Data Quality Best Practices for ERP Catalog Management

Data quality in ERP catalog management is not a one-time cleanup project. It is an ongoing operational discipline that spans how data is created, how it is maintained across contributors and how it is validated before each ERP interaction. Organisations that treat data quality as a project rather than a process find themselves repeating the same cleanup work every six to twelve months.

The five dimensions of catalog data quality

Data quality for ERP catalogs can be measured across five dimensions. Each dimension has distinct failure modes and requires specific controls.

Why catalogs degrade over time

Catalog data does not degrade because of negligence. It degrades because the systems and processes that maintain it evolve at different speeds. A supplier changes their reference codes; the ERP is upgraded with new mandatory fields; a sales representative starts using a new Excel template with slightly different column names. Each change is small; the accumulated effect over twelve months is significant.

• Supplier reference changes not propagated to the catalog
• New ERP required fields not added to existing contributor templates
• Price updates applied to some rows but not all when a supplier reprices
• Unit of measure values not updated when ERP reference table is revised
• Category codes from a superseded classification still present in the file

Building a data quality governance process

A data quality governance process for SME catalogs does not need to be complex. The minimum viable framework has three components:

1. 1A format specification document: the single source of truth for column names, data types, allowed values and required fields. Updated whenever the ERP template changes. Distributed to all contributors.
2. 2A pre-submission validation gate: every catalog file is validated against the specification before it reaches the ERP. Errors are returned to the contributor who owns that section of the catalog.
3. 3A periodic data review: monthly or quarterly review of catalog data against current ERP reference tables — supplier codes, category codes, unit codes — to catch drift before it accumulates.

Metrics worth tracking

What you measure is what you improve. The following metrics give visibility into catalog data quality over time:

• Row error rate: percentage of rows containing at least one error, trended over time
• First-pass acceptance rate: percentage of files accepted by the ERP on first submission
• Error type distribution: which error types account for the most rows — drives targeted fixes
• Correction cycle time: average time between first submission and clean import
• Contributor error rate: which contributors introduce the most errors — drives targeted training

The role of automation

Manual data quality checks are feasible for catalogs under 500 rows with a single contributor. At SME scale — 2,000 to 50,000 rows, multiple contributors, regular updates — manual checks become the bottleneck. Automated validation tools run comprehensive checks in seconds, produce structured reports actionable by non-technical users, and create the audit trail that manual processes cannot.