Data Artificial Intelligence Governance Essentials (DAIG)

Data Management involves organizing, storing, and processing information systematically to ensure data quality, security, and accessibility, fostering informed decision-making and streamlined operations across diverse domains.

Data Governance is a framework ensuring data quality, security, and compliance. By establishing policies, procedures, and responsibilities, fostering effective data management, mitigating risks, Data Governance enhances trust in organizational data, crucial for informed decision-making.

Some of the key components of Data Governance includes having clear policies, defined roles, data quality standards, and compliance measures. In this module, learners will gain advanced knowledge on how to ensure systematic data management, fostering trust, integrity, and maximizing the value of organizational data.

A dedicated organization structure with defined roles will enhance the effectiveness of Data Governance. The collaboration between Data stewards, compliance officers and data owners establishes accountability, transparency, and trust in data management practices.

From metadata management to access controls, these tools facilitate compliance, quality assurance, and efficient data management, ensuring organizations harness the full potential of their data while maintaining security and regulatory standards.

Artificial Intelligence and Generative Artificial Intelligence (AI) Governance is a crucial field that addresses the ethical, legal, and societal challenges posed by the rapid advancement of AI technologies, including generative AI. This domain focuses on establishing frameworks and guidelines to ensure responsible and accountable development, deployment, and use of AI systems.