Data Management Best Practices for 2D Barcode Migration

1. Establish Clear Data Ownership Before Migration

Data issues rarely originate from technology. They originate from unclear ownership.

In many organizations, different teams define and manage the same data differently. ERP systems may define products one way, while WMS platforms apply different rules. Manufacturing may calculate expiration based on production date, while quality teams use inspection date.

Without clear ownership, 2D barcode migration amplifies these conflicts.

Best practices include:

• Assign a single authoritative system for each data element
• Clearly document ownership and update responsibility
• Define escalation paths for conflicts before deployment

Lowry often sees migration delays avoided simply by resolving ownership questions early.

2. Standardize Data Formats and Rules

2D barcodes rely on consistent formatting to be usable across scanners, software platforms, and external partners.

Common formatting issues include:

• Inconsistent date structures (MM/DD vs YYYYMMDD)
• Variable lot numbering logic across plants or suppliers
• Custom data fields that break interoperability

Best practices:

• Align formats strictly with GS1 barcode standards
• Avoid custom or proprietary data structures
• Validate formatting consistency across all systems before rollout

Standardization ensures data remains readable and actionable throughout the supply chain.

3. Validate Data at the Point of Capture

The easiest place to prevent data errors is at the moment of capture.

Once incorrect data enters downstream systems, it becomes expensive to correct. Common scan-level risks include partial reads, misinterpreted fields, and manual overrides entered under time pressure.

Best practices include:

• Enabling format validation on scanners
• Requiring mandatory fields for critical workflows
• Limiting free-text inputs on mobile devices
• Providing immediate feedback when data does not meet standards

Lowry’s enterprise mobility deployments consistently show that scan-level validation reduces error rates and rework.

4. Map How 2D Barcode Data Moves Through Systems

2D barcode data rarely stays in one system. It often flows through ERP, WMS, inventory systems, asset tracking platforms, and analytics tools.

Without a clear data map, organizations risk duplicating logic or creating conflicting interpretations.

Best practices:

• Document end-to-end data flows
• Define where transformations occur
• Establish validation checkpoints
• Avoid embedding business logic in multiple systems

Lowry designs integrations so that data is interpreted consistently, regardless of where it is consumed.

The requirement for structured data governance emerges through the sequential development path of organizations from their first ownership stage until they reach their final validation stage. The expanded barcode data has increased its velocity, together with its ability to interact with different systems, which results in operational edge detection of system errors. The absence of established guidelines leads to different interpretations of the same data element by manufacturing and warehousing, quality, and compliance teams.

Strong governance ensures that data definitions remain consistent over time. The lot number, which gets scanned during the receiving process, needs to maintain its original meaning throughout inventory reporting and quality investigations, and recall execution processes. The trustworthiness of barcode data declines when definitions change because organizations must return to manual verification processes.

5. Plan for Data Changes Over Time

Product data is not static. Regulations change. Packaging is updated. Formulas are adjusted. New suppliers are introduced.

A common mistake is overwriting old data when changes occur.

Best practices include:

• Versioning data rather than replacing it
• Ensuring systems can interpret older barcodes still in circulation
• Maintaining historical context for compliance and traceability

This approach prevents disruptions when products with older labels remain in the field.

6. Control Access to Richer Barcode Data

As barcodes carry more data, access control becomes more important.

Not all data encoded in or associated with a barcode should be visible to every user or system.

Best practices:

• Implement role-based access controls
• Separate internal operational data from consumer-facing information
• Secure integrations between systems using authenticated APIs

Lowry emphasizes security by design, especially in healthcare, regulated manufacturing, and government environments.

7. Prepare for Mixed Barcode Environments

Most organizations will operate mixed environments for years, including:

• Traditional 1D barcodes
• GS1-compliant 2D barcodes
• RFID tags in select use cases

Attempting to run parallel data models for each technology increases complexity and maintenance costs.

Best practices:

• Normalize identifiers across all capture technologies
• Maintain a unified data model
• Let technology selection be driven by use case and ROI

Lowry’s technology-agnostic approach ensures that barcode and RFID data work together rather than compete.

8. Use Data Purposefully, Not Excessively

Capturing more data does not automatically improve performance. Data without a defined purpose creates noise.

Best practices include:

• Defining KPIs before migration begins
• Tying each data element to a measurable outcome, such as:
  • Inventory accuracy
  • Recall response time
  • Labor efficiency
  • Compliance performance

This focus ensures that data collection supports operational goals rather than adding overhead.