Scenario: A financial institution is planning to implement a data quality management program. As a data engineer, how would you establish data quality metrics tailored to the organization's needs?

Establishing data quality metrics tailored to a financial institution's needs involves considering factors such as Completeness (all necessary data present), Validity (data conforms to defined rules and standards), Accuracy (data reflecting true values), and Timeliness (data being up-to-date). Additionally, ensuring the relevance of data to business objectives, precision in measurement, reliability in data sources, and scalability for future growth are essential for effective data quality management in financial institutions. These metrics enable informed decision-making, regulatory compliance, and risk management.