Data Quality

Poor-quality data can produce incorrect reports, broken workflows, and unreliable AI models. Data-quality programs define expectations, validation rules, monitoring, ownership, and remediation processes for important datasets. The concept is part of the data lifecycle that connects operational sources with reporting, analytics, automation, and machine-learning workloads.