This whitepaper argues that most AI programs fail not because models are weak, but because enterprise data is fragmented, inconsistent, incomplete, and poorly governed, which also increases the risk of hallucinations, bias, and non-compliance as privacy rules tighten and “shadow AI” spreads. It proposes a pragmatic, five-step data quality assessment framework: map current data systems and flows, use AI-driven analysis to surface data gaps and inconsistencies, align cross-functional teams on shared definitions and processes, establish governance and accountability with auditable controls for privacy, lineage, and access, and then continuously measure with monitoring and KPIs. It also outlines a phased assessment approach (discover, review, gap analysis, recommend, closure) that produces sco

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