AI in Data Management and Infrastructure
This dimension assesses the organization’s ability to provide the right data foundation and infrastructure for AI implementations in the SDLC.
Sample assessment questions for each level:
- Level -1 (Resistant): “Does the organization actively restrict access to data needed for AI initiatives in the SDLC?”
- Level 0 (Ad-hoc): “Is data for AI initiatives collected manually or inconsistently without standardization?”
- Level 1 (Exploratory): “Has the team identified data requirements for AI implementation in development workflows?”
- Level 2 (Structured): “Are data pipelines established for basic AI use cases in the SDLC?”
- Level 3 (Established): “Is there a unified data strategy that aligns with AI needs across development processes?”
- Level 4 (Integrated): “Are data governance and quality measures embedded throughout the AI-enabled SDLC?”
- Level 5 (Transformative): “Does the organization have a self-optimizing data infrastructure that anticipates AI needs?”
Key metrics to track:
- Data accessibility: Time required to provision high-quality data for AI initiatives
- Data quality score: Measured completeness, accuracy, and consistency of development data
- Infrastructure scalability: Speed at which AI computing resources can be scaled for development needs
- Data pipeline automation: Percentage of data pipelines that are fully automated
- Integration breadth: Number of development systems with integrated data accessible to AI tools
- MLOps maturity: Assessment of model lifecycle management capabilities
- Data governance compliance: Percentage of AI initiatives meeting data governance requirements
