In today's data-driven world, the quality of data has become increasingly vital for organizations across various industries. Poor data quality can lead to erroneous insights, ineffective decision-making, and compromised business operations. To ensure the reliability and accuracy of data, organizations often follow a framework known as the "7 Pillars of Data Quality."
The 7 Pillars of Data Quality represent a comprehensive set of principles and guidelines that organizations can employ to measure, assess, and enhance the quality of their data. These pillars serve as a roadmap to guide organizations in identifying and addressing data quality issues, establishing robust data governance practices, and optimizing data management processes.
Each pillar focuses on a specific aspect of data quality and provides a framework for evaluating and improving it. These pillars typically include completeness, conformity, uniqueness, accuracy, validity, consistency, and integrity.
The following video delves into practical examples of how the 7 pillars of data quality are measured and applied in real-world scenarios.
