Data Quality & Governance for Load Planning Analytics
N.B. If actual correlation coefficients are unavailable, infer using rank correlations from historical series. Use triangular or PERT distributions when only three-point estimates exist. Validate NPV sign conventions and ensure discount rate aligns with real vs. nominal cash flows.
📄 Prompt Template
Establish a data governance framework to sustain high-quality consolidation and load planning analytics at [CompanyName]. Profile data from [HistoricalShipmentsFile], [CarrierTenderFile], and [MasterDataRepo] for completeness, accuracy, timeliness, and consistency (e.g., NMFC class, stackability, dims/weight). Define ownership across [TransportationOrg] and [ITDataTeam].
Output requirements: (1) A data dictionary (Markdown table): Field, Source, Definition, Quality Rules, Owner, Criticality. (2) A scorecard spec (monthly): metrics, thresholds, and remediation SLAs. (3) A master-data change workflow (numbered steps) and a golden-record policy for SKU dimensions.
Include validation rules that directly impact consolidation (e.g., cube mismatch tolerances, accessorial codes standardization) and an exception log template.