Inventory and Cost Analysis
Turnover, excess stock, shortages and cost changes need evidence before action.
Explore this solutionClear data. Reliable analysis.
Know what each table and field means.
Table blocks: separate detail, summary and explanatory areas, retaining each source and business purpose. Multi-level headers: clarify header hierarchy, column groups and units so every column has a clear field name. Semantic fields: define business names, data types, units, time granularity and the meaning of enumerated values. Structure confirmation: business users review the identified structure and confirm table boundaries, primary keys and field mappings.
Understand the issue before repairing it.
Missing values: distinguish unavailable, blank and zero values, then decide how to handle them using business context. Duplicate records: distinguish repeated imports from separate business events; confirm the deduplication criteria and retention rules. Formats and units: check dates, codes, numbers and units, and define conversion rules. Field conflicts: compare a field's meaning and values across table blocks; confirm its definition before merging.
Review the difference. Confirm the change.
Repair proposals: record the cause, suggested action and possible impact; keep suggestions distinct from business facts. Difference review: compare original and repaired values, marking affected fields, records and rules. Confirmation responsibility: an authorized business owner confirms the repair decision, application scope and version. Rollback target: identify the original version to restore before confirmation, retaining the change record and rollback rationale.
Publish the data with its definitions.
Field dictionary: retain names, business meaning, types, units, time granularity and value rules. Quality notes: list resolved and outstanding issues, exclusions and responsible owners. Version publication: identify source files, repair records, approvers and the published version, preserving their lineage. Downstream use: base metrics, Dashboards and reports on a confirmed version, and review how data changes affect subsequent analysis.
Assign ownership of data and confirmation.
Inputs: original files, representative samples, field definitions, analysis goals and the permitted data scope. Outputs: standard dataset, field dictionary, quality issue list, repair differences and version notes. Customer responsibilities: confirm data authorization, field meaning, repair decisions and publication scope. Willow responsibilities: map table structures, explain quality issues and repair differences, and deliver standardized results within the agreed scope.