AI Data Management SOP

This detailed SOP provides data managers, compliance officers, and IT professionals with a structured framework for managing AI data within an organisation.
It includes:
– Step-by-Step Process Flow: Outlines essential subprocesses such as Data Governance, Data Collection, Data Cleaning, and Data Distribution, with clear actions, decision points, and feedback mechanisms to ensure data quality, security, and readiness for AI applications.  
– Risk Management: Identifies key risks such as inadequate data governance, poor data quality, and security breaches, with mitigation strategies including robust governance policies, regular data validation, and strong access control measures to minimise risks and optimise data management outcomes.  
– Compliance and Regulatory Requirements: Ensures adherence to relevant regulations, including GDPR and data protection laws, by integrating compliance checks during data collection, processing, and distribution to safeguard legal and regulatory adherence.  
– Key Performance Indicators (KPIs) and Controls: Defines KPIs such as data governance compliance, data quality scores, and security incident rates, with controls like audits, validation protocols, and secure access management to ensure continuous improvement and compliance.  
– RACI Framework: Clearly defines roles and responsibilities for each task in the AI data management process, ensuring that data governance managers, data stewards, compliance officers, and IT support are accountable and engaged at every stage.  
– Systems Requirements: Details the necessary systems, including a Data Governance Framework, Data Collection Platforms, Data Cleaning Tools, and Data Storage Solutions, to support the AI data management process and ensure secure, efficient, and effective management.  
– Appendices: Provides practical resources such as governance policy templates, data collection checklists, and real-life case studies to guide users through each stage of the AI data management process effectively.