Paper Contents
Abstract
As big data systems play a bigger role in decision-making across industries, it is critical to make sure the data they analyze is reliable and trustworthy. Data integrity and transparency in data-driven analytics are greatly aided by data provenance, which is the documentation of the sources, changes, and ownership of data. This study examines the difficulties and approaches involved in maintaining data integrity and provenance in the setting of large data systems. It examines different provenance tracking strategies, such as audit trails, blockchain technology, and metadata management, emphasizing how well they guarantee data accuracy, validity, and ancestry. The study also highlights the significance of reliable data for producing actionable insights by discussing the consequences of data integrity for analytics. Data provenance offers insight into the creation, alteration, and aggregation of data, enabling the detection of irregularities or discrepancies and the verification of data accuracy. It becomes difficult to trace this ancestry in a distributed, complicated big data environment where data comes from several sources and is constantly changing. Effective provenance tracking methods are examined in this study, covering data lineage tools, metadata management, and cutting-edge technologies like blockchain and decentralized ledgers that improve data security and transparency. A wide range of best practices are suggested in this article for businesses to use, such as integrating automated provenance tracking technologies, using secure data validation methods, and creating rules to guarantee accountable and transparent data management.
Copyright
Copyright © 2025 Dr. A. Antony Prakash. This is an open access article distributed under the Creative Commons Attribution License.
