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Data Retention

Industrial IQ documentation

Data Retention documentation for AI2COE Industrial IQ buyers, operators, data owners, executives, and governance teams.

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Executive decision context

Data Retention

Uploaded source files are processed for diagnostic output and purged according to the disclosed retention boundary.

This guide explains what problem is solved, who cares, what data is needed, what output is delivered, what ROI lever is affected, what report can be shared internally, and what recurring value is created.

UploadCSV export or sample dataset
Source FitRequired fields and data quality
AI MatchColumn aliases and confidence
NormalizeCanonical field preparation
DiagnoseEngine logic and evidence
ReportScore, actions, assumptions
RepeatScore history and renewal value
Required data by engine

Use this table before upload.

EngineRequired Data FileRequired FieldsOptional FieldsOutput GeneratedMinimum Viable UploadBest Upload
PartsCleanse AI Material or item master CSV Description Material Id, Manufacturer, Mpn, Supplier, Uom, Quantity Catalog health score, evidence, report, actions, score history Description Required fields plus site, value, date, owner, asset, supplier, and criticality context where available.
InventoryMind AI Inventory balance CSV Material Id, Quantity Description, Unit Cost, Stock Value, Last Movement Date, Movement Qty, Demand Inventory health score, evidence, report, actions, score history Material Id, Quantity Required fields plus site, value, date, owner, asset, supplier, and criticality context where available.
ProcureMind AI Purchase order CSV Po Number, Description Material Id, Supplier, Unit Price, Quantity, Order Date, Order Type Procurement leakage score, evidence, report, actions, score history Po Number, Description Required fields plus site, value, date, owner, asset, supplier, and criticality context where available.
FinanceMind AI Inventory value file Material Id, Stock Value Description, Quantity, Unit Cost, Duplicate Family, Carrying Cost Rate, Emergency Premium Working capital score, evidence, report, actions, score history Material Id, Stock Value Required fields plus site, value, date, owner, asset, supplier, and criticality context where available.
Accepted CSV format

Prepare clean exports before upload.

Use UTF-8 CSV with one header row, one operational record per row, stable identifiers, ISO-style dates where available, numeric values without currency symbols, and separate columns for site, supplier, asset, quantity, value, and owner context.

Common errors: merged header rows, hidden subtotal rows, mixed currencies in one value column, duplicate column names, free-text dates, missing material or asset identifiers, and supplier names embedded inside descriptions.

If fields are missing: Industrial IQ still profiles source fit, but unmapped required fields reduce the AI Match Score, Mapping Readiness Score, diagnostic confidence, and report completeness until the user normalizes the data.

Sample row examples

Minimum viable row patterns.

  • Catalog Intelligence: Description: 6205-2RS bearing sealed SKF
  • Inventory Risk Intelligence: Material Id: MAT-100245 | Quantity: 42
  • Procurement Leakage Intelligence: Po Number: PO_NUMBER | Description: 6205-2RS bearing sealed SKF
  • Working Capital Intelligence: Material Id: MAT-100245 | Stock Value: 1850.00
Related engines

Which diagnostic should the buyer run next?

Related engine

PartsCleanse AI

MRO catalog deduplication, field quality, UOM consistency, and duplicate capital exposure.

Open documentation
Related engine

InventoryMind AI

Dead stock, slow-moving stock, excess, stockout risk, and duplicated stock exposure.

Open documentation
Related engine

ProcureMind AI

Emergency procurement, stocked-but-purchased events, repeated buys, supplier alias risk, and price variance.

Open documentation
Related engine

FinanceMind AI

Duplicate capital exposure, carrying cost, emergency premium, and recoverable value scenarios.

Open documentation
Report anatomy

What the buyer can share internally.

Industrial IQ reports include scope, source-fit score, AI match score, mapping readiness, diagnostic confidence, score components, evidence records, assumptions, limitations, review requirements, action items, and recurring score history.

Data RetentionValue lever
Uploaded dataCustomer-specific diagnostic required
FinanceMind AIRelated diagnostic engine
EstimateAssumption-labeled until reviewed
Benchmark assumptionUploaded evidenceConfidence tierHuman reviewReportable value
Force Team validation

Data Retention buyer enablement checklist.

Problem solvedTurns an operational data question into source-backed diagnostic evidence.
Who caresCFO, COO, CIO, procurement, maintenance, reliability, ERP, governance, and board stakeholders where relevant.
Data neededCSV exports with stable IDs, descriptions, quantities, values, dates, site, asset, supplier, and owner context where available.
Output deliveredScores, evidence table, confidence tiers, executive report, action tracker items, score history, and governance status.
Value quantifiedWorking capital, carrying cost, emergency premium, dead stock, stockout risk, readiness gaps, or governance risk depending on diagnostic intent.
Decision supportedWhether to act, review, normalize data, escalate findings, fund remediation, or repeat the diagnostic cadence.
Report shared internallyExecutive, CFO, procurement, inventory, readiness, governance, or renewal value report.
Recurring valueBaseline score -> re-upload -> score movement -> action closure -> benchmark comparison -> renewal value report.
Assumptions and limitationsBenchmarks and estimates remain planning context until customer-specific uploaded data is analyzed and reviewed.
Trust controlsNo ERP write-back, evidence traceability, confidence tiers, human review, false-positive control, audit trail, and data-retention boundaries.
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