Snowflake on Why Data Engineering Fundamentals Define AI Success or Failure

Data engineering, once dismissed as unglamorous infrastructure work, has become headline material as organizations rush to deploy AI systems. The sudden attention reflects a harsh reality: AI initiatives fail when data pipelines, governance, and lineage aren't solid. According to research cited by Snowflake, 72% of 400 surveyed technology leaders now view data engineers as integral to their business, marking a significant shift in how organizations value the discipline.

Veronika Durgin, writing for Snowflake's blog, applies a risk analysis framework to explain why AI projects struggle. The "knowns and unknowns" model divides organizational knowledge into four critical quadrants: what teams understand and rely on, what they recognize they don't know, what answers exist but remain hidden in silos, and what surprises await that nobody anticipated. Each quadrant presents distinct challenges that require different responses, from reinforcing fundamentals to fostering cross-team communication.

The most dangerous category isn't what organizations don't know, but what they think they know. Models hallucinate because nobody validated training data. Pipelines break silently, feeding stale information to production systems. Quick prototypes become business-critical dependencies without proper safeguards. These failures trace directly back to skipped fundamentals: the lineage jobs, schema tests, and safety switches that prevent small problems from cascading into system-wide outages.

Organizations face expanding questions they recognize but can't answer: measuring explainability when models make unauditable decisions, tracing lineage when models retrain themselves, governing synthetic data, and handling drift that happens in milliseconds. Meanwhile, critical answers often hide in plain sight across team boundaries. Vendors optimize systems until performance tanks while controlling diagnostic information. Upstream teams change schemas without notifying downstream dependencies. Teams solve identical problems independently because knowledge never flows between them.

Durgin argues the solution isn't perfection but resilience. Organizations must design systems assuming failure will occur, building rollbacks and retries into architecture. They need to contain blast radius so single failures don't compromise entire platforms. Most importantly, they must create cultures where engineers feel safe flagging problems they observe and where post-mortems become learning opportunities rather than blame sessions. AI has compressed years of technology maturity into months, but the fundamentals that make systems trustworthy haven't changed.