In analytics, what is the goal of data governance?

The goal of data governance is fundamentally rooted in ensuring the quality and security of data. This involves establishing policies, procedures, and standards for managing data throughout its lifecycle. Proper data governance helps organizations maintain accurate, consistent, and secure data, which in turn supports effective decision-making and compliance with regulations.

Quality assurance in data governance includes processes such as data validation, data cleaning, and establishing data quality metrics, which all contribute to the reliability of analytics. Security is equally important as it protects sensitive information from unauthorized access and breaches, ensuring that data is not only trustworthy but also safeguarded.

In contrast, the other options do not align with the primary objectives of data governance. Limiting data usage among stakeholders does not promote collaboration and informed decision-making, which are crucial in an analytics context. Promoting sales through misleading data analysis raises ethical concerns and undermines the integrity of data. Similarly, enhancing creative design in marketing, while important in its own right, is not the core focus of data governance and does not address the fundamental concerns of data quality and security.