasizing the need for a holistic strategy, strong leadership, and a collaborative culture for successful data governance and quality management outcomes.
Keywords
Data governance frameworks, Data profiling and monitoring, Data validation and standards, Data quality assurance, Metadata management.
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