A data-readiness guide that argues AI outcomes depend on disciplined upstream data practices. It lays out a six-step path to “AI-ready” data, starting with clarifying outcomes and aligning stakeholders, then improving data quality, consistency, and governance so models and automation can be trusted. It highlights the need for repeatable pipelines (not ad hoc spreadsheets), clear lineage/audit trails, and standardized definitions so teams can reuse data confidently across analytics and AI initiatives. The focus is pragmatic: reduce friction in data prep and make it easier to operationalize insights safely at scale.

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