Data Lifecycle Management addresses how data is handled from creation through to deletion, specifically in the context of AI systems. This encompasses data classification (identifying sensitive, personal, or confidential data), retention schedules (how long data is kept), access controls (who can use data for AI training or inference), data lineage (tracking where data came from and how it’s transformed), quality assurance processes, and compliant disposal. For AI, this also includes managing training datasets, test datasets, and the data generated by AI systems themselves.
AI systems are data-intensive and can amplify the consequences of poor data management—using outdated data degrades performance, including personal data without proper controls breaches privacy laws, and lacking lineage makes it impossible to trace problems to their source. This dimension assesses how your organisation classifies, manages, and controls data throughout its lifecycle.
Why It Matters
Poor data management leads to compliance breaches, biased models, and operational inefficiencies.
Maturity Levels
| Basic | Standard | Advanced | Leading |
|---|---|---|---|
| Unmanaged data; no classification or retention policies applied to AI data. | Data classification and retention schedules in place. | Controlled data pipelines with lineage tracking and access controls. | Fully automated data lifecycle management, with continuous compliance and quality assurance. |
📥 Related Resources & Templates
Downloadable templates, examples, and frameworks to help you implement this dimension.
Data Classification Policy for AI
Data classification policy extended to cover generative AI and LLM use cases, including handling guidelines and visual aids.
Data Retention Schedule
Template for defining data retention policies for AI training data, model inputs/outputs, and related artifacts.
Data Lineage Diagram
Visual template for documenting data lineage in AI systems, tracking data flow from source to model to output.
