Enterprise data systems can fail silently even when all technical metrics appear healthy, according to Gopichand Mannava, Chief Data & AI Architect for the State of Connecticut. This "silent failure" occurs when changes to data definitions or categories go unnoticed by downstream users, leading to incorrect interpretations and flawed decision-making. Mannava emphasizes that data reliability extends beyond system uptime to include completeness, timeliness, consistency, and understandability, advocating for treating data interfaces with the same rigor as software interfaces, including clear contracts for meaning, ownership, and change communication. AI
IMPACT Highlights the critical need for robust data governance and provenance, essential for trustworthy AI systems.
RANK_REASON Opinion piece by an industry expert on a data governance issue.
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
