Why most enterprise data isn't actually big data

Most data leaders chase distributed infrastructure they don't need, paying what Mahesh Mishra calls a complexity tax. As VP of Artificial Intelligence at Cloudera, he argues most enterprise data isn't big data at all, and platform readiness, more than model quality, is the real barrier to production AI.

Mahesh breaks down knowledge engineering as the missing layer beyond prompt and context engineering, explains why traditional data catalogs are becoming obsolete, and maps out how Iceberg and Lance are reshaping the lake house for agentic workloads.

Topics discussed:

- Distinguishing knowledge engineering from prompt and context engineering

- Building decision flow and decision tracing languages for agents

- Why data catalogs must evolve into semantic knowledge graphs

- Combining Iceberg and Lance for structured and unstructured data

- Three reasons AI projects stall before reaching production

- Treating AI agents as identity-bound team members for governance

- Asking whether your data actually qualifies as big data

- Forecasting agent-driven decision making and falling engineering costs