AI workflows are only as useful as the business context they can safely use. That context usually lives across operational records, product data, customer history, analytics models, workflow runs, generated artifacts, and human decisions.
Shikha Labs approaches data engineering as part of the operating layer. Warehouses, pipelines, metrics, BI views, CRM records, support tickets, activity, and workflow history should reinforce one another rather than drift into disconnected reporting and execution systems.
For enterprises, the practical work is often architectural: define the records agents can act on, the data they can read, the metrics humans trust, the knowledge artifacts workflows can publish, and the boundaries that keep automation accountable.