Security, compliance, and governance
Data Governance Strategies
Data governance for AI manages the data lifecycle from collection and storage to use and security.
Strategies
- Data quality and integrity: quality standards, validation and cleansing, and lineage and provenance (where data came from and how it changed).
- Data protection and privacy: privacy policies, access controls, encryption, and breach response.
- Data lifecycle management: classify and catalog data, set retention and disposal policies, back up and recover.
- Responsible AI: frameworks for bias, fairness, transparency, and accountability, plus monitoring and team training.
- Governance structures and roles: a data governance council, and defined data stewards, owners, and custodians.
- Data sharing and collaboration: sharing agreements, and data virtualization or federation that keeps ownership intact.
Concepts to know
| Concept | Meaning |
|---|---|
| Data lifecycle | Collection, processing, storage, consumption, then disposal or archiving |
| Data logging | Recording inputs, outputs, performance metrics, and system events |
| Data residency | Where data is physically stored and processed: privacy regulations, sovereignty, proximity to compute |
| Data monitoring | Watching quality, anomalies, and data drift (input distribution changing over time) |
| Data analysis | Statistics, visualization, and exploratory data analysis (EDA) |
| Data retention | How long to keep data: regulations, retraining needs, storage cost |
Data transcription, batch processing, and OLTP are processing techniques, not governance strategies.