Mastering the Art of Auditing Data Risk - Online Classroom

This training course is designed to equip participants with in-depth knowledge and practical skills in data management, ethics, and governance, with a particular focus on data used for artificial intelligence (AI) and analytics.
Throughout this course, participants will explore various aspects of data accountability, data quality, data security, data privacy, and more. The program combines theoretical understanding with real-world case studies and practical applications to ensure that participants are well-prepared to manage data effectively and responsibly in today's data-driven world.
At the end of the training course, the participants will gain knowledge in the following areas:

Data Accountability - Roles and Responsibilities:

• Understand the typical data roles within an organisation.
• Differentiate the responsibilities of roles such as Chief Data Officer, Chief Data and Analytics Officer, Data Steward, with real-world examples like the BEAR regime in the banking sector.

Data Management for AI - Ethics, Bias, and Drift:

• Identify data management risks associated with AI and analytics.
• Apply data ethics principles to mitigate bias and ensure fairness.
• Implement strategies for monitoring AI model drift to maintain reliable outputs.

Data Quality - Management and Monitoring:

• Determine which data should be monitored for quality.
• Implement effective data quality monitoring processes.
• Manage data quality issues when identified.

Data Remediation and Issue Management:

• Develop data remediation programs.
• Differentiate between preventative and detective data controls. Identify and manage issues related to data management.

Data Classification and Security:

• Establish a robust data classification standard.
• Implement corresponding data security controls for different data classification levels.

Data Privacy Management:

• Analyse case studies of data privacy management issues, including high-profile data breaches.

Data Retention, De-Identification, and Destruction:

• Ensure compliance with clear data retention rules.
• Understand the processes for data destruction at the end of its lifecycle.
• Implement de-identification controls to protect sensitive information.

Data Lineage Management, Including Tools:

• Document and understand data as it moves from source to consumption.
• Familiarise with industry-standard data lineage tools to track data flow.

Data Change Management:

• Consider implications when projects or programs necessitate data changes.
• Minimise the impact on existing controls and processes during data changes.