AI Data Blog and Insights

Data can look clean and still be unsuitable for AI. Part 5 explores quality dimensions, risk-based thresholds, monitoring, lineage, ownership, and remediation.

ETL or ELT: which approach is better for modern AI systems? Explore where data transformation should happen, the strengths and risks of each pattern, and why hybrid architectures often provide the right balance between governance, flexibility, and scale.

What makes data truly ready for AI? Part 2 of the Info2K Data Engineering for AI series explains why clean data is not enough—and explores the quality, context, governance, security, accessibility, and continuous monitoring required to build reliable AI systems.