Flashcards for:
Data Governance vs. Data Quality Discussion

Data governance should come first as it establishes a framework, identifies roles and responsibilities, and helps in determining key stakeholders for data governance initiatives. This setup allows for the prioritization of definitions and data quality rules.

Establishing a data governance framework involves setting up structures, roles, and responsibilities crucial for managing and overseeing data quality and governance initiatives.

Data quality activities provide practical insights and data handling practices that can be integrated into a structured data governance framework to enhance strategic data management.

Key stakeholders are crucial in defining data governance initiatives, determining data quality rules, and ensuring the alignment of data strategies with organizational objectives.

When integrated into a data governance framework, data quality efforts should focus on critical data needs and ensure alignment with organizational priorities and governance standards.

Common data quality activities include data cleansing, data fixing, and creating data quality reports often misnamed as exception reports.

Recognizing pre-existing data quality initiatives is important as it allows organizations to leverage current practices and incorporate them into a comprehensive data governance strategy.

Understanding the sequence is significant as it helps organizations effectively prioritize and implement both data governance and quality practices, ensuring alignment with business goals.

Which comes first, data governance or data quality?

Organizations can ensure effective prioritization by focusing data quality efforts on critical data sets once integrated into the overarching data governance framework.

'Exception reports' are a type of data quality report that identifies deviations from expected data standards, often used in early data quality initiatives.

Early data quality initiatives often increase organizational awareness about the necessity of a formalized data governance framework for better data management and quality assurance.

Organizations can turn tactical activities into strategic initiatives by integrating existing data quality efforts into a broader data governance framework and focusing these efforts on critical data with the right priorities.

Many organizations have pre-existing data quality activities such as data cleansing and basic data quality reporting that predate their awareness of the need for a formal data governance structure.

Data governance can enhance data quality efforts by providing structured guidelines, clear roles, and a strategic approach to maintaining high data quality standards.