The industry joke — "as much artificial as intelligence" — points to a serious fact: the upper bound of AI application quality is largely determined by data quality.
Three Common Data Problems
Scattered: data sits in ERP, MES, CRM and spreadsheets with inconsistent definitions and no connectivity. Dirty: missing, duplicated and erroneous records abound — feeding them to models yields untrustworthy output. Missing: scenario-specific datasets (defect samples, domain Q&A pairs) usually need dedicated construction.
Data Engineering Should Go First
In our project experience, efforts that sort out data during POC proceed noticeably smoother afterwards: aggregate and connect sources, clean and standardize, classify and manage, then build and iterate scenario datasets. Data engineering is slow work — and the best first investment.
The Long-Term Dividend of Data Assets
Well-governed data serves more than the current AI project: business analytics, compliance auditing and cross-team collaboration all benefit. For most enterprises, a solid data foundation is the highest-compound investment in intelligent transformation.