10. Data Lifecycle Management

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AI systems are data-intensive and can amplify the consequences of poor data management—using outdated data degrades performance, including personal data without proper controls breaches privacy laws, and lacking lineage makes it impossible to trace problems to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.

The Challenge of Data Silos

One of the most common barriers to AI readiness is data isolated in silos—where data is fragmented across different systems, departments, or legacy databases. Data silos create several problems for AI:

• Inaccessibility — AI systems cannot access the data they need for training or inference
• Inconsistency — The same data may be stored differently in multiple places, causing quality issues
• Security gaps — Duplicated data creates multiple attack surfaces and compliance challenges
• Inefficiency — Teams cannot share insights or reuse data assets across the organisation

Breaking down data silos requires both technical solutions (unified data platforms, data lakes, integration tools) and governance frameworks (data classification, access controls, ownership policies). This dimension assesses whether your organisation has implemented both aspects.

Maturity Levels

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