Data Integration & Mapping Checklist (POS, inventory, labor)

Purpose

This checklist helps teams scope, map, test, and validate integrations between POS, inventory, labor/scheduling, and adjacent systems. Use it to make sure dashboards and automations receive consistent, auditable data and to reduce the risk of bad KPIs or incorrect automation actions.

How to use this checklist

Walk through the sections during design and again during testing and cutover. Capture decisions, mapping tables, and test results. Adapt thresholds and cadence items to your business needs and systems performance.

Pre-integration scoping

• Identify systems and versions involved (POS, Inventory WMS, Scheduling/LMS, Payroll, Delivery platforms).
• Record owners and primary contacts for each system (name, role, escalation path).
• Define the authoritative source-of-truth for each domain (e.g., POS = sales; Inventory Master = ERP).
• Agree business keys/identifiers to use (e.g., SKU, item_code, order_id, employee_id). Ensure stable canonical IDs exist.
• Document time zones, timestamp formats, and daylight saving behavior for each system.
• Specify units, currencies, rounding rules, and locale formatting (e.g., weight UOM, currency decimals).
• Define acceptable latency and consistency expectations for each data type (real-time, near-real-time, hourly, daily).
• List regulatory or privacy constraints (PII, GDPR, payroll rules) and retention policy requirements.

Field mapping checklist (common fields and mapping notes)

Below are typical fields to consider. Use this as a starting template — adapt to your systems.

POS & Order data

• order_id (business key) — mapping rule: preserve source ID, ensure uniqueness
• order_line_id / line_item_id
• item_sku / item_code
• item_name / menu_name (note: use SKU as canonical, not free-text name)
• quantity, modifiers, portion_size
• unit_price, extended_price, discounts, promotions
• tax_amount, tax_rate
• payment_method, tender_type
• order_timestamp (timezone-normalized)
• location_id / store_id
• order_status (completed, voided, refunded)
• customer_id / loyalty_id (if applicable)

Inventory data

• item_sku (match to POS SKU)
• on_hand_qty, reserved_qty, allocated_qty
• uom (unit of measure) — normalize conversions
• cost_per_unit / last_cost
• lot_id / batch_number / expiry_date (if applicable)
• location_id / bin / zone
• last_received_date, last_count_date

Labor & Scheduling data

• employee_id (canonical ID linking to HR/payroll)
• shift_id, clock_in_time, clock_out_time, break_durations
• hours_worked (rounded rule), pay_rate or labor_cost_rate reference
• position / role / department_id
• approved_timesheet_status

Mapping notes and transformations

• Document any transformation rules (e.g., SKU renaming, unit conversions, tax mapping).
• Define rounding/precision rules for money and quantity fields.
• Note fields that require enrichment (e.g., mapping POS SKU to inventory item_id).
• Mark any fields that are optional vs required for downstream consumers.

Sync cadence & strategy

• Decide sync mode per domain: event-driven (near-real-time) vs batch (hourly/daily) vs hybrid.
• Typical recommendations:
  • POS orders: near-real-time or event stream for orders/refunds to avoid stale sales KPIs.
  • Inventory movements: near-real-time for receiving/adjustments; hourly for counts and on-hand reconciliations.
  • Labor punches: per-event or near-real-time for labor cost visibility; daily batch for payroll.
• Prefer delta/CDC (change-data-capture) where available. Avoid frequent full-table extracts unless necessary.
• Design idempotent operations: retries should not create duplicates. Use natural keys and upsert behavior.
• Account for ordering: ensure dependent events (e.g., order -> inventory decrement) are applied in correct sequence or reconciled later.

Reconciliation & validation tests (sample)

Define automated and manual tests to confirm mapping correctness and run them as part of pre-prod and post-go-live checks.

1. Record-level checks
   • Count of orders in source vs destination for a sample window (e.g., busiest hour).
   • Count of line items per order matches.
2. Summation checks
   • Total sales amount by store/timeframe matches within an agreed tolerance (e.g., 0.2%).
   • Inventory on-hand valuation vs system-calculated expected value — flag large variances.
3. Edge cases
   • Refunds and voids are correctly reflected and reversed in inventory and sales reports.
   • Partial refunds and modifiers behave consistently.
4. Sample reconciliation procedure
   1. Pick a 1–4 hour window during a real shift; export source and destination records for that window.
   2. Run automated scripts to match by order_id and compare key fields (quantities, prices, timestamps).
   3. Document mismatches, root cause, and corrective action.
5. Define acceptable variance thresholds and escalation rules.

Permissions, security & governance

• Create dedicated integration/service accounts rather than using personal credentials.
• Apply least privilege: only grant APIs the scopes they need (read/write boundaries).
• Use TLS/HTTPS and rotate API keys/secrets on a schedule.
• Enable audit logging and store logs for sufficient retention for troubleshooting and compliance.
• IP allowlists, client certificates, or VPN where supported for higher-risk connections.
• Periodic access reviews and emergency access (break-glass) process documented.

Error handling & rollback steps

• Detect: implement monitoring to detect integration failures, high error rates, and data drift.
• Pause: have an operational command to pause inbound feeds if serious errors occur.
• Queue & retry: failed events should be queued with exponential backoff and a dead-letter queue for manual review.
• Rollback strategy:
  • Prefer compensating transactions (reverse events) rather than destructive deletes.
  • Keep incremental backups or transactional logs to support rollback/replay.
  • Document manual reconciliation steps to correct destination data when automated rollback is insufficient.
• Notification: automatic alerts to relevant owners when thresholds are breached or rollbacks occur.
• Post-incident reconciliation and sign-off before resuming normal operations.

Monitoring, alerts & dashboards

• Track latency (time from event in source to applied in destination), error rates, and reconciliation delta magnitudes.
• Create dashboards with daily reconciliation KPIs and anomaly detection for sudden variance.
• Set alert thresholds for missing syncs, high error volumes, or reconciliation failures.

Acceptance criteria & go-live checklist

• Mapping table completed and signed off by business owners.
• Pre-prod smoke tests pass: record counts, summations, and edge case tests.
• Backfill plan and performance tested (if migrating historical data).
• Rollback plan tested in staging; notify list and escalation path validated.
• Monitoring/alerts configured and validated with test alerts.
• Cutover window scheduled with stakeholders and communication plan ready.

Post-go-live operations

• Run daily reconciliations for an agreed stabilization period (e.g., first 7–14 days).
• Log and triage all mismatches; perform root-cause and permanent fixes.
• Review access and rotate keys after successful go-live milestone.
• Schedule a retrospective and update mapping and runbooks based on findings.

Templates & next steps

Recommended follow-ups:

• Create a mapping spreadsheet (source field, destination field, transformation rule, owner, examples).
• Build automated reconciliation scripts that can be run daily and produce exception reports.
• Convert this checklist into an interactive mapping form to capture per-integration details and store them with your project artifacts.

Note: This checklist is a practical starting point. Tailor cadence, thresholds, and tests to your systems, volume, and acceptable business risk.